WorldWideScience

Sample records for artificial wetland modelling

  1. Artificial wetlands - yes or no?

    Czech Academy of Sciences Publication Activity Database

    Horák, Václav; Lusk, Stanislav; Halačka, Karel; Lusková, Věra

    2004-01-01

    Roč. 4, č. 2 (2004), s. 119-127 ISSN 1642-3593. [International Symposium on the Ecology of Fluvial Fishes /9./. Lodz, 23.06.2003-26.06.2003] R&D Projects: GA AV ČR IBS6093007; GA AV ČR KSK6005114 Institutional research plan: CEZ:AV0Z6093917 Keywords : floodplain * artificial wetlands * fish communities Subject RIV: EH - Ecology, Behaviour

  2. Value Assessment of Artificial Wetland Derived from Mining Subsided Lake: A Case Study of Jiuli Lake Wetland in Xuzhou

    OpenAIRE

    Laijian Wang; Lachun Wang; Pengcheng Yin; Haiyang Cui; Longwu Liang; Zhenbo Wang

    2017-01-01

    Mining subsided lakes are major obstacles for ecological restoration and resource reuse in mining regions. Transforming mining subsided lakes into artificial wetlands is an ecological restoration approach that has been attempted in China in recent years, but a value assessment of the approach still needs systematic research. This paper considers Jiuli Lake wetland, an artificial wetland derived from restoration of a mining subsided lake in plain area, as a case study. A value assessment model...

  3. Artificial wetland for wastewater treatment

    International Nuclear Information System (INIS)

    Arias I, Carlos A; Brix, Hans

    2003-01-01

    The development of constructed wetland technology for wastewater treatment has gone a long way and from an experimental and unknown empirical method, which was capable of handling wastewater a sound technology was developed. Thanks to research, and the work of many public and private companies that have gather valuable operation information, constructed wetland technology has evolved to be a relievable, versatile and effective way to treat wastewater, run off, handle sludge and even improve environmental quality and provide recreation sites, while maintaining low operation and maintenance costs, and at the same time, producing water of quality that can meet stringent regulations, while being and environmental friendly solution to treat waste-waters. Constructed wetlands can be established in many different ways and its characteristics can differ greatly, according to the user needs, the geographic site and even the climatic conditions of the area. The following article deals with the general characteristics of the technology and the physical and chemical phenomena that govern the pollution reduction with in the different available systems

  4. Value Assessment of Artificial Wetland Derived from Mining Subsided Lake: A Case Study of Jiuli Lake Wetland in Xuzhou

    Directory of Open Access Journals (Sweden)

    Laijian Wang

    2017-10-01

    Full Text Available Mining subsided lakes are major obstacles for ecological restoration and resource reuse in mining regions. Transforming mining subsided lakes into artificial wetlands is an ecological restoration approach that has been attempted in China in recent years, but a value assessment of the approach still needs systematic research. This paper considers Jiuli Lake wetland, an artificial wetland derived from restoration of a mining subsided lake in plain area, as a case study. A value assessment model for the artificial wetland was established based on cost–benefit analysis by means of field monitoring, social surveys, GIS geostatistics, raster calculation methods, etc. Empirical analysis and calculations were performed on the case study region. The following conclusions were drawn: (1 after ecological restoration, ecosystem services of Jiuli Lake wetland which has become a national level wetland park yield positive values; (2 the improved environment of the Jiuli Lake wetland has a spillover effect on the price of surrounding land, resulting in land price appreciation; (3 using GIS geostatistics and raster calculation methods, the impact range, strength, and value of the spillover effect can be explicitly measured; (4 through the establishment of a value assessment model of the artificial wetland, incomes of the ecological restoration was found to be sufficient to cover the implementation costs, which provides a research foundation for economic feasibility of ecological restoration of mining subsided lakes.

  5. The use of artificial wetlands to treat greenhouse effluents

    OpenAIRE

    Lévesque, Vicky; Dorais, Martine; Gravel, Valérie; Ménard, Claudine; Antoun, Hani; Rochette, Philippe; Roy, Stéphane

    2011-01-01

    Untreated greenhouse effluents or leak solution constitute a major environmental burden because their nitrate and phosphate concentrations may induce eutrophication. Artificial wetlands may offer a low cost alternative treatment of greenhouse effluents and consequently improve the sustainability of greenhouse growing systems. The objectives of this study were to 1) characterize the efficiency of different types of wetland to reduce ion content of greenhouse tomato effluent, and 2) improve the...

  6. Waterfowl community from a protected artificial wetland in Mexico State, Mexico

    Directory of Open Access Journals (Sweden)

    Arturo Hernández-Colina

    2017-11-01

    Full Text Available Wetlands are one of the most important ecosystems worldwide due to the great biologic diversity that they harbor and the re­sources and ecosystem services that they provide; however, their conservation is seriously threatened. Waterfowl are one of the most representative components of wetland biodiversity and the study of their communities is necessary to establish protection priorities appropriately. In this study, we describe the species richness and relative abundance of the waterfowl community of an artificial wetland in Mexico State which we visited from August 2010 to August 2011. We found 23 species, most of which belong to the Anatidae (ducks and Ardeidae (herons families and we recorded an accumulated abundance of 25,220 individuals. We performed an accumulation curve and we used Clench’s model which estimated 24 species; thus, we observed 95% of the predicted species. The arrival of migratory species contributed substantially to the increase of the species richness and the abundance of individuals, especially from October to March. We consider that the species richness and the abundance that we recorded, including observations of rare species, species reproducing, and species under a conservation category, are indicative of the great ecological value of this wetland despite its limited size. Therefore, it is relevant to assess ecological features of natural and artificial wetlands, including waterfowl communities, in order to improve the conservation actions in this region.

  7. Vegetation of natural and artificial shorelines in Upper Klamath Basin’s fringe wetlands

    Science.gov (United States)

    Ray, Andrew M.; Irvine, Kathryn M.; Hamilton, Andy S.

    2013-01-01

    The Upper Klamath Basin (UKB) in northern California and southern Oregon supports large hypereutrophic lakes surrounded by natural and artificial shorelines. Lake shorelines contain fringe wetlands that provide key ecological services to the people of this region. These wetlands also provide a context for drawing inferences about how differing wetland types and wave exposure contribute to the vegetative assemblages in lake-fringe wetlands. Here, we summarize how elevation profiles and vegetation richness vary as a function of wave exposure and wetland type. Our results show that levee wetland shorelines are 4X steeper and support fewer species than other wetland types. We also summarize the occurrence probability of the five common wetland plant species that represent the overwhelming majority of the diversity of these wetlands. In brief, the occurrence probability of the culturally significant Nuphar lutea spp. polysepala and the invasive Phalaris arundinacea in wave exposed and sheltered sites varies based on wetland type. The occurrence probability for P. arundinacea was greatest in exposed portions of deltaic shorelines, but these trends were reversed on levees where the occurrence probability was greater in sheltered sites. The widespread Schoenoplectus acutus var. acutus occurred throughout all wetland and exposure type combinations but had a higher probability of occurrence in wave exposed sites. Results from this work will add to our current understanding of how wetland shoreline profiles interact with wave exposure to influence the occurrence probability of the dominant vegetative species in UKB’s shoreline wetlands.

  8. Characterising and modelling groundwater discharge in anagricultural wetland on the French Atlantic coast

    Directory of Open Access Journals (Sweden)

    Ph. Weng

    2003-01-01

    Full Text Available Interaction between a wetland and its surrounding aquifer was studied in the Rochefort agricultural marsh (150 km2. Groundwater discharge in the marsh was measured with a network of nested piezometers. Hydrological modelling of the wetland showed that a water volume of 770,000 m3 yr–1 is discharging into the marsh, but that this water flux essentially takes place along the lateral borders of the wetland. However, this natural discharge volume represents only 20% of the artificial freshwater injected each year into the wetland to maintain the water level close to the soil surface. Understanding and quantifying the groundwater component in wetland hydrology is crucial for wetland management and conservation. Keywords: wetland, hydrology, groundwater, modelling, marsh

  9. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  10. Stochastic modeling of wetland-groundwater systems

    Science.gov (United States)

    Bertassello, Leonardo Enrico; Rao, P. Suresh C.; Park, Jeryang; Jawitz, James W.; Botter, Gianluca

    2018-02-01

    Modeling and data analyses were used in this study to examine the temporal hydrological variability in geographically isolated wetlands (GIWs), as influenced by hydrologic connectivity to shallow groundwater, wetland bathymetry, and subject to stochastic hydro-climatic forcing. We examined the general case of GIWs coupled to shallow groundwater through exfiltration or infiltration across wetland bottom. We also examined limiting case with the wetland stage as the local expression of the shallow groundwater. We derive analytical expressions for the steady-state probability density functions (pdfs) for wetland water storage and stage using few, scaled, physically-based parameters. In addition, we analyze the hydrologic crossing time properties of wetland stage, and the dependence of the mean hydroperiod on climatic and wetland morphologic attributes. Our analyses show that it is crucial to account for shallow groundwater connectivity to fully understand the hydrologic dynamics in wetlands. The application of the model to two different case studies in Florida, jointly with a detailed sensitivity analysis, allowed us to identify the main drivers of hydrologic dynamics in GIWs under different climate and morphologic conditions.

  11. Application of the artificial intelligence to estimate the constructed wetland response to heavy metal removal

    Energy Technology Data Exchange (ETDEWEB)

    Elektorowicz, M. [Concordia Univ., Building, Civil and Environmental Engineering, Montreal, Quebec (Canada)]. E-mail: mariae@civil.concordia.ca; Balanzinski, M. [Ecole Polytechnique de Montreal, Mechnical Engineering, Montreal, Quebec (Canada); Qasaimeh, A. [Concordia Univ., Building, Civil and Environmental Engineering, Montreal, Quebec (Canada)

    2002-06-15

    Current design approaches lack essential parameters necessary to evaluate the removal of metals contained in wastewater which is discharged to constructed wetlands. As a result, there is no guideline for an accurate design of constructed wetlands. An artificial intelligence approach was used to assess constructed wetland design. For this purpose concentrations of bioavailable mercury were evaluated in conditions where initial concentrations of inorganic mercury, chloride concentrations and pH values changed. Fuzzy knowledge base was built based on results obtained from previous investigations performed in a greenhouse for floating plants, and from computations for mercury speciation. The Fuzzy Decision Support System (FDSS) used the knowledge base to find parameters that permit to generate the highest amount of mercury available for plants. The findings of this research can be applied to wetlands and all natural processes where correlations between them are uncertain. (author)

  12. Application of the artificial intelligence to estimate the constructed wetland response to heavy metal removal

    International Nuclear Information System (INIS)

    Elektorowicz, M.; Balanzinski, M.; Qasaimeh, A.

    2002-01-01

    Current design approaches lack essential parameters necessary to evaluate the removal of metals contained in wastewater which is discharged to constructed wetlands. As a result, there is no guideline for an accurate design of constructed wetlands. An artificial intelligence approach was used to assess constructed wetland design. For this purpose concentrations of bioavailable mercury were evaluated in conditions where initial concentrations of inorganic mercury, chloride concentrations and pH values changed. Fuzzy knowledge base was built based on results obtained from previous investigations performed in a greenhouse for floating plants, and from computations for mercury speciation. The Fuzzy Decision Support System (FDSS) used the knowledge base to find parameters that permit to generate the highest amount of mercury available for plants. The findings of this research can be applied to wetlands and all natural processes where correlations between them are uncertain. (author)

  13. Habitat edges affect patterns of artificial nest predation along a wetland-meadow boundary

    Czech Academy of Sciences Publication Activity Database

    Suvorov, P.; Svobodová, J.; Albrecht, Tomáš

    2014-01-01

    Roč. 59, č. 1 (2014), s. 91-96 ISSN 1146-609X R&D Projects: GA ČR GA206/06/0851 Institutional support: RVO:68081766 Keywords : Artificial nest * Ecotonal effect * Habitat fragmentation * Nest predation * Wetland meadow Subject RIV: EG - Zoology Impact factor: 1.617, year: 2014

  14. Transport of pesticides and artificial tracers in vertical-flow lab-scale wetlands

    Science.gov (United States)

    Durst, Romy; Imfeld, Gwenaël.; Lange, Jens

    2013-01-01

    Wetland systems can be hydrologically connected to a shallow aquifer and intercept upward flow of pesticide-contaminated water during groundwater discharge. However, pesticide transport and attenuation through wetland sediments (WSs) intercepting contaminated water is rarely evaluated quantitatively. The use of artificial tracers to evaluate pesticide transport and associated risks is a fairly new approach that requires evaluation and validation. Here we evaluate during 84 days the transport of two pesticides (i.e., isoproturon (IPU) and metalaxyl (MTX)) and three tracers (i.e., bromide (Br), uranine (UR), and sulforhodamine B (SRB)) in upward vertical-flow vegetated and nonvegetated lab-scale wetlands. The lab-scale wetlands were filled with outdoor WSs and were continuously supplied with tracers and the pesticide-contaminated water. The transport of IPU and UR was characterized by high solute recovery (approximately 80%) and low retardation compared to Br. The detection of desmethylisoproturon in the wetlands indicated IPU degradation. SRB showed larger retardation (>3) and lower recovery (approximately 60%) compared to Br, indicating that sorption controlled SRB transport. MTX was moderately retarded (approximately 1.5), and its load attenuation in the wetland reached 40%. In the vegetated wetland, preferential flow along the roots decreased interactions between solutes and sediments, resulting in larger pesticide and tracer recovery. Our results show that UR and IPU have similar transport characteristics under the tested subsurface-flow conditions, whereas SRB may serve as a proxy for less mobile and more persistent pesticides. Since UR and SRB are not significantly affected by degradation, their use as proxies for fast degrading pollutants may be limited. We anticipate our results to be a starting point for considering artificial tracers for investigating pesticide transport in environments at groundwater/surface-water interfaces.

  15. Modeling natural wetlands: A new global framework built on wetland observations

    Science.gov (United States)

    Matthews, E.; Romanski, J.; Olefeldt, D.

    2015-12-01

    Natural wetlands are the world's largest methane (CH4) source, and their distribution and CH4 fluxes are sensitive to interannual and longer-term climate variations. Wetland distributions used in wetland-CH4 models diverge widely, and these geographic differences contribute substantially to large variations in magnitude, seasonality and distribution of modeled methane fluxes. Modeling wetland type and distribution—closely tied to simulating CH4 emissions—is a high priority, particularly for studies of wetlands and CH4 dynamics under past and future climates. Methane-wetland models either prescribe or simulate methane-producing areas (aka wetlands) and both approaches result in predictable over- and under-estimates. 1) Monthly satellite-derived inundation data include flooded areas that are not wetlands (e.g., lakes, reservoirs, and rivers), and do not identify non-flooded wetlands. 2) Models simulating methane-producing areas overwhelmingly rely on modeled soil moisture, systematically over-estimating total global area, with regional over- and under-estimates, while schemes to model soil-moisture typically cannot account for positive water tables (i.e., flooding). Interestingly, while these distinct hydrological approaches to identify wetlands are complementary, merging them does not provide critical data needed to model wetlands for methane studies. We present a new integrated framework for modeling wetlands, and ultimately their methane emissions, that exploits the extensive body of data and information on wetlands. The foundation of the approach is an existing global gridded data set comprising all and only wetlands, including vegetation information. This data set is augmented with data inter alia on climate, inundation dynamics, soil type and soil carbon, permafrost, active-layer depth, growth form, and species composition. We investigate this enhanced wetland data set to identify which variables best explain occurrence and characteristics of observed

  16. Greenhouse gas production and efficiency of planted and artificially aerated constructed wetlands

    Energy Technology Data Exchange (ETDEWEB)

    Maltais-Landry, Gabriel [Departement des sciences biologiques, Universite de Montreal 90, rue Vincent-D' Indy, Montreal (Ciheam), H2V 2S9 (Canada); Institut de recherche en biologie vegetale, Universite de Montreal 4101, rue Sherbrooke Est, Montreal (Ciheam), H1X 2B2 (Canada)], E-mail: gabriel.maltais-landry@umontreal.ca; Maranger, Roxane [Departement des sciences biologiques, Universite de Montreal 90, rue Vincent-D' Indy, Montreal (Ciheam), H2V 2S9 (Canada)], E-mail: r.maranger@umontreal.ca; Brisson, Jacques [Departement des sciences biologiques, Universite de Montreal 90, rue Vincent-D' Indy, Montreal (Ciheam), H2V 2S9 (Canada); Institut de recherche en biologie vegetale, Universite de Montreal 4101, rue Sherbrooke Est, Montreal (Ciheam), H1X 2B2 (Canada)], E-mail: jacques.brisson@umontreal.ca; Chazarenc, Florent [Institut de recherche en biologie vegetale, Universite de Montreal 4101, rue Sherbrooke Est, Montreal (Ciheam), H1X 2B2 (Canada)

    2009-03-15

    Greenhouse gas (GHG) emissions by constructed wetlands (CWs) could mitigate the environmental benefits of nutrient removal in these man-made ecosystems. We studied the effect of 3 different macrophyte species and artificial aeration on the rates of nitrous oxide (N{sub 2}O), carbon dioxide (CO{sub 2}) and methane (CH{sub 4}) production in CW mesocosms over three seasons. CW emitted 2-10 times more GHG than natural wetlands. Overall, CH{sub 4} was the most important GHG emitted in unplanted treatments. Oxygen availability through artificial aeration reduced CH{sub 4} fluxes. Plant presence also decreased CH{sub 4} fluxes but favoured CO{sub 2} production. Nitrous oxide had a minor contribution to global warming potential (GWP < 15%). The introduction of oxygen through artificial aeration combined with plant presence, particularly Typha angustifolia, had the overall best performance among the treatments tested in this study, including lowest GWP, greatest nutrient removal, and best hydraulic properties. - Methane is the main greenhouse gas produced in constructed wetlands and oxygen availability is the main factor controlling fluxes.

  17. Wastewater treatment by artificial wetlands in the Museum of Popular Culture of the National University

    Directory of Open Access Journals (Sweden)

    Carolina Alfaro

    2013-06-01

    Full Text Available The fulfillment of the Millennium Development Goals in terms of sustainable access to sanitation requires increasing the development of research programs that promote simple and low cost technological options, appropriate to the social, economic, and environmental conditions of each population. These processes must be accompanied by actions of environmental and sanitation education, which allow appropriation of these systems by the communities. In this sense, there are two projects in the National University converging on this subject. The Museum of Popular Culture together with the Public Service Company of Heredia develop an environmental education project that promotes the protection of water, from an historical perspective of its management, which has an artificial wetland as the main teaching unit. On the other hand, the Waste Management Laboratory at the School of Chemistry evaluates the performance of this artificial wetland as part of a research project that promotes this type of alternative sanitation. This paper presents results of the monitoring of this artificial wetland, showing average removal percentages of 93% BOD5,20 , 95% COD, 73% P-PO4, and 95% for SS.

  18. Remediation of mercury-polluted soils using artificial wetlands.

    Science.gov (United States)

    García-Mercadoa, Héctor Daniel; Fernándezb, Georgina; Garzón-Zúñigac, Marco Antonio; Durán-Domínguez-de-Bazúaa, María Del Carmen

    2017-01-02

    Mexico's mercury mining industry is important for economic development, but has unfortunately contaminated soils due to open-air disposal. This case was seen at two sites in the municipality of Pinal de Amoles, State of Queretaro, Mexico. This paper presents an evaluation of mercury dynamics and biogeochemistry in two soils (mining waste soil) using ex-situ wetlands over 36 weeks. In soils sampled in two former mines of Pinal de Amoles, initial mercury concentrations were 424 ± 29 and 433 ± 12 mg kg -1 in La Lorena and San Jose, former mines, respectively. Typha latifolia and Phragmites australis were used and 20 reactors were constructed (with and without plants). The reactors were weekly amended with a nutrient solution (NPK), for each plant, at a pH of 5.0. For remediation using soils from San Jose 70-78% of mercury was removed in T. latifolia reactors and 76-82% in P. australis reactors, and for remediation of soils from La Lorena, mercury content was reduced by 55-71% using T. latifolia and 58-66% in P. australis reactors. Mercury emissions into the atmosphere were estimated to be 2-4 mg m -2 h -1 for both soils.

  19. Process-Based Modeling of Constructed Wetlands

    Science.gov (United States)

    Baechler, S.; Brovelli, A.; Rossi, L.; Barry, D. A.

    2007-12-01

    Constructed wetlands (CWs) are widespread facilities for wastewater treatment. In subsurface flow wetlands, contaminated wastewater flows through a porous matrix, where oxidation and detoxification phenomena occur. Despite the large number of working CWs, system design and optimization are still mainly based upon empirical equations or simplified first-order kinetics. This results from an incomplete understanding of the system functioning, and may in turn hinder the performance and effectiveness of the treatment process. As a result, CWs are often considered not suitable to meet high water quality-standards, or to treat water contaminated with recalcitrant anthropogenic contaminants. To date, only a limited number of detailed numerical models have been developed and successfully applied to simulate constructed wetland behavior. Among these, one of the most complete and powerful is CW2D, which is based on Hydrus2D. The aim of this work is to develop a comprehensive simulator tailored to model the functioning of horizontal flow constructed wetlands and in turn provide a reliable design and optimization tool. The model is based upon PHWAT, a general reactive transport code for saturated flow. PHWAT couples MODFLOW, MT3DMS and PHREEQC-2 using an operator-splitting approach. The use of PHREEQC to simulate reactions allows great flexibility in simulating biogeochemical processes. The biogeochemical reaction network is similar to that of CW2D, and is based on the Activated Sludge Model (ASM). Kinetic oxidation of carbon sources and nutrient transformations (nitrogen and phosphorous primarily) are modeled via Monod-type kinetic equations. Oxygen dissolution is accounted for via a first-order mass-transfer equation. While the ASM model only includes a limited number of kinetic equations, the new simulator permits incorporation of an unlimited number of both kinetic and equilibrium reactions. Changes in pH, redox potential and surface reactions can be easily incorporated

  20. Aquaculture in artificially developed wetlands in urban areas: an application of the bivariate relationship between soil and surface water in landscape ecology.

    Science.gov (United States)

    Paul, Abhijit

    2011-01-01

    Wetlands show a strong bivariate relationship between soil and surface water. Artificially developed wetlands help to build landscape ecology and make built environments sustainable. The bheries, wetlands of eastern Calcutta (India), utilize the city sewage to develop urban aquaculture that supports the local fish industries and opens a new frontier in sustainable environmental planning research.

  1. Development of a "Hydrologic Equivalent Wetland" Concept for Modeling Cumulative Effects of Wetlands on Watershed Hydrology

    Science.gov (United States)

    Wang, X.; Liu, T.; Li, R.; Yang, X.; Duan, L.; Luo, Y.

    2012-12-01

    Wetlands are one of the most important watershed microtopographic features that affect, in combination rather than individually, hydrologic processes (e.g., routing) and the fate and transport of constituents (e.g., sediment and nutrients). Efforts to conserve existing wetlands and/or to restore lost wetlands require that watershed-level effects of wetlands on water quantity and water quality be quantified. Because monitoring approaches are usually cost or logistics prohibitive at watershed scale, distributed watershed models, such as the Soil and Water Assessment Tool (SWAT), can be a best resort if wetlands can be appropriately represented in the models. However, the exact method that should be used to incorporate wetlands into hydrologic models is the subject of much disagreement in the literature. In addition, there is a serious lack of information about how to model wetland conservation-restoration effects using such kind of integrated modeling approach. The objectives of this study were to: 1) develop a "hydrologic equivalent wetland" (HEW) concept; and 2) demonstrate how to use the HEW concept in SWAT to assess effects of wetland restoration within the Broughton's Creek watershed located in southwestern Manitoba of Canada, and of wetland conservation within the upper portion of the Otter Tail River watershed located in northwestern Minnesota of the United States. The HEWs were defined in terms of six calibrated parameters: the fraction of the subbasin area that drains into wetlands (WET_FR), the volume of water stored in the wetlands when filled to their normal water level (WET_NVOL), the volume of water stored in the wetlands when filled to their maximum water level (WET_MXVOL), the longest tributary channel length in the subbasin (CH_L1), Manning's n value for the tributary channels (CH_N1), and Manning's n value for the main channel (CH_N2). The results indicated that the HEW concept allows the nonlinear functional relations between watershed processes

  2. Nitrous oxide and methane emission in an artificial wetland treating polluted runoff from an agricultural catchment

    Science.gov (United States)

    Mander, Ülo; Tournebize, Julien; Soosaar, Kaido; Chaumont, Cedric; Hansen, Raili; Muhel, Mart; Teemusk, Alar; Vincent, Bernard

    2015-04-01

    An artificial wetland built in 2010 to reduce water pollution in a drained agricultural watershed showed real potential for pesticide and nitrate removal. The 1.2 ha off-shore wetland with a depth of from 0.1 to 1 m intercepts drainage water from a 450 ha watershed located near the village of Rampillon (03°03'37.3'' E, 48°32'16.7'' N, 70 km south-east of Paris, France). A sluice gate installed at the inlet makes it possible to close the wetland during the winter months (December - March), when no pesticides are applied and rainfall events are more frequent. The flow entering the wetland fluctuates from 0 to 120 L/s. The wetland is partially covered by Carex spp., Phragmites australis, Juncus conglomeratus, Typha latifolia and philamentous algae. Since 2011, an automatic water quality monitoring system measures water discharge, temperature, dissolved O2, conductivity pH, NO3- and DOC in both inlet and outlet. In May 2014, an automatic weather station and Campbell Irgason system for the measurement of CO2 and H2O fluxes were installed in the middle of the wetland. In May and November 2014 one-week high frequency measurement campaigns were conducted to study N2O and CH4 fluxes using 6 manually operated opaque floating static chambers and 12 floating automatic dynamic chambers. The latter were operated via multiplexer and had an incubation time of 5 minutes, whereas the gas flow was continuously measured using the Aerodyne TILDAS quantum cascade laser system. During the campaign, the reduction of NO3- concentration was measured in nine reactor pipes. Also, water samples were collected for N2O and N2 isotope analysis, and sediments were collected for potential N2 emission measurements. In May, the hydraulic retention time (HRT) was 30 days, and the average NO3- concentration decreased from 24 in the inflow to 0 mg/L in the outflow. Methane flux was relatively high (average 1446, variation 0.2-113990 μg CH4-C m-2 h-1), while about 2/3 was emitted via ebullition

  3. Conceptual hierarchical modeling to describe wetland plant community organization

    Science.gov (United States)

    Little, A.M.; Guntenspergen, G.R.; Allen, T.F.H.

    2010-01-01

    Using multivariate analysis, we created a hierarchical modeling process that describes how differently-scaled environmental factors interact to affect wetland-scale plant community organization in a system of small, isolated wetlands on Mount Desert Island, Maine. We followed the procedure: 1) delineate wetland groups using cluster analysis, 2) identify differently scaled environmental gradients using non-metric multidimensional scaling, 3) order gradient hierarchical levels according to spatiotem-poral scale of fluctuation, and 4) assemble hierarchical model using group relationships with ordination axes and post-hoc tests of environmental differences. Using this process, we determined 1) large wetland size and poor surface water chemistry led to the development of shrub fen wetland vegetation, 2) Sphagnum and water chemistry differences affected fen vs. marsh / sedge meadows status within small wetlands, and 3) small-scale hydrologic differences explained transitions between forested vs. non-forested and marsh vs. sedge meadow vegetation. This hierarchical modeling process can help explain how upper level contextual processes constrain biotic community response to lower-level environmental changes. It creates models with more nuanced spatiotemporal complexity than classification and regression tree procedures. Using this process, wetland scientists will be able to generate more generalizable theories of plant community organization, and useful management models. ?? Society of Wetland Scientists 2009.

  4. Macroinvertebrate assemblages and biodiversity levels: ecological role of constructed wetlands and artificial ponds in a natural park

    Directory of Open Access Journals (Sweden)

    Laura Sartori

    2014-02-01

    Full Text Available Normal 0 14 false false false MicrosoftInternetExplorer4 Constructed wetlands play an important role in water supply, floodwater retention and nutrient removal, at the same time allowing the restoration of lost habitat and the preservation of biodiversity. There is little knowledge about the biodiversity that can be found in these artificial environments along time, especially at the invertebrate community level. Macroinvertebrate assemblages, water chemistry, morphology, and environmental characteristics of natural ponds, artificial pools and constructed wetlands in Parco Pineta (Northern Italy were studied to evaluate the effects of local factors on macroinvertebrate communities. The objective was to verify if each ecosystem could equally contribute to local biodiversity, regardless of its natural or artificial origin. Principal Components Analysis showed that ponds were divided into clusters, based on their morphology and their water quality, independently from their origin. The composition of macroinvertebrate communities was similar among natural wetlands and ponds artificially created to provide new habitats in the park, while it was different among natural wetlands and constructed wetlands created for wastewater treatment purposes. Biodiversity of natural ponds and constructed wetlands, evaluated using taxa richness, Shannon index, and Pielou index, was comparable. Canonical Correspondence Analysis highlighted differences in macroinvertebrate community composition and pointed out the relationships among macroinvertebrates and various environmental variables: habitat heterogeneity resulted as the most relevant factor that influences taxa richness. Water quality also affects the macroinvertebrate community structure. We determined that constructed wetlands with higher pollutant concentrations show different assemblage compositions but comparable overall macroinvertebrate biodiversity. Constructed wetlands became valuable ecological elements

  5. Can Artificial Ecosystems Enhance Local Biodiversity? The Case of a Constructed Wetland in a Mediterranean Urban Context

    Science.gov (United States)

    De Martis, Gabriele; Mulas, Bonaria; Malavasi, Veronica; Marignani, Michela

    2016-05-01

    Constructed wetlands (CW) are considered a successful tool to treat wastewater in many countries: their success is mainly assessed observing the rate of pollution reduction, but CW can also contribute to the conservation of ecosystem services. Among the many ecosystem services provided, the biodiversity of CW has received less attention. The EcoSistema Filtro (ESF) of the Molentargius-Saline Regional Natural Park is a constructed wetland situated in Sardinia (Italy), built to filter treated wastewater, increase habitat diversity, and enhance local biodiversity. A floristic survey has been carried out yearly 1 year after the construction of the artificial ecosystem in 2004, observing the modification of the vascular flora composition in time. The flora of the ESF accounted for 54 % of the whole Regional Park's flora; alien species amount to 12 %; taxa of conservation concern are 6 %. Comparing the data in the years, except for the biennium 2006/2007, we observed a continuous increase of species richness, together with an increase of endemics, species of conservation concern, and alien species too. Once the endemics appeared, they remained part of the flora, showing a good persistence in the artificial wetland. Included in a natural park, but trapped in a sprawling and fast growing urban context, this artificial ecosystem provides multiple uses, by preserving and enhancing biodiversity. This is particularly relevant considering that biodiversity can act as a driver of sustainable development in urban areas where most of the world's population lives and comes into direct contact with nature.

  6. Can Artificial Ecosystems Enhance Local Biodiversity? The Case of a Constructed Wetland in a Mediterranean Urban Context.

    Science.gov (United States)

    De Martis, Gabriele; Mulas, Bonaria; Malavasi, Veronica; Marignani, Michela

    2016-05-01

    Constructed wetlands (CW) are considered a successful tool to treat wastewater in many countries: their success is mainly assessed observing the rate of pollution reduction, but CW can also contribute to the conservation of ecosystem services. Among the many ecosystem services provided, the biodiversity of CW has received less attention. The EcoSistema Filtro (ESF) of the Molentargius-Saline Regional Natural Park is a constructed wetland situated in Sardinia (Italy), built to filter treated wastewater, increase habitat diversity, and enhance local biodiversity. A floristic survey has been carried out yearly 1 year after the construction of the artificial ecosystem in 2004, observing the modification of the vascular flora composition in time. The flora of the ESF accounted for 54% of the whole Regional Park's flora; alien species amount to 12%; taxa of conservation concern are 6%. Comparing the data in the years, except for the biennium 2006/2007, we observed a continuous increase of species richness, together with an increase of endemics, species of conservation concern, and alien species too. Once the endemics appeared, they remained part of the flora, showing a good persistence in the artificial wetland. Included in a natural park, but trapped in a sprawling and fast growing urban context, this artificial ecosystem provides multiple uses, by preserving and enhancing biodiversity. This is particularly relevant considering that biodiversity can act as a driver of sustainable development in urban areas where most of the world's population lives and comes into direct contact with nature.

  7. Model parameters for representative wetland plant functional groups

    Science.gov (United States)

    Williams, Amber S.; Kiniry, James R.; Mushet, David M.; Smith, Loren M.; McMurry, Scott T.; Attebury, Kelly; Lang, Megan; McCarty, Gregory W.; Shaffer, Jill A.; Effland, William R.; Johnson, Mari-Vaughn V.

    2017-01-01

    Wetlands provide a wide variety of ecosystem services including water quality remediation, biodiversity refugia, groundwater recharge, and floodwater storage. Realistic estimation of ecosystem service benefits associated with wetlands requires reasonable simulation of the hydrology of each site and realistic simulation of the upland and wetland plant growth cycles. Objectives of this study were to quantify leaf area index (LAI), light extinction coefficient (k), and plant nitrogen (N), phosphorus (P), and potassium (K) concentrations in natural stands of representative plant species for some major plant functional groups in the United States. Functional groups in this study were based on these parameters and plant growth types to enable process-based modeling. We collected data at four locations representing some of the main wetland regions of the United States. At each site, we collected on-the-ground measurements of fraction of light intercepted, LAI, and dry matter within the 2013–2015 growing seasons. Maximum LAI and k variables showed noticeable variations among sites and years, while overall averages and functional group averages give useful estimates for multisite simulation modeling. Variation within each species gives an indication of what can be expected in such natural ecosystems. For P and K, the concentrations from highest to lowest were spikerush (Eleocharis macrostachya), reed canary grass (Phalaris arundinacea), smartweed (Polygonum spp.), cattail (Typha spp.), and hardstem bulrush (Schoenoplectus acutus). Spikerush had the highest N concentration, followed by smartweed, bulrush, reed canary grass, and then cattail. These parameters will be useful for the actual wetland species measured and for the wetland plant functional groups they represent. These parameters and the associated process-based models offer promise as valuable tools for evaluating environmental benefits of wetlands and for evaluating impacts of various agronomic practices in

  8. Wetland biogeochemical processes and simulation modeling

    Science.gov (United States)

    Bai, Junhong; Huang, Laibin; Gao, Haifeng; Jia, Jia; Wang, Xin

    2018-02-01

    As the important landscape with rich biodiversity and high productivity, wetlands can provide numerous ecological services including playing an important role in regulating global biogeochemical cycles, filteringpollutants from terrestrial runoff and atmospheric deposition, protecting and improving water quality, providing living habitats for plants and animals, controlling floodwaters, and retaining surface water flow during dry periods (Reddy and DeLaune, 2008; Qin and Mitsch, 2009; Zhao et al., 2016). However, more than 50% of the world's wetlands had been altered, degraded or lost through a wide range of human activities in the past 150 years, and only a small percentage of the original wetlands remained around the world after over two centuries of intensive development and urbanization (O'connell, 2003; Zhao et al., 2016).

  9. Artificial Immune Networks: Models and Applications

    Directory of Open Access Journals (Sweden)

    Xian Shen

    2008-06-01

    Full Text Available Artificial Immune Systems (AIS, which is inspired by the nature immune system, has been applied for solving complex computational problems in classification, pattern rec- ognition, and optimization. In this paper, the theory of the natural immune system is first briefly introduced. Next, we compare some well-known AIS and their applications. Several representative artificial immune networks models are also dis- cussed. Moreover, we demonstrate the applications of artificial immune networks in various engineering fields.

  10. A Model for Wetland Hydrology: Description and Validation

    Science.gov (United States)

    R.S. Mansell; S.A. Bloom; Ge Sun

    2000-01-01

    WETLANDS, a multidimensional model describing water flow in variably saturated soil and evapotranspiration, was used to simulate successfully 3-years of local hydrology for a cypress pond located within a relatively flat Coastal Plain pine forest landscape. Assumptions included negligible net regional groundwater flow and radially symmetric local flow impinging on a...

  11. Transport modeling: An artificial immune system approach

    Directory of Open Access Journals (Sweden)

    Teodorović Dušan

    2006-01-01

    Full Text Available This paper describes an artificial immune system approach (AIS to modeling time-dependent (dynamic, real time transportation phenomenon characterized by uncertainty. The basic idea behind this research is to develop the Artificial Immune System, which generates a set of antibodies (decisions, control actions that altogether can successfully cover a wide range of potential situations. The proposed artificial immune system develops antibodies (the best control strategies for different antigens (different traffic "scenarios". This task is performed using some of the optimization or heuristics techniques. Then a set of antibodies is combined to create Artificial Immune System. The developed Artificial Immune transportation systems are able to generalize, adapt, and learn based on new knowledge and new information. Applications of the systems are considered for airline yield management, the stochastic vehicle routing, and real-time traffic control at the isolated intersection. The preliminary research results are very promising.

  12. Predictive modelling of wetland occurrence in KwaZulu-Natal, South Africa

    Directory of Open Access Journals (Sweden)

    Jens Hiestermann

    2015-07-01

    Full Text Available The global trend of transformation and loss of wetlands through conversion to other land uses has deleterious effects on surrounding ecosystems, and there is a resultant increasing need for the conservation and preservation of wetlands. Improved mapping of wetland locations is critical to achieving objective regional conservation goals, which depends on accurate spatial knowledge. Current approaches to mapping wetlands through the classification of satellite imagery typically under-represents actual wetland area; the importance of ancillary data in improving accuracy in mapping wetlands is therefore recognised. In this study, we compared two approaches Bayesian networks and logistic regression to predict the likelihood of wetland occurrence in KwaZulu-Natal, South Africa. Both approaches were developed using the same data set of environmental surrogate predictors. We compared and verified model outputs using an independent test data set, with analyses including receiver operating characteristic curves and area under the curve (AUC. Both models performed similarly (AUC>0.84, indicating the suitability of a likelihood approach for ancillary data for wetland mapping. Results indicated that high wetland probability areas in the final model outputs correlated well with known wetland systems and wetland-rich areas in KwaZulu-Natal. We conclude that predictive models have the potential to improve the accuracy of wetland mapping in South Africa by serving as valuable ancillary data.

  13. The landscape pattern characteristics of coastal wetlands in Jiaozhou Bay under the impact of human activities.

    Science.gov (United States)

    Gu, Dongqi; Zhang, Yuanzhi; Fu, Jun; Zhang, Xuliang

    2007-01-01

    In this study, we interpreted coastal wetland types from an ASTER satellite image in 2002, and then compared the results with the land-use status of coastal wetlands in 1952 to determine the wetland loss and degradation around Jiaozhou Bay. Seven types of wetland landscape were classified, namely: shallow open water, inter-tidal flats, estuarine water, brackish marshes, salt ponds, fishery ponds and ports. Several landscape pattern indices were analysed: the results indicate that the coastal wetlands have been seriously degraded. More and more natural wetlands have been transformed into artificial wetlands, which covered about 33.7% of the total wetlands in 2002. In addition, we used a defined model to assess the impacts of human activities on coastal wetlands. The results obtained show that the coastal wetlands of Jiaozhou Bay have suffered severe human disturbance. Effective coastal management and control is therefore needed to solve the issues of the coastal wetland loss and degradation existing in this area.

  14. Response of ammonium removal to growth and transpiration of Juncus effusus during the treatment of artificial sewage in laboratory-scale wetlands.

    Science.gov (United States)

    Wiessner, A; Kappelmeyer, U; Kaestner, M; Schultze-Nobre, L; Kuschk, P

    2013-09-01

    The correlation between nitrogen removal and the role of the plants in the rhizosphere of constructed wetlands are the subject of continuous discussion, but knowledge is still insufficient. Since the influence of plant growth and physiological activity on ammonium removal has not been well characterized in constructed wetlands so far, this aspect is investigated in more detail in model wetlands under defined laboratory conditions using Juncus effusus for treating an artificial sewage. Growth and physiological activity, such as plant transpiration, have been found to correlate with both the efficiency of ammonium removal within the rhizosphere of J. effusus and the methane formation. The uptake of ammonium by growing plant stocks is within in a range of 45.5%, but under conditions of plant growth stagnation, a further nearly complete removal of the ammonium load points to the likely existence of additional nitrogen removal processes. In this way, a linear correlation between the ammonium concentration inside the rhizosphere and the transpiration of the plant stocks implies that an influence of plant physiological activity on the efficiency of N-removal exists. Furthermore, a linear correlation between methane concentration and plant transpiration has been estimated. The findings indicate a fast response of redox processes to plant activities. Accordingly, not only the influence of plant transpiration activity on the plant-internal convective gas transport, the radial oxygen loss by the plant roots and the efficiency of nitrification within the rhizosphere, but also the nitrogen gas released by phytovolatilization are discussed. The results achieved by using an unplanted control system are different in principle and characterized by a low efficiency of ammonium removal and a high methane enrichment of up to a maximum of 72.7% saturation. Copyright © 2013 Elsevier Ltd. All rights reserved.

  15. Hydrodynamic modelling of a tidal delta wetland using an enhanced quasi-2D model

    Science.gov (United States)

    Wester, Sjoerd J.; Grimson, Rafael; Minotti, Priscilla G.; Booija, Martijn J.; Brugnach, Marcela

    2018-04-01

    Knowledge about the hydrological regime of wetlands is key to understand their physical and biological properties. Modelling hydrological and hydrodynamic processes within a wetland is therefore becoming increasingly important. 3D models have successfully modelled wetland dynamics but depend on very detailed bathymetry and land topography. Many 1D and 2D models of river deltas highly simplify the interaction between the river and wetland area or simply neglect the wetland area. This study proposes an enhanced quasi-2D modelling strategy that captures the interaction between river discharge and moon tides and the resulting hydrodynamics, while using the scarce data available. The water flow equations are discretised with an interconnected irregular cell scheme, in which a simplification of the 1D Saint-Venant equations is used to define the water flow between cells. The spatial structure of wetlands is based on the ecogeomorphology in complex estuarine deltas. The islands within the delta are modelled with levee cells, creek cells and an interior cell representing a shallow marsh wetland. The model is calibrated for an average year and the model performance is evaluated for another average year and additionally an extreme dry three-month period and an extreme wet three-month period. The calibration and evaluation are done based on two water level measurement stations and two discharge measurement stations, all located in the main rivers. Additional calibration is carried out with field water level measurements in a wetland area. Accurate simulations are obtained for both calibration and evaluation with high correlations between observed and simulated water levels and simulated discharges in the same order of magnitude as observed discharges. Calibration against field measurements showed that the model can successfully simulate the overflow mechanism in wetland areas. A sensitivity analysis for several wetland parameters showed that these parameters are all

  16. "Wetlands: Water Living Filters?",

    OpenAIRE

    Dordio, Ana; Palace, A. J.; Pinto, Ana Paula

    2008-01-01

    Human societies have indirectly used natural wetlands as wastewater discharge sites for many centuries. Observations of the wastewater depuration capacity of natural wetlands have led to a greater understanding of the potential of these ecosystems for pollutant assimilation and have stimulated the development of artificial wetlands systems for treatment of wastewaters from a variety of sources. Constructed wetlands, in contrast to natural wetlands, are human-made systems that are designed, bu...

  17. Mitigating agrichemicals from an artificial runoff event using a managed riverine wetland.

    Science.gov (United States)

    Lizotte, Richard E; Shields, F Douglas; Murdock, Justin N; Kröger, Robert; Knight, Scott S

    2012-06-15

    We examined the mitigation efficiency of a managed riverine wetland amended with a mixture of suspended sediment, two nutrients (nitrogen and phosphorus), and three pesticides (atrazine, metolachlor, and permethrin) during a simulated agricultural runoff event. Hydrologic management of the 500 m-long, 25 m-wide riverine wetland was done by adding weirs at both ends. The agrichemical mixture was amended to the wetland at the upstream weir simulating a four-hour, ~1cm rainfall event from a 16ha agricultural field. Water samples (1L) were collected every 30 min within the first 4h, then every 4h until 48 h, and again on days 5, 7, 14, 21, and 28 post-amendment at distances of 0m, 10 m, 40 m, 300 m and 500 m from the amendment point within the wetland for suspended solids, nutrient, and pesticide analyses. Peak sediment, nutrient, and pesticide concentrations occurred within 3 h of amendment at 0m, 10 m, 40 m, and 300 m downstream and showed rapid attenuation of agrichemicals from the water column with 79-98%, 42-98%, and 63-98% decrease in concentrations of sediments, nutrients, and pesticides, respectively, within 48 h. By day 28, all amendments were near or below pre-amendment concentrations. Water samples at 500 m showed no changes in sediment or nutrient concentrations; pesticide concentrations peaked within 48 h but at ≤11% of upstream peak concentrations and had dissipated by day 28. Managed riverine wetlands≥1 ha and with hydraulic residence times of days to weeks can efficiently trap agricultural runoff during moderate (1cm) late-spring and early-summer rainfall events, mitigating impacts to receiving rivers. Published by Elsevier B.V.

  18. Inclusion of Riparian Wetland Module (RWM) into the SWAT model for assessment of wetland hydrological benefit

    Science.gov (United States)

    Wetlands are an integral part of many agricultural watersheds. They provide multiple ecosystem functions, such as improving water quality, mitigating flooding, and serving as natural habitats. Those functions are highly depended on wetland hydrological characteristics and their connectivity to the d...

  19. Artificial neural network cardiopulmonary modeling and diagnosis

    Science.gov (United States)

    Kangas, Lars J.; Keller, Paul E.

    1997-01-01

    The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.

  20. Understanding the Hydrodynamics of a Coastal Wetland with an Integrated Distributed Model

    Science.gov (United States)

    Zhang, Y.; Li, W.; Sun, G.

    2017-12-01

    Coastal wetlands linking ocean and terrestrial landscape provide important ecosystem services including flood mitigation, fresh water supply, erosion control, carbon sequestration, and wildlife habitats. Wetland hydrology is the major driving force for wetland formation, structure, function, and ecosystem services. The dynamics of wetland hydrology and energy budget are strongly affected by frequent inundation and drying of wetland soil and vegetation due to tide, sea level rise (SLR) and climatic variability (change). However, the quantitative representation of how the energy budget and groundwater variation of coastal wetlands respond to frequent water level fluctuation is limited, especially at regional scales. This study developed a physically based distributed wetland hydrological model by integrating coastal processes and considering the inundation influence on energy budget and ET. Analysis using in situ measurements and satellite data for a coastal wetland in North Carolina confirm that the model sufficiently captures the wetland hydrologic behaviors. The validated model was then applied to examine the wetland hydrodynamics under a 30-year historical climate forcing (1985-2014) for the wetland region. The simulation reveals that 43% of the study area has inundation events, 63% of which has a frequency higher than 50% each year. The canopy evaporation and transpiration decline dramatically when the inundation level exceeds the canopy height. Additionally, inundation causes about 10% increase of the net shortwave radiation. This study also demonstrates that the critical wetland zones highly influenced by the coastal processes spans 300-800 m from the coastline. The model developed in the study offers a new tool for understanding the complex wetland hydrodynamics in response to natural and human-induced disturbances at landscape to regional scales.

  1. A simple procedure to model water level fluctuations in partially inundated wetlands

    NARCIS (Netherlands)

    Spieksma, JFM; Schouwenaars, JM

    When modelling groundwater behaviour in wetlands, there are specific problems related to the presence of open water in small-sized mosaic patterns. A simple quasi two-dimensional model to predict water level fluctuations in partially inundated wetlands is presented. In this model, the ratio between

  2. Evapotranspiration from drained wetlands: drivers, modeling, storage functions, and restoration implications

    Science.gov (United States)

    Shukla, S.; Wu, C. L.; Shrestha, N.

    2017-12-01

    Abstract Evapotranspiration (ET) is a major component of wetland and watershed water budgets. The effect of wetland drainage on ET is not well understood. We tested whether the current understanding of insignificant effect of drainage on ET in the temperate region wetlands applies to those in the sub-tropics. Eddy covariance (EC) based ET measurements were made for two years at two previously drained and geographically close wetlands in the Everglades region of Florida. One wetland was significantly drained with 97% of its storage capacity lost. The other was a more functional wetland with 42% of storage capacity lost. Annual average ET at the significantly drained wetland was 836 mm, 34% less than the function wetland (1271 mm) and the difference was statistically significant (p = 0.001). Such differences in wetland ET in the same climatic region have not been observed. The difference in ET was mainly due to drainage driven differences in inundation and associated effects on net radiation (Rn) and local relative humidity. Two daily ET models, a regression (r2 = 0.80) and a Relevance Vector Machine (RVM) model (r2 = 0.84), were developed with the latter being more robust. These models, when used in conjunction with hydrologic models, improved ET predictions for drained wetlands. Predictions from an integrated model showed that more intensely drained wetlands at higher elevation should be targeted for restoration of downstream flows (flooding) because they have the ability to loose higher water volume through ET which increases available water storage capacity of wetlands. Daily ET models can predict changes in ET for improved evaluation of basin-scale effects of restoration programs and climate change scenarios.

  3. [Suitable investigation method of exploration and suggestions for investigating Chinese materia medica resources from wetland and artificial water of Hongze Lake region].

    Science.gov (United States)

    Liu, Rui; Yan, Hui; Duan, Jin-Ao; Liu, Xing-Jian; Ren, Quan-Jin; Li, Hui-Wei; Bao, Bei-Hua; Zhang, Zhao-Hui

    2016-08-01

    According to the technology requirements of the fourth national survey of Chinese Materia Medica resources (pilot), suitable investigation method of exploration and suggestions for investigating Chinese Materia Medica resources was proposed based on the type of wetland and artificial water of Hongze Lake region. Environment of Hongze Lake and overview of wetland, present situation of ecology and vegetation and vegetation distribution were analyzed. Establishment of survey plan, selection of sample area and sample square and confirmation of representative water area survey plan were all suggested. The present study provide references for improving Chinese materia medica resources survey around Hongze Lake, and improving the technical specifications. It also provide references for investigating Chinese Materia Medica resources survey on similar ecological environment under the condition of artificial intervention. Copyright© by the Chinese Pharmaceutical Association.

  4. Introducing a boreal wetland model within the Earth System model framework

    Science.gov (United States)

    Getzieh, R. J.; Brovkin, V.; Reick, C.; Kleinen, T.; Raddatz, T.; Raivonen, M.; Sevanto, S.

    2009-04-01

    Wetlands of the northern high latitudes with their low temperatures and waterlogged conditions are prerequisite for peat accumulation. They store at least 25% of the global soil organic carbon and constitute currently the largest natural source of methane. These boreal and subarctic peat carbon pools are sensitive to climate change since the ratio of carbon sequestration and emission is closely dependent on hydrology and temperature. Global biogeochemistry models used for simulations of CO2 dynamics in the past and future climates usually ignore changes in the peat storages. Our approach aims at the evaluation of the boreal wetland feedback to climate through the CO2 and CH4 fluxes on decadal to millennial time scales. A generic model of organic matter accumulation and decay in boreal wetlands is under development in the MPI for Meteorology in cooperation with the University of Helsinki. Our approach is to develop a wetland model which is consistent with the physical and biogeochemical components of the land surface module JSBACH as a part of the Earth System model framework ECHAM5-MPIOM-JSBACH. As prototypes, we use modelling approach by Frolking et al. (2001) for the peat dynamics and the wetland model by Wania (2007) for vegetation cover and plant productivity. An initial distribution of wetlands follows the GLWD-3 map by Lehner and Döll (2004). First results of the modelling approach will be presented. References: Frolking, S. E., N. T. Roulet, T. R. Moore, P. J. H. Richard, M. Lavoie and S. D. Muller (2001): Modeling Northern Peatland Decomposition and Peat Accumulation, Ecosystems, 4, 479-498. Lehner, B., Döll P. (2004): Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology 296 (1-4), 1-22. Wania, R. (2007): Modelling northern peatland land surface processes, vegetation dynamics and methane emissions. PhD thesis, University of Bristol, 122 pp.

  5. Simplified hydraulic model of French vertical-flow constructed wetlands.

    Science.gov (United States)

    Arias, Luis; Bertrand-Krajewski, Jean-Luc; Molle, Pascal

    2014-01-01

    Designing vertical-flow constructed wetlands (VFCWs) to treat both rain events and dry weather flow is a complex task due to the stochastic nature of rain events. Dynamic models can help to improve design, but they usually prove difficult to handle for designers. This study focuses on the development of a simplified hydraulic model of French VFCWs using an empirical infiltration coefficient--infiltration capacity parameter (ICP). The model was fitted using 60-second-step data collected on two experimental French VFCW systems and compared with Hydrus 1D software. The model revealed a season-by-season evolution of the ICP that could be explained by the mechanical role of reeds. This simplified model makes it possible to define time-course shifts in ponding time and outlet flows. As ponding time hinders oxygen renewal, thus impacting nitrification and organic matter degradation, ponding time limits can be used to fix a reliable design when treating both dry and rain events.

  6. Modelling carbon cycle in boreal wetlands with the Earth System Model ECHAM6/MPIOM

    Science.gov (United States)

    Getzieh, Robert J.; Brovkin, Victor; Kleinen, Thomas; Raivonen, Maarit; Sevanto, Sanna

    2010-05-01

    Wetlands of the northern high latitudes provide excellent conditions for peat accumulation and methanogenesis. High moisture and low O2 content in the soils lead to effective preservation of soil organic matter and methane emissions. Boreal Wetlands contain about 450 PgC and currently constitute a significant natural source of methane (CH4) even though they cover only 3% of the global land surface. While storing carbon and removing CO2 from the atmosphere, boreal wetlands have contributed to global cooling on millennial timescales. Undisturbed boreal wetlands are likely to continue functioning as a net carbon sink. On the other hand these carbon pools might be destabilised in future since they are sensitive to climate change. Given that processes of peat accumulation and decay are closely dependent on hydrology and temperature, this balance may be altered significantly in the future. As a result, northern wetlands could have a large impact on carbon cycle-climate feedback mechanisms and therefore play an important role in global carbon cycle dynamics. However global biogeochemistry models used for simulations of CO2 dynamics in past and future climates usually neglect carbon cycle in wetlands. We investigate the potential for positive or negative feedbacks to the climate system through fluxes of greenhouse gases (CO2 and CH4) with the general circulation model ECHAM6/MPIOM. A generic model of peat accumulation and decay has been developed and implemented into the land surface module JSBACH. We consider anaerobic biogeochemical processes which lead to formation of thick organic soils. Furthermore we consider specific wetland plant functional types (PFTs) in our model such as vascular plants (sedges) which impact methane transport and oxidation processes and non vascular plants (sphagnum mosses) which are promoting peat growth. As prototypes we use the modelling approaches by Frolking et al. (2001) as well as Walter & Heimann (2001) for the peat dynamics, and the

  7. Artificial intelligence model for sustain ability measurement

    International Nuclear Information System (INIS)

    Navickiene, R.; Navickas, K.

    2012-01-01

    The article analyses the main dimensions of organizational sustain ability, their possible integrations into artificial neural network. In this article authors performing analyses of organizational internal and external environments, their possible correlations with 4 components of sustain ability, and the principal determination models for sustain ability of organizations. Based on the general principles of sustainable development organizations, a artificial intelligence model for the determination of organizational sustain ability has been developed. The use of self-organizing neural networks allows the identification of the organizational sustain ability and the endeavour to explore vital, social, antropogenical and economical efficiency. The determination of the forest enterprise sustain ability is expected to help better manage the sustain ability. (Authors)

  8. Bioeconomic Modelling of Wetlands and Waterfowl in Western Canada: Accounting for Amenity Values

    NARCIS (Netherlands)

    Kooten, van G.C.; Whitey, P.; Wong, L.

    2011-01-01

    This study reexamines and updates an original bioeconomic model of optimal duck harvest and wetland retention by Hammack and Brown (1974, Waterfowl and Wetlands: Toward Bioeconomic Analysis. Washington, DC: Resources for the Future). It then extends the model to include the nonmarket (in situ) value

  9. An integrated model of soil, hydrology, and vegetation for carbon dynamics in wetland ecosystems

    Science.gov (United States)

    Yu Zhang; Changsheng Li; Carl C. Trettin; Harbin Li; Ge Sun

    2002-01-01

    Wetland ecosystems are an important component in global carbon (C) cycles and may exert a large influence on global clinlate change. Predictions of C dynamics require us to consider interactions among many critical factors of soil, hydrology, and vegetation. However, few such integrated C models exist for wetland ecosystems. In this paper, we report a simulation model...

  10. Modelling Holocene carbon accumulation and methane emissions of boreal wetlands – an Earth system model approach

    Directory of Open Access Journals (Sweden)

    R. J. Schuldt

    2013-03-01

    Full Text Available Since the Last Glacial Maximum, boreal wetlands have accumulated substantial amounts of peat, estimated at 180–621 Pg of carbon. Wetlands have significantly affected the atmospheric greenhouse gas composition in the past and will play a significant role in future changes of atmospheric CO2 and CH4 concentrations. In order to investigate those changes with an Earth system model, biogeochemical processes in boreal wetlands need to be accounted for. Thus, a model of peat accumulation and decay was developed and included in the land surface model JSBACH of the Max Planck Institute Earth System Model (MPI-ESM. Here we present the evaluation of model results from 6000 yr BP to the pre-industrial period. Over this period of time, 240 Pg of peat carbon accumulated in the model in the areas north of 40° N. Simulated peat accumulation rates agree well with those reported for boreal wetlands. The model simulates CH4 emissions of 49.3 Tg CH4 yr−1 for 6000 yr BP and 51.5 Tg CH4 yr−1 for pre-industrial times. This is within the range of estimates in the literature, which range from 32 to 112 Tg CH4 yr−1 for boreal wetlands. The modelled methane emission for the West Siberian Lowlands and Hudson Bay Lowlands agree well with observations. The rising trend of methane emissions over the last 6000 yr is in agreement with measurements of Antarctic and Greenland ice cores.

  11. Removal of Cu, Zn, Pb, and Cr from Yangtze Estuary Using the Phragmites australis Artificial Floating Wetlands

    Directory of Open Access Journals (Sweden)

    Xiaofeng Huang

    2017-01-01

    Full Text Available Contamination of heavy metals would threaten the water and soil resources; phytoremediation can be potentially used to remediate metal contaminated sites. We constructed the Phragmites australis artificial floating wetlands outside the Qingcaosha Reservoir in the Yangtze Estuary. Water characteristic variables were measured in situ by using YSI Professional Pro Meter. Four heavy metals (copper, zinc, lead, and chromium in both water and plant tissues were determined. Four heavy metals in estuary water were as follows: 0.03 mg/Kg, 0.016 mg/Kg, 0.0015 mg/Kg, and 0.004 mg/Kg. These heavy metals were largely retained in the belowground tissues of P. australis. The bioaccumulation (BAF and translation factor (TF value of four heavy metals were affected by the salinity, temperature, and dissolved oxygen. The highest BAF of each metal calculated was as follows: Cr (0.091 in winter > Cu (0.054 in autumn > Pb (0.016 in summer > Zn (0.011 in summer. Highest root-rhizome TF values were recorded for four metals: 6.450 for Cu in autumn, 2.895 for Zn in summer, 7.031 for Pb in autumn, and 2.012 for Cr in autumn. This indicates that the P. australis AFW has potential to be used to protect the water of Qingcaosha Reservoir from heavy metal contamination.

  12. Modelling fuel cell performance using artificial intelligence

    Science.gov (United States)

    Ogaji, S. O. T.; Singh, R.; Pilidis, P.; Diacakis, M.

    Over the last few years, fuel cell technology has been increasing promisingly its share in the generation of stationary power. Numerous pilot projects are operating worldwide, continuously increasing the amount of operating hours either as stand-alone devices or as part of gas turbine combined cycles. An essential tool for the adequate and dynamic analysis of such systems is a software model that enables the user to assess a large number of alternative options in the least possible time. On the other hand, the sphere of application of artificial neural networks has widened covering such endeavours of life such as medicine, finance and unsurprisingly engineering (diagnostics of faults in machines). Artificial neural networks have been described as diagrammatic representation of a mathematical equation that receives values (inputs) and gives out results (outputs). Artificial neural networks systems have the capacity to recognise and associate patterns and because of their inherent design features, they can be applied to linear and non-linear problem domains. In this paper, the performance of the fuel cell is modelled using artificial neural networks. The inputs to the network are variables that are critical to the performance of the fuel cell while the outputs are the result of changes in any one or all of the fuel cell design variables, on its performance. Critical parameters for the cell include the geometrical configuration as well as the operating conditions. For the neural network, various network design parameters such as the network size, training algorithm, activation functions and their causes on the effectiveness of the performance modelling are discussed. Results from the analysis as well as the limitations of the approach are presented and discussed.

  13. Modelling fuel cell performance using artificial intelligence

    Energy Technology Data Exchange (ETDEWEB)

    Ogaji, S.O.T.; Singh, R.; Pilidis, P.; Diacakis, M. [Power Propulsion and Aerospace Engineering Department, Centre for Diagnostics and Life Cycle Costs, Cranfield University (United Kingdom)

    2006-03-09

    Over the last few years, fuel cell technology has been increasing promisingly its share in the generation of stationary power. Numerous pilot projects are operating worldwide, continuously increasing the amount of operating hours either as stand-alone devices or as part of gas turbine combined cycles. An essential tool for the adequate and dynamic analysis of such systems is a software model that enables the user to assess a large number of alternative options in the least possible time. On the other hand, the sphere of application of artificial neural networks has widened covering such endeavours of life such as medicine, finance and unsurprisingly engineering (diagnostics of faults in machines). Artificial neural networks have been described as diagrammatic representation of a mathematical equation that receives values (inputs) and gives out results (outputs). Artificial neural networks systems have the capacity to recognise and associate patterns and because of their inherent design features, they can be applied to linear and non-linear problem domains. In this paper, the performance of the fuel cell is modelled using artificial neural networks. The inputs to the network are variables that are critical to the performance of the fuel cell while the outputs are the result of changes in any one or all of the fuel cell design variables, on its performance. Critical parameters for the cell include the geometrical configuration as well as the operating conditions. For the neural network, various network design parameters such as the network size, training algorithm, activation functions and their causes on the effectiveness of the performance modelling are discussed. Results from the analysis as well as the limitations of the approach are presented and discussed. (author)

  14. Modeling biomass competition and invasion in a schematic wetland

    Science.gov (United States)

    Ursino, N.

    2010-08-01

    Plants growing along hydrologic gradients adjust their biomass allocation and distribution in response to interspecific competition. Furthermore, susceptibility of a community to invasion is to some extent mediated by differences in growth habit, including root architecture and canopy hight. With reference to the study of a schematic wetland, the aim of this paper is (1) to test, via numerical modeling, the capacity of native plants to counteract an alien dominant species and cause eco-hydrological shifts of the ecosystem by changing their growth habit (e.g. allocating biomass below ground and by so doing changing the evapotranspiration locally) and (2) to test the impact on biodiversity of management practices that alter nutrient supply. The results demonstrated that unique combinations of vegetation types characterized by different growth habits may lead to different vegetation patterns under the same hydrologic forcing, and additionally, the vegetation patterns may change in response to major hydrological shifts, which could be related to diverse wetland management and restoration practices.

  15. Artificial Intelligence Software Engineering (AISE) model

    Science.gov (United States)

    Kiss, Peter A.

    1990-01-01

    The American Institute of Aeronautics and Astronautics has initiated a committee on standards for Artificial Intelligence. Presented are the initial efforts of one of the working groups of that committee. A candidate model is presented for the development life cycle of knowledge based systems (KBSs). The intent is for the model to be used by the aerospace community and eventually be evolved into a standard. The model is rooted in the evolutionary model, borrows from the spiral model, and is embedded in the standard Waterfall model for software development. Its intent is to satisfy the development of both stand-alone and embedded KBSs. The phases of the life cycle are shown and detailed as are the review points that constitute the key milestones throughout the development process. The applicability and strengths of the model are discussed along with areas needing further development and refinement by the aerospace community.

  16. A network model framework for prioritizing wetland conservation in the Great Plains

    Science.gov (United States)

    Albanese, Gene; Haukos, David A.

    2017-01-01

    ContextPlaya wetlands are the primary habitat for numerous wetland-dependent species in the Southern Great Plains of North America. Plant and wildlife populations that inhabit these wetlands are reciprocally linked through the dispersal of individuals, propagules and ultimately genes among local populations.ObjectiveTo develop and implement a framework using network models for conceptualizing, representing and analyzing potential biological flows among 48,981 spatially discrete playa wetlands in the Southern Great Plains.MethodsWe examined changes in connectivity patterns and assessed the relative importance of wetlands to maintaining these patterns by targeting wetlands for removal based on network centrality metrics weighted by estimates of habitat quality and probability of inundation.ResultsWe identified several distinct, broad-scale sub networks and phase transitions among playa wetlands in the Southern Plains. In particular, for organisms that can disperse >2 km a dense and expansive wetland sub network emerges in the Southern High Plains. This network was characterized by localized, densely connected wetland clusters at link distances (h) >2 km but <5 km and was most sensitive to changes in wetland availability (p) and configuration when h = 4 km, and p = 0.2–0.4. It transitioned to a single, large connected wetland system at broader spatial scales even when the proportion of inundated wetland was relatively low (p = 0.2).ConclusionsOur findings suggest that redundancy in the potential for broad and fine-scale movements insulates this system from damage and facilitates system-wide connectivity among populations with different dispersal capacities.

  17. Treatment Wetlands

    OpenAIRE

    Dotro, Gabriela; Langergraber, Günter; Molle, Pascal; Nivala, Jaime; Puigagut, Jaume; Stein, Otto; Von Sperling, Marcos

    2017-01-01

    Overview of Treatment Wetlands; Fundamentals of Treatment Wetlands; Horizontal Flow Wetlands; Vertical Flow Wetlands; French Vertical Flow Wetlands; Intensified and Modified Wetlands; Free Water Surface Wetlands; Other Applications; Additional Aspects.

  18. Modeling the effects of tile drain placement on the hydrologic function of farmed prairie wetlands

    Science.gov (United States)

    Werner, Brett; Tracy, John; Johnson, W. Carter; Voldseth, Richard A.; Guntenspergen, Glenn R.; Millett, Bruce

    2016-01-01

    The early 2000s saw large increases in agricultural tile drainage in the eastern Dakotas of North America. Agricultural practices that drain wetlands directly are sometimes limited by wetland protection programs. Little is known about the impacts of tile drainage beyond the delineated boundaries of wetlands in upland catchments that may be in agricultural production. A series of experiments were conducted using the well-published model WETLANDSCAPE that revealed the potential for wetlands to have significantly shortened surface water inundation periods and lower mean depths when tile is placed in certain locations beyond the wetland boundary. Under the soil conditions found in agricultural areas of South Dakota in North America, wetland hydroperiod was found to be more sensitive to the depth that drain tile is installed relative to the bottom of the wetland basin than to distance-based setbacks. Because tile drainage can change the hydrologic conditions of wetlands, even when deployed in upland catchments, tile drainage plans should be evaluated more closely for the potential impacts they might have on the ecological services that these wetlands currently provide. Future research should investigate further how drainage impacts are affected by climate variability and change.

  19. Evaluation of methane emissions from West Siberian wetlands based on inverse modeling

    Energy Technology Data Exchange (ETDEWEB)

    Kim, H-S; Inoue, G [Research Institute for Humanity and Nature, 457-4 Motoyama, Kamigamo, Kita-ku, Kyoto 603-8047 (Japan); Maksyutov, S; Machida, T [National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506 (Japan); Glagolev, M V [Lomonosov Moscow State University, GSP-1, Leninskie Gory, Moscow 119991 (Russian Federation); Patra, P K [Research Institute for Global Change/JAMSTEC, 3173-25 Showa-cho, Kanazawa-ku, Yokohama, Kanagawa 236-0001 (Japan); Sudo, K, E-mail: heonsook.kim@gmail.com [Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601 (Japan)

    2011-07-15

    West Siberia contains the largest extent of wetlands in the world, including large peat deposits; the wetland area is equivalent to 27% of the total area of West Siberia. This study used inverse modeling to refine emissions estimates for West Siberia using atmospheric CH{sub 4} observations and two wetland CH{sub 4} emissions inventories: (1) the global wetland emissions dataset of the NASA Goddard Institute for Space Studies (the GISS inventory), which includes emission seasons and emission rates based on climatology of monthly surface air temperature and precipitation, and (2) the West Siberian wetland emissions data (the Bc7 inventory), based on in situ flux measurements and a detailed wetland classification. The two inversions using the GISS and Bc7 inventories estimated annual mean flux from West Siberian wetlands to be 2.9 {+-} 1.7 and 3.0 {+-} 1.4 Tg yr{sup -1}, respectively, which are lower than the 6.3 Tg yr{sup -1} predicted in the GISS inventory, but similar to those of the Bc7 inventory (3.2 Tg yr{sup -1}). The well-constrained monthly fluxes and a comparison between the predicted CH{sub 4} concentrations in the two inversions suggest that the Bc7 inventory predicts the seasonal cycle of West Siberian wetland CH{sub 4} emissions more reasonably, indicating that the GISS inventory predicts more emissions from wetlands in northern and middle taiga.

  20. An application of artificial intelligence for rainfall–runoff modeling

    Indian Academy of Sciences (India)

    This study proposes an application of two techniques of artificial intelligence (AI) ... (2006) applied rainfall–runoff modeling using ANN ... in artificial intelligence, engineering and science .... usually be estimated from a sample of observations.

  1. Flood routing modelling with Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    R. Peters

    2006-01-01

    Full Text Available For the modelling of the flood routing in the lower reaches of the Freiberger Mulde river and its tributaries the one-dimensional hydrodynamic modelling system HEC-RAS has been applied. Furthermore, this model was used to generate a database to train multilayer feedforward networks. To guarantee numerical stability for the hydrodynamic modelling of some 60 km of streamcourse an adequate resolution in space requires very small calculation time steps, which are some two orders of magnitude smaller than the input data resolution. This leads to quite high computation requirements seriously restricting the application – especially when dealing with real time operations such as online flood forecasting. In order to solve this problem we tested the application of Artificial Neural Networks (ANN. First studies show the ability of adequately trained multilayer feedforward networks (MLFN to reproduce the model performance.

  2. Modelling methane emissions from natural wetlands by development and application of the TRIPLEX-GHG model

    Science.gov (United States)

    Zhu, Qing; Liu, Jinxun; Peng, C.; Chen, H.; Fang, X.; Jiang, H.; Yang, G.; Zhu, D.; Wang, W.; Zhou, X.

    2014-01-01

    A new process-based model TRIPLEX-GHG was developed based on the Integrated Biosphere Simulator (IBIS), coupled with a new methane (CH4) biogeochemistry module (incorporating CH4 production, oxidation, and transportation processes) and a water table module to investigate CH4 emission processes and dynamics that occur in natural wetlands. Sensitivity analysis indicates that the most sensitive parameters to evaluate CH4 emission processes from wetlands are r (defined as the CH4 to CO2 release ratio) and Q10 in the CH4 production process. These two parameters were subsequently calibrated to data obtained from 19 sites collected from approximately 35 studies across different wetlands globally. Being heterogeneously spatially distributed, r ranged from 0.1 to 0.7 with a mean value of 0.23, and the Q10 for CH4 production ranged from 1.6 to 4.5 with a mean value of 2.48. The model performed well when simulating magnitude and capturing temporal patterns in CH4 emissions from natural wetlands. Results suggest that the model is able to be applied to different wetlands under varying conditions and is also applicable for global-scale simulations.

  3. Mathematical problems in modeling artificial heart

    Directory of Open Access Journals (Sweden)

    Ahmed N. U.

    1995-01-01

    Full Text Available In this paper we discuss some problems arising in mathematical modeling of artificial hearts. The hydrodynamics of blood flow in an artificial heart chamber is governed by the Navier-Stokes equation, coupled with an equation of hyperbolic type subject to moving boundary conditions. The flow is induced by the motion of a diaphragm (membrane inside the heart chamber attached to a part of the boundary and driven by a compressor (pusher plate. On one side of the diaphragm is the blood and on the other side is the compressor fluid. For a complete mathematical model it is necessary to write the equation of motion of the diaphragm and all the dynamic couplings that exist between its position, velocity and the blood flow in the heart chamber. This gives rise to a system of coupled nonlinear partial differential equations; the Navier-Stokes equation being of parabolic type and the equation for the membrane being of hyperbolic type. The system is completed by introducing all the necessary static and dynamic boundary conditions. The ultimate objective is to control the flow pattern so as to minimize hemolysis (damage to red blood cells by optimal choice of geometry, and by optimal control of the membrane for a given geometry. The other clinical problems, such as compatibility of the material used in the construction of the heart chamber, and the membrane, are not considered in this paper. Also the dynamics of the valve is not considered here, though it is also an important element in the overall design of an artificial heart. We hope to model the valve dynamics in later paper.

  4. Pneumatic Artificial Muscle Actuation and Modeling

    Science.gov (United States)

    Leephakpreeda, Thananchai; Wickramatunge, Kanchana C.

    2009-10-01

    A Pneumatic Artificial Muscle (PAM) yields a natural muscle-like actuator with a high force to weight ratio, a soft and flexible structure, and adaptable compliance for a humanoid robot, rehabilitation and prosthetic appliances to the disabled, etc. To obtain optimum design and usage, the mechanical behavior of the PAM need to be understood. In this study, observations of experimental results reveal an empirical model for relations of physical variables, contraction and air pressure within the PAM, as compared to mechanical characteristics, such as stiffness or/and pulling forces of the PAM available now in market.

  5. An entropy model for artificial grammar learning

    Directory of Open Access Journals (Sweden)

    Emmanuel Pothos

    2010-06-01

    Full Text Available A model is proposed to characterize the type of knowledge acquired in Artificial Grammar Learning (AGL. In particular, Shannon entropy is employed to compute the complexity of different test items in an AGL task, relative to the training items. According to this model, the more predictable a test item is from the training items, the more likely it is that this item should be selected as compatible with the training items. The predictions of the entropy model are explored in relation to the results from several previous AGL datasets and compared to other AGL measures. This particular approach in AGL resonates well with similar models in categorization and reasoning which also postulate that cognitive processing is geared towards the reduction of entropy.

  6. Modeling Vertical Flow Treatment Wetland Hydraulics to Optimize Treatment Efficiency

    Science.gov (United States)

    2011-03-24

    be forced to flow in a 90 serpentine manner back and forth as it moves upward through the wetland (think waiting in line at Disneyland ). This...Flow Treatment Wetland Hydraulics to Optimize Treatment Efficiency 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR

  7. Evaluation and comparison of models and modelling tools simulating nitrogen processes in treatment wetlands

    DEFF Research Database (Denmark)

    Edelfeldt, Stina; Fritzson, Peter

    2008-01-01

    with Modelica 2.1 (Wiley-IEEE Press, USA, 2004).] and an associated tool. The differences and similarities between the MathModelica Model Editor and three other ecological modelling tools have also been evaluated. The results show that the models can well be modelled and simulated in the MathModelica Model...... Editor, and that nitrogen decrease in a constructed treatment wetland should be described and simulated using the Nitrification/Denitrification model as this model has the highest overall quality score and provides a more variable environment.......In this paper, two ecological models of nitrogen processes in treatment wetlands have been evaluated and compared. These models were implemented, simulated, and visualized using the Modelica modelling and simulation language [P. Fritzson, Principles of Object-Oriented Modelling and Simulation...

  8. Estimating the effects of wetland conservation practices in croplands: Approaches for modeling in CEAP–Cropland Assessment

    Science.gov (United States)

    De Steven, Diane; Mushet, David

    2018-01-01

    Quantifying the current and potential benefits of conservation practices can be a valuable tool for encouraging greater practice adoption on agricultural lands. A goal of the CEAP-Cropland Assessment is to estimate the environmental effects of conservation practices that reduce losses (exports) of soil, nutrients, and pesticides from farmlands to streams and rivers. The assessment approach combines empirical data on reported cropland practices with simulation modeling that compares field-level exports for scenarios “with practices” and “without practices.” Conserved, restored, and created wetlands collectively represent conservation practices that can influence sediment and nutrient exports from croplands. However, modeling the role of wetlands within croplands presents some challenges, including the potential for negative impacts of sediment and nutrient inputs on wetland functions. This Science Note outlines some preliminary solutions for incorporating wetlands and wetland practices into the CEAP-Cropland modeling framework. First, modeling the effects of wetland practices requires identifying wetland hydrogeomorphic type and accounting for the condition of both the wetland and an adjacent upland zone. Second, modeling is facilitated by classifying wetland-related practices into two functional categories (wetland and upland buffer). Third, simulating practice effects requires alternative field configurations to account for hydrological differences among wetland types. These ideas are illustrated for two contrasting wetland types (riparian and depressional).

  9. Artificial life models in lung CTS

    International Nuclear Information System (INIS)

    Sorin, Cheran

    2006-01-01

    A new method for the analysis of 3D medical images is introduced. The algorithm is based on Biological Models of ants known as Artificial Life models. Test images (lung Computed Tomographies) undergo a 3D region growing procedure for the identification of the ribs cage. Active Contour Models (snakes) are used in order to build a confined area where ants are deployed. The ant-based approach, in which steps are allowed in any direction with different probabilities, allows a kind of tunneling effect for the successful identification of small 3D structures that are not clearly connected to the rest of the tree. The best approach is based on a gradient rule for the release of pheromone. A possible application, as part of a Computer Assisted Detection system for the identification of lung nodules, is the removal of the bronchial and vascular tree from lung CTs thus reducing the number of false positives a Nodule Hunter might report. (Full Text)

  10. Wetland Hydrology

    Science.gov (United States)

    This chapter discusses the state of the science in wetland hydrology by touching upon the major hydraulic and hydrologic processes in these complex ecosystems, their measurement/estimation techniques, and modeling methods. It starts with the definition of wetlands, their benefit...

  11. Modeling the climatic and subsurface stratigraphy controls on the hydrology of a Carolina Bay wetland in South Carolina, USA

    Science.gov (United States)

    Ge Sun; Timothy J. Callahan; Jennifer E. Pyzoha; Carl C. Trettin

    2006-01-01

    Restoring depressional wetlands or geographically isolated wetlands such as cypress swamps and Carolina bays on the Atlantic Coastal Plains requires a clear understanding of the hydrologic processes and water balances. The objectives of this paper are to (1) test a distributed forest hydrology model, FLATWOODS, for a Carolina bay wetland system using seven years of...

  12. Modeling the climatic and subsurface stratigraphy controls on the hydrology of a Carolina bay wetland in South Carolina, USA

    Science.gov (United States)

    Ge Sun; Timothy J. Callahan; Jennifer E. Pyzoha; Carl C. Trettin

    2006-01-01

    Restoring depressional wetlands or geographically isolated wetlands such as cypress swamps and Carolina bays on the Atlantic Coastal Plains requires a clear understanding of the hydrologic processes and water balances. The objectives of this paper are to (1) test a distributed forest hydrology model, FLATWOODS, for a Carolina bay wetland system using seven years of...

  13. Estuarine Sediment Deposition during Wetland Restoration: A GIS and Remote Sensing Modeling Approach

    Science.gov (United States)

    Newcomer, Michelle; Kuss, Amber; Kentron, Tyler; Remar, Alex; Choksi, Vivek; Skiles, J. W.

    2011-01-01

    Restoration of the industrial salt flats in the San Francisco Bay, California is an ongoing wetland rehabilitation project. Remote sensing maps of suspended sediment concentration, and other GIS predictor variables were used to model sediment deposition within these recently restored ponds. Suspended sediment concentrations were calibrated to reflectance values from Landsat TM 5 and ASTER using three statistical techniques -- linear regression, multivariate regression, and an Artificial Neural Network (ANN), to map suspended sediment concentrations. Multivariate and ANN regressions using ASTER proved to be the most accurate methods, yielding r2 values of 0.88 and 0.87, respectively. Predictor variables such as sediment grain size and tidal frequency were used in the Marsh Sedimentation (MARSED) model for predicting deposition rates for three years. MARSED results for a fully restored pond show a root mean square deviation (RMSD) of 66.8 mm (<1) between modeled and field observations. This model was further applied to a pond breached in November 2010 and indicated that the recently breached pond will reach equilibrium levels after 60 months of tidal inundation.

  14. Using decomposition kinetics to model the removal of mine water pollutants in constructed wetlands

    Energy Technology Data Exchange (ETDEWEB)

    Tarutis, W J; Unz, R F [Pennsylvania State University, University Park, PA (United States)

    1994-01-01

    Although numerous mathematical models have been used to describe decomposition, few, if any, have been used to model the removal of pollutants in constructed wetlands. A steady state method based on decomposition kinetics and reaction stoichiometry has been developed which simulates the removal of ferrous iron entering wetlands constructed for mine drainage treatment. Input variables for the model include organic matter concentration, reaction rate coefficient, porosity and dry density, and hydraulic detection time. Application of the model assumes complete anaerobic conditions within the entire substrate profile, constant temperature, no additional organic matter input, and subsurface flow only. For these ideal conditions, model simulations indicate that wetlands constructed with readily decomposable substrates rich in organic carbon are initially capable of removing far greater amounts of iron than wetlands built with less biodegradable substrates. However, after three to five years of operation this difference becomes negligible. For acceptable long-term treatment performance, therefore, periodic additions of decomposable organic matter will be required.

  15. Characterizing the Surface Connectivity of Depressional Wetlands: Linking Remote Sensing and Hydrologic Modeling Approaches

    Science.gov (United States)

    Christensen, J.; Evenson, G. R.; Vanderhoof, M.; Wu, Q.; Golden, H. E.; Lane, C.

    2017-12-01

    Surface connectivity of wetlands in the 700,000 km2 Prairie Pothole Region of North America (PPR) can occur through fill-spill and fill-merge mechanisms, with some wetlands eventually spilling into stream/river systems. These wetland-to-wetland and wetland-to-stream connections vary both spatially and temporally in PPR watersheds and are important to understanding hydrologic and biogeochemical processes in the landscape. To explore how to best characterize spatial and temporal variability in aquatic connectivity, we compared three approaches, 1) hydrological modeling alone, 2) remotely-sensed data alone, and 3) integrating remotely-sensed data into a hydrological model. These approaches were tested in the Pipestem Creek Watershed, North Dakota across a drought to deluge cycle (1990-2011). A Soil and Water Assessment Tool (SWAT) model was modified to include the water storage capacity of individual non-floodplain wetlands identified in the National Wetland Inventory (NWI) dataset. The SWAT-NWI model simulated the water balance and storage of each wetland and the temporal variability of their hydrologic connections between wetlands during the 21-year study period. However, SWAT-NWI only accounted for fill-spill, and did not allow for the expansion and merging of wetlands situated within larger depressions. Alternatively, we assessed the occurrence of fill-merge mechanisms using inundation maps derived from Landsat images on 19 cloud-free days during the 21 years. We found fill-merge mechanisms to be prevalent across the Pipestem watershed during times of deluge. The SWAT-NWI model was then modified to use LiDAR-derived depressions that account for the potential maximum depression extent, including the merging of smaller wetlands. The inundation maps were used to evaluate the ability of the SWAT-depression model to simulate fill-merge dynamics in addition to fill-spill dynamics throughout the study watershed. Ultimately, using remote sensing to inform and validate

  16. Benefits of using a Social-Ecological Systems Approach to Conceptualize and Model Wetlands Restoration

    Science.gov (United States)

    Using a social-ecological systems (SES) perspective to examine wetland restoration helps decision-makers recognize interdependencies and relations between ecological and social components of coupled systems. Conceptual models are an invaluable tool to capture, visualize, and orga...

  17. Delineating wetland catchments and modeling hydrologic connectivity using LiDAR data and aerial imagery

    OpenAIRE

    Wu, Qiusheng; Lane, Charles R.

    2017-01-01

    In traditional watershed delineation and topographic modeling, surface depressions are generally treated as spurious features and simply removed from a digital elevation model (DEM) to enforce flow continuity of water across the topographic surface to the watershed outlets. In reality, however, many depressions in the DEM are actual wetland landscape features that are seldom fully filled with water. For instance, wetland depressions in the Prairie Pothole Region (PPR) are seasonally to perman...

  18. Challenges and Conundrums in Modeling Global Methane Emissions from Wetlands: An Empiricist's Viewpoint

    Science.gov (United States)

    Bridgham, S. D.

    2015-12-01

    Wetlands emit a third to half of the global CH4 flux and have the largest uncertainty of any emission source. Moreover, wetlands have provided an important radiative feedback to climate in the geologic and recent past. A number of largescale wetland CH4 models have been developed recently, but intermodel comparisons show wide discrepancies in their predictions. I present an empiricist's overview of the current limitations and challenges of more accurately modeling wetland CH4 emissions. One of the largest limitations is simply the poor knowledge of wetland area, with estimated global values varying by a more than a factor of three. The areas of seasonal and tropical wetlands are particularly poorly constrained. There are also few wetlands with complete, multi-year datasets for all of the input variables for many models, and this lack of data is particularly alarming in tropical wetlands given that they are arguably the single largest natural or anthropogenic global CH4 source. Almost all largescale CH4 models have little biogeochemical mechanistic detail and treat anaerobic carbon cycling in a highly simplified manner. The CH4:CO2 ratio in anaerobic carbon mineralization is a central parameter in many models, but is at most set at a few values with no mechanistic underpinning. However, empirical data show that this ratio varies by five orders of magnitude in different wetlands, and tropical wetlands appear to be particularly methanogenic, all for reasons that are very poorly understood. The predominance of the acetoclastic pathway of methanogenesis appears to be related to total CH4 production, but different methanogenesis pathways are generally not incorporated into models. Other important anaerobic processes such as humic substances acting as terminal electron acceptors, fermentation, homoacetogenesis, and anaerobic CH4 oxidation are also not included in most models despite evidence of their importance in empirical studies. Moreover, there has been an explosion

  19. Integrated hydrological modelling of a managed coastal Mediterranean wetland (Rhone delta, France: initial calibration

    Directory of Open Access Journals (Sweden)

    P. Chauvelon

    2003-01-01

    Full Text Available This paper presents a model of a heavily managed coastal Mediterranean wetland. The hydrosystem studied , called ``Ile de Camargue', is the central part of the Rhone river delta. It comprises flat agricultural drainage basins, marshes, and shallow brackish lagoons whose connection to the sea is managed. This hydrosystem is subject to strong natural hydrological variability due to the combination of a Mediterranean climate and the artificial hydrological regime imposed by flooded rice cultivation. To quantify the hydrological balance at different spatial and temporal scales, a simplified model is developed — including the basin and the lagoons — using a time step that enables the temporal dynamic to be reproduced that is adapted to data availability. This modelling task takes into account the functioning of the natural and anthropogenic components of the hydrosystem. A conceptual approach is used for modelling drainage from the catchment, using a GIS to estimate water input for rice irrigation. The lagoon system is modelled using a two-dimensional finite element hydrodynamic model. Simulated results from the hydrodynamic model run under various hydro-climatic forcing conditions (water level, wind speed and direction, sea connection are used to calculate hydraulic exchanges between lagoon sub units considered as boxes. Finally, the HIC ('Hydrologie de l’Ile de Camargue' conceptual model is applied to simulate the water inputs and exchanges between the different units, together with the salt balance in the hydrosystem during a calibration period. Keywords: water management,conceptual hydrological model, hydrodynamic model, box model, GIS, Rhone delta, Camargue.

  20. Design-a-wetland: a tool for generating and assessing constructed wetland designs for wastewater treatment

    International Nuclear Information System (INIS)

    Casaril, Carolina J.

    2007-01-01

    Full text: Full text: The hydrological cycle is a key cycle affected by current and predicted climate change. Wetlands are one of the key ecosystems within the hydrological cycle and could contribute significantly in facing the challenges of climate change, such as water shortage. The impact of wetlands on greenhouse gas emissions is much debated and, conversely, the impact of climate change on wetlands also raises many questions. There have been many attempts to harness and integrate the natural capacities of wetlands into constructed systems. These systems are especially designed for multiple purposes. They can be used for wastewater treatment and reuse, and have the potential to increase sustainability by changing land and water use practices. This project generates a 'Design-A-Wetland' prototype model, designed to facilitate decision-making in the creation of constructed wetlands. Constructed wetlands are specifically tailored to their end use; water treatment fish and fowl habitat, flood buffer zones, or sequestration of greenhouse gases. This project attempts to answer the following questions: Can a single integrated decision model be created for the design and assessment of artificial wetlands, provided either entry or exit standards are known and specified?; Can the elements of a system of interfacing the model with public consultation be specified?; The project identifies model schematics and lays the groundwork for modelling suited to the wide variety of inputs required for decision making

  1. Biogeochemial modeling of biodegradation and stable isotope fractionation of DCE in a small-scale wetland

    Science.gov (United States)

    Alvarez-Zaldívar, Pablo; Imfeld, Gwenaël; Maier, Uli; Centler, Florian; Thullner, Martin

    2013-04-01

    In recent years, the use of (constructed) wetlands has gained significant attention for the in situ remediation of groundwater contaminated with (chlorinated) organic hydrocarbons. Although many sophisticated experimental methods exist for the assessment of contaminant removal in such wetlands the understanding how changes in wetland hydrochemistry affect the removal processes is still limited. This knowledge gap might be reduced by the use of biogeochemical reactive transport models. This study presents the reactive transport simulation of a small-scale constructed wetland treated with groundwater containing cis-1,2-dichloroethene (cDCE). Simulated processes consider different cDCE biodegradation pathways and the associated carbon isotope fractionation, a set of further (bio)geochemical processes as well as the activity of the plant roots. Spatio-temporal hydrochemical and isotope data from a long-term constructed wetland experiment [1] are used to constrain the model. Simulation results for the initial oxic phase of the wetland experiment indicate carbon isotope enrichment factors typical for cometabolic DCE oxidation, which suggests that aerobic treatment of cDCE is not an optimal remediation strategy. For the later anoxic phase of the experiment model derived enrichment factors indicate reductive dechlorination pathways. This degradation is promoted at all wetland depths by a sufficient availability of electron donor and carbon sources from root exudates, which makes the anoxic treatment of groundwater in such wetlands an effective remediation strategy. In combination with the previous experimental data results from this study suggest that constructed wetlands are viable remediation means for the treatment of cDCE contaminated groundwater. Reactive transport models can improve the understanding of the factors controlling chlorinated ethenes removal, and the used model approach would also allow for an optimization of the wetland operation needed for a complete

  2. A data-model integration approach toward improved understanding on wetland functions and hydrological benefits at the catchment scale

    Science.gov (United States)

    Yeo, I. Y.; Lang, M.; Lee, S.; Huang, C.; Jin, H.; McCarty, G.; Sadeghi, A.

    2017-12-01

    The wetland ecosystem plays crucial roles in improving hydrological function and ecological integrity for the downstream water and the surrounding landscape. However, changing behaviours and functioning of wetland ecosystems are poorly understood and extremely difficult to characterize. Improved understanding on hydrological behaviours of wetlands, considering their interaction with surrounding landscapes and impacts on downstream waters, is an essential first step toward closing the knowledge gap. We present an integrated wetland-catchment modelling study that capitalizes on recently developed inundation maps and other geospatial data. The aim of the data-model integration is to improve spatial prediction of wetland inundation and evaluate cumulative hydrological benefits at the catchment scale. In this paper, we highlight problems arising from data preparation, parameterization, and process representation in simulating wetlands within a distributed catchment model, and report the recent progress on mapping of wetland dynamics (i.e., inundation) using multiple remotely sensed data. We demonstrate the value of spatially explicit inundation information to develop site-specific wetland parameters and to evaluate model prediction at multi-spatial and temporal scales. This spatial data-model integrated framework is tested using Soil and Water Assessment Tool (SWAT) with improved wetland extension, and applied for an agricultural watershed in the Mid-Atlantic Coastal Plain, USA. This study illustrates necessity of spatially distributed information and a data integrated modelling approach to predict inundation of wetlands and hydrologic function at the local landscape scale, where monitoring and conservation decision making take place.

  3. Model estimation of land-use effects on water levels of northern Prairie wetlands

    Science.gov (United States)

    Voldseth, R.A.; Johnson, W.C.; Gilmanov, T.; Guntenspergen, G.R.; Millett, B.V.

    2007-01-01

    Wetlands of the Prairie Pothole Region exist in a matrix of grassland dominated by intensive pastoral and cultivation agriculture. Recent conservation management has emphasized the conversion of cultivated farmland and degraded pastures to intact grassland to improve upland nesting habitat. The consequences of changes in land-use cover that alter watershed processes have not been evaluated relative to their effect on the water budgets and vegetation dynamics of associated wetlands. We simulated the effect of upland agricultural practices on the water budget and vegetation of a semipermanent prairie wetland by modifying a previously published mathematical model (WETSIM). Watershed cover/land-use practices were categorized as unmanaged grassland (native grass, smooth brome), managed grassland (moderately heavily grazed, prescribed burned), cultivated crops (row crop, small grain), and alfalfa hayland. Model simulations showed that differing rates of evapotranspiration and runoff associated with different upland plant-cover categories in the surrounding catchment produced differences in wetland water budgets and linked ecological dynamics. Wetland water levels were highest and vegetation the most dynamic under the managed-grassland simulations, while water levels were the lowest and vegetation the least dynamic under the unmanaged-grassland simulations. The modeling results suggest that unmanaged grassland, often planted for waterfowl nesting, may produce the least favorable wetland conditions for birds, especially in drier regions of the Prairie Pothole Region. These results stand as hypotheses that urgently need to be verified with empirical data.

  4. User-Friendly Predictive Modeling of Greenhouse Gas (GHG) Fluxes and Carbon Storage in Tidal Wetlands

    Science.gov (United States)

    Ishtiaq, K. S.; Abdul-Aziz, O. I.

    2015-12-01

    We developed user-friendly empirical models to predict instantaneous fluxes of CO2 and CH4 from coastal wetlands based on a small set of dominant hydro-climatic and environmental drivers (e.g., photosynthetically active radiation, soil temperature, water depth, and soil salinity). The dominant predictor variables were systematically identified by applying a robust data-analytics framework on a wide range of possible environmental variables driving wetland greenhouse gas (GHG) fluxes. The method comprised of a multi-layered data-analytics framework, including Pearson correlation analysis, explanatory principal component and factor analyses, and partial least squares regression modeling. The identified dominant predictors were finally utilized to develop power-law based non-linear regression models to predict CO2 and CH4 fluxes under different climatic, land use (nitrogen gradient), tidal hydrology and salinity conditions. Four different tidal wetlands of Waquoit Bay, MA were considered as the case study sites to identify the dominant drivers and evaluate model performance. The study sites were dominated by native Spartina Alterniflora and characterized by frequent flooding and high saline conditions. The model estimated the potential net ecosystem carbon balance (NECB) both in gC/m2 and metric tonC/hectare by up-scaling the instantaneous predicted fluxes to the growing season and accounting for the lateral C flux exchanges between the wetlands and estuary. The entire model was presented in a single Excel spreadsheet as a user-friendly ecological engineering tool. The model can aid the development of appropriate GHG offset protocols for setting monitoring plans for tidal wetland restoration and maintenance projects. The model can also be used to estimate wetland GHG fluxes and potential carbon storage under various IPCC climate change and sea level rise scenarios; facilitating an appropriate management of carbon stocks in tidal wetlands and their incorporation into a

  5. Comparison of Four Nitrate Removal Kinetic Models in Two Distinct Wetland Restoration Mesocosm Systems

    Directory of Open Access Journals (Sweden)

    Tiffany L. Messer

    2017-07-01

    Full Text Available The objective of the study was to determine the kinetic model that best fit observed nitrate removal rates at the mesocosm scale in order to determine ideal loading rates for two future wetland restorations slated to receive pulse flow agricultural drainage water. Four nitrate removal models were investigated: zero order, first order decay, efficiency loss, and Monod. Wetland mesocosms were constructed using the primary soil type (in triplicate at each of the future wetland restoration sites. Eighteen mesocosm experiments were conducted over two years across seasons. Simulated drainage water was loaded into wetlands as batches, with target nitrate-N levels typically observed in agricultural drainage water (between 2.5 and 10 mg L−1. Nitrate-N removal observed during the experiments provided the basis for calibration and validation of the models. When the predictive strength of each of the four models was assessed, results indicated that the efficiency loss and first order decay models provided the strongest agreement between predicted and measured NO3-N removal rates, and the fit between the two models were comparable. Since the predictive power of these two models were similar, the less complicated first order decay model appeared to be the best choice in predicting appropriate loading rates for the future full-scale wetland restorations.

  6. Urban wastewater process by aerobic constructed wetland; Depuracion de aguas residuales urbanas utilizando un humedal artificial aerobio

    Energy Technology Data Exchange (ETDEWEB)

    Gil Rodriguez, M.

    2007-07-01

    In this paper the experiences of urban wastewater treatment are shown in an aerobic constructed wetland, using phragmites australis.They were carried out changes on the design and operation of aerobic constructed wetlands of subsurface flow, in order to increase denitrification and biodegradation rate and to diminish the surface of the installation. the flow was channeled through a long and narrow channel to get bigger biodegradation rate to approach to the plug flow performance. the active space of process consists of two sites, one first anoxic in which denitrification takes place, and in the other one the wetland in oxygenated environment the organic matters of the wastewater are consumed by biodegradation and it takes place nitrification, and utilization of nitrates and phosphates by the vegetable culture. (Author) 14 refs.

  7. Evapotranspiration from drained wetlands with different hydrologic regimes: Drivers, modeling, and storage functions

    Science.gov (United States)

    Wu, Chin-Lung; Shukla, Sanjay; Shrestha, Niroj K.

    2016-07-01

    We tested whether the current understanding of insignificant effect of drainage on evapotranspiration (ET) in the temperate region wetlands applies to those in the subtropics. Hydro-climatic drivers causing the changes in drained wetlands were identified and used to develop a generic model to predict wetland ET. Eddy covariance (EC)-based ET measurements were made for two years at two differently drained but close by wetlands, a heavily drained wetland (SW) (97% reduced surface storage) and a more functional wetland (DW) (42% reduced storage). Annual ET for more intensively drained SW was 836 mm, 34% less than DW (1271 mm) and the difference was significant (p = 0.001). This difference was mainly due to drainage driven differences in inundation and associated effects on net radiation (Rn) and local relative humidity. Two generic daily ET models, a regression model (MSE = 0.44 mm2, R2 = 0.80) and a machine learning-based Relevance Vector Machine (RVM) model (MSE = 0.36 mm2, R2 = 0.84), were developed with the latter being more robust. The RVM model can predict changes in ET for different restoration scenarios; a 1.1 m rise in drainage level showed 7% increase ET (18 mm) at SW while the increase at DW was negligible. The additional ET, 28% of surface flow, can enhance water storage, flood protection, and climate mitigation services at SW compared to DW. More intensely drained wetlands at higher elevation should be targeted for restoration for enhanced storage through increased ET. The models developed can predict changes in ET for improved evaluation of basin-scale effects of restoration programs and climate change scenarios.

  8. WETCHIMP-WSL: intercomparison of wetland methane emissions models over West Siberia

    Directory of Open Access Journals (Sweden)

    T. J. Bohn

    2015-06-01

    Full Text Available Wetlands are the world's largest natural source of methane, a powerful greenhouse gas. The strong sensitivity of methane emissions to environmental factors such as soil temperature and moisture has led to concerns about potential positive feedbacks to climate change. This risk is particularly relevant at high latitudes, which have experienced pronounced warming and where thawing permafrost could potentially liberate large amounts of labile carbon over the next 100 years. However, global models disagree as to the magnitude and spatial distribution of emissions, due to uncertainties in wetland area and emissions per unit area and a scarcity of in situ observations. Recent intensive field campaigns across the West Siberian Lowland (WSL make this an ideal region over which to assess the performance of large-scale process-based wetland models in a high-latitude environment. Here we present the results of a follow-up to the Wetland and Wetland CH4 Intercomparison of Models Project (WETCHIMP, focused on the West Siberian Lowland (WETCHIMP-WSL. We assessed 21 models and 5 inversions over this domain in terms of total CH4 emissions, simulated wetland areas, and CH4 fluxes per unit wetland area and compared these results to an intensive in situ CH4 flux data set, several wetland maps, and two satellite surface water products. We found that (a despite the large scatter of individual estimates, 12-year mean estimates of annual total emissions over the WSL from forward models (5.34 ± 0.54 Tg CH4 yr−1, inversions (6.06 ± 1.22 Tg CH4 yr−1, and in situ observations (3.91 ± 1.29 Tg CH4 yr−1 largely agreed; (b forward models using surface water products alone to estimate wetland areas suffered from severe biases in CH4 emissions; (c the interannual time series of models that lacked either soil thermal physics appropriate to the high latitudes or realistic emissions from unsaturated peatlands tended to be dominated by a single environmental driver

  9. An application of artificial intelligence for rainfall–runoff modeling

    Indian Academy of Sciences (India)

    This study proposes an application of two techniques of artificial intelligence (AI) for rainfall–runoff modeling: the artificial neural networks (ANN) and the evolutionary computation (EC). Two diff- erent ANN techniques, the feed forward back propagation (FFBP) and generalized regression neural network (GRNN) methods ...

  10. Comparison of Modeling and Simulation Results Management Microclimate of the Greenhouse by Fuzzy Logic Between a Wetland and Arid Region

    Directory of Open Access Journals (Sweden)

    Didi Faouzi

    2016-05-01

    Full Text Available Currently the climate computer offers many benefits and solves problems related to the regulation, monitoring and controls. Greenhouse growers remain vigilant and attentive, facing this technological development. They ensure competitiveness and optimize their investments / production cost which continues to grow. The application of artificial intelligence in the industry known for considerable growth, which is not the case in the field of agricultural greenhouses, where enforcement remains timid. It is from this fact, we undertake research work in this area and conduct a simulation based on meteorological data through MATLAB Simulink to finally analyze the thermal behavior -greenhouse microclimate energy. In this paper, we present comparison of modelling and simulation management of the greenhouse microclimate by fuzzy logic between a wetland (Dar El Beida Algeria and the other arid (Biskra Algeria.

  11. Analysis of Mental Processes Represented in Models of Artificial Consciousness

    Directory of Open Access Journals (Sweden)

    Luana Folchini da Costa

    2013-12-01

    Full Text Available The Artificial Consciousness concept has been used in the engineering area as being an evolution of the Artificial Intelligence. However, consciousness is a complex subject and often used without formalism. As a main contribution, in this work one proposes an analysis of four recent models of artificial consciousness published in the engineering area. The mental processes represented by these models are highlighted and correlations with the theoretical perspective of cognitive psychology are made. Finally, considerations about consciousness in such models are discussed.

  12. Sensitivity of wetland methane emissions to model assumptions: application and model testing against site observations

    Directory of Open Access Journals (Sweden)

    L. Meng

    2012-07-01

    Full Text Available Methane emissions from natural wetlands and rice paddies constitute a large proportion of atmospheric methane, but the magnitude and year-to-year variation of these methane sources are still unpredictable. Here we describe and evaluate the integration of a methane biogeochemical model (CLM4Me; Riley et al., 2011 into the Community Land Model 4.0 (CLM4CN in order to better explain spatial and temporal variations in methane emissions. We test new functions for soil pH and redox potential that impact microbial methane production in soils. We also constrain aerenchyma in plants in always-inundated areas in order to better represent wetland vegetation. Satellite inundated fraction is explicitly prescribed in the model, because there are large differences between simulated fractional inundation and satellite observations, and thus we do not use CLM4-simulated hydrology to predict inundated areas. A rice paddy module is also incorporated into the model, where the fraction of land used for rice production is explicitly prescribed. The model is evaluated at the site level with vegetation cover and water table prescribed from measurements. Explicit site level evaluations of simulated methane emissions are quite different than evaluating the grid-cell averaged emissions against available measurements. Using a baseline set of parameter values, our model-estimated average global wetland emissions for the period 1993–2004 were 256 Tg CH4 yr−1 (including the soil sink and rice paddy emissions in the year 2000 were 42 Tg CH4 yr−1. Tropical wetlands contributed 201 Tg CH4 yr−1, or 78% of the global wetland flux. Northern latitude (>50 N systems contributed 12 Tg CH4 yr−1. However, sensitivity studies show a large range (150–346 Tg CH4 yr−1 in predicted global methane emissions (excluding emissions from rice paddies. The large range is

  13. Delineating wetland catchments and modeling hydrologic connectivity using lidar data and aerial imagery

    Directory of Open Access Journals (Sweden)

    Q. Wu

    2017-07-01

    Full Text Available In traditional watershed delineation and topographic modeling, surface depressions are generally treated as spurious features and simply removed from a digital elevation model (DEM to enforce flow continuity of water across the topographic surface to the watershed outlets. In reality, however, many depressions in the DEM are actual wetland landscape features with seasonal to permanent inundation patterning characterized by nested hierarchical structures and dynamic filling–spilling–merging surface-water hydrological processes. Differentiating and appropriately processing such ecohydrologically meaningful features remains a major technical terrain-processing challenge, particularly as high-resolution spatial data are increasingly used to support modeling and geographic analysis needs. The objectives of this study were to delineate hierarchical wetland catchments and model their hydrologic connectivity using high-resolution lidar data and aerial imagery. The graph-theory-based contour tree method was used to delineate the hierarchical wetland catchments and characterize their geometric and topological properties. Potential hydrologic connectivity between wetlands and streams were simulated using the least-cost-path algorithm. The resulting flow network delineated potential flow paths connecting wetland depressions to each other or to the river network on scales finer than those available through the National Hydrography Dataset. The results demonstrated that our proposed framework is promising for improving overland flow simulation and hydrologic connectivity analysis.

  14. An Artificial Emotion Model For Visualizing Emotion of Characters

    OpenAIRE

    Junseok Ham; Chansun Jung; Junhyung Park; Jihye Ryeo; Ilju Ko

    2009-01-01

    It is hard to express emotion through only speech when we watch a character in a movie or a play because we cannot estimate the size, kind, and quantity of emotion. So this paper proposes an artificial emotion model for visualizing current emotion with color and location in emotion model. The artificial emotion model is designed considering causality of generated emotion, difference of personality, difference of continual emotional stimulus, and co-relation of various emo...

  15. Ecological modelling of a wetland for phytoremediating Cu, Zn and Mn in a gold–copper mine site using Typha domingensis (Poales: Typhaceae near Orange, NSW, Australia

    Directory of Open Access Journals (Sweden)

    Subrahmanyam Sreenath

    2017-12-01

    Full Text Available An artificial wetland was computationally modelled using STELLA®, a graphical programming tool for an Au-Cu mine site in Central-west NSW, the aim of which was to offer a predictive analysis of a proposed wetland for Cu, Zn and Mn removal using Typha domingensis as the agent. The model considers the important factors that impact phytoremediation of Cu, Zn and Mn. Simulations were performed to optimise the area of the wetland; concentration of Cu, Zn and Mn released from mine (AMD; and flow rates of water for maximum absorption of the metals. A scenario analysis indicates that at AMD = 0.75mg/L for Cu, Zn and Mn, 12.5, 8.6, and 357.9 kg of Cu, Zn and Mn, respectively, will be assimilated by the wetland in 35 years, which would be equivalent to 61 mg of Cu/kg, 70 mg of Zn/kg and 2,886 mg of Mn/kg of T. domingensis, respectively. However, should Cu, Zn and Mn in AMD increase to 3 mg/L, then 18.6 kg of Cu and 11.8 kg of Zn, respectively, will be assimilated in 35 years, whereas no substantial increase in absorption for Mn would occur. This indicates that 91 mg of Cu, 96 mg of Zn and 2917 mg of Mn will be assimilated for every kg of T. domingensis in the wetland. The best option for Cu storage would be to construct a wetland of 50,000 m2 area (AMD = 0.367 mg/L of Cu, which would capture 14.1 kg of Cu in 43 years, eventually releasing only 3.9 kg of Cu downstream. Simulations performed for a WA of 30,000 m2 indicate that for AMD = 0.367 mg/L of Zn, the wetland captures 6.2 kg, releasing only 3.5 kg downstream after 43 years; the concentration of Zn in the leachate would be 10.2 kg, making this the most efficient wetland amongst the options considered for phytoremediating Zn. This work will help mine managers and environmental researchers in developing an effective environmental management plan by focusing on phytoremediation, with a view at extracting Cu, Zn and Mn from the contaminated sites.

  16. A Physically-based Model for Predicting Soil Moisture Dynamics in Wetlands

    Science.gov (United States)

    Kalin, L.; Rezaeianzadeh, M.; Hantush, M. M.

    2017-12-01

    Wetlands are promoted as green infrastructures because of their characteristics in retaining and filtering water. In wetlands going through wetting/drying cycles, simulation of nutrient processes and biogeochemical reactions in both ponded and unsaturated wetland zones are needed for an improved understanding of wetland functioning for water quality improvement. The physically-based WetQual model can simulate the hydrology and nutrient and sediment cycles in natural and constructed wetlands. WetQual can be used in continuously flooded environments or in wetlands going through wetting/drying cycles. Currently, WetQual relies on 1-D Richards' Equation (RE) to simulate soil moisture dynamics in unponded parts of the wetlands. This is unnecessarily complex because as a lumped model, WetQual only requires average moisture contents. In this paper, we present a depth-averaged solution to the 1-D RE, called DARE, to simulate the average moisture content of the root zone and the layer below it in unsaturated parts of wetlands. DARE converts the PDE of the RE into ODEs; thus it is computationally more efficient. This method takes into account the plant uptake and groundwater table fluctuations, which are commonly overlooked in hydrologic models dealing with wetlands undergoing wetting and drying cycles. For verification purposes, DARE solutions were compared to Hydrus-1D model, which uses full RE, under gravity drainage only assumption and full-term equations. Model verifications were carried out under various top boundary conditions: no ponding at all, ponding at some point, and no rain. Through hypothetical scenarios and actual atmospheric data, the utility of DARE was demonstrated. Gravity drainage version of DARE worked well in comparison to Hydrus-1D, under all the assigned atmospheric boundary conditions of varying fluxes for all examined soil types (sandy loam, loam, sandy clay loam, and sand). The full-term version of DARE offers reasonable accuracy compared to the

  17. Environmental Modeling, The Natural Filter Wetland Priority layers identify priority wetland restoration sites by subwatershed. Land use, hydrology, soil, and landscape characteristics were analyzed to rank opportunities with high nutrient removal potential., Published in 2014, Smaller than 1:100000 scale, Maryland Department of Natural Resources (DNR).

    Data.gov (United States)

    NSGIC Education | GIS Inventory — Environmental Modeling dataset current as of 2014. The Natural Filter Wetland Priority layers identify priority wetland restoration sites by subwatershed. Land use,...

  18. Spatial and temporal modeling of wetland surface temperature using Landsat-8 imageries in Sulduz, Iran

    Directory of Open Access Journals (Sweden)

    Vahid Eisavi

    2016-01-01

    Full Text Available Wetland Surface Temperature (WST maps are an increasingly important parameter to understand the extensive range of existing processes in wetlands. The Wetlands placed in neighborhoods of agricultural and industrial lands are exposed to more chemical pollutants and pesticides that can lead to spatial and temporal variations of their surface temperature. Therefore, more studies are required for temperature modeling and the management and conservation of these variations in their ecosystem. Landsat 8 time series data of Sulduz region, Western Azerbaijan province, Iran were used in this study. The WST was derived using a mono-window algorithm after implementation of atmospheric correction. The NDVI (Normalized Differential Vegetation Index threshold method was also employed to determine the surface emissivity. Our findings show that the WST experienced extensive spatial and temporal variations. It reached its maximum value in June and also experienced the highest mean in the same month. In this research, August (2013.12.08 had a lowest spatial standard deviation regarding surface temperature and June (2013.06.28 had the highest one. Wetlands' watersides adjacent to industrial zones have a higher surface temperature than the middle lands of these places. The map obtained from the WST variance over time can be exploited to reveal thermal stable and unstable zones. The outcome demonstrates that land use, land cover effectively contribute to wetland ecosystem health. The results are useful in the water management, preventive efforts against drying of wetland and evapotranspiration modeling. The approach employed in this research indicates that remote sensing is a valuable, low-cost and stable tool for thermal monitoring of wetlands health.

  19. Modelling of seasonal dynamics of Wetland-Groundwater flow interaction in the Canadian Prairies

    Science.gov (United States)

    Ali, Melkamu; Nussbaumer, Raphaël; Ireson, Andrew; Keim, Dawn

    2015-04-01

    Wetland-shallow groundwater interaction is studied at the St. Denis National Wildlife Area in Saskatchewan, Canada, located within the northern glaciated prairies of North America. Ponds in the Canadian Prairies are intermittently connected by fill-spill processes in the spring and growing season of some wetter years. The contribution of the ponds and wetlands to groundwater is still a significant research challenge. The objective of this study is to evaluate model's ability to reproduce observed effects of groundwater-wetland interactions including seasonal pattern of shallow groundwater table, intended flow direction and to quantify the depression induced infiltration from the wetland to the surrounding uplands. The integrated surface-wetland-shallow groundwater processes and the changes in land-energy and water balances caused by the flow interaction are simulated using ParFlow-CLM at a small watershed of 1km2 containing both permanent and seasonal wetland complexes. We compare simulated water table depth with piezometers reading monitored by level loggers at the watershed. We also present the strengths and limitations of the model in reproducing observed behaviour of the groundwater table response to the spring snowmelt and summer rainfall. Simulations indicate that the shallow water table at the uphill recovers quickly after major rainfall events in early summer that generates lateral flow to the pond. In late summer, the wetland supplies water to the surrounding upland when the evapotranspiration is higher than the precipitation in which more water from the root zone is up taken by plants. Results also show that Parflow-CLM is able to reasonably simulate the water table patterns response to summer rainfall, while it is insufficient to reproduce the spring snowmelt infiltration which is the most dominant hydrological process in the Prairies.

  20. Braided artificial muscles: modeling and experimental validation

    Science.gov (United States)

    Dragan, Liliana; Cioban, Horia

    2009-01-01

    The paper presents a few graphical modalities for constructing the double helical braid, which is the basis for the braided artificial pneumatic muscles, by using specialized software applications. This represents the first stage in achieving the method of finite element analysis of this type of linear pneumatic actuator.

  1. Modeling adaptation of wetland plants under changing environments

    Science.gov (United States)

    Muneepeerakul, R.; Muneepeerakul, C. P.

    2010-12-01

    An evolutionary-game-theoretic approach is used to study the changes in traits of wetland plants in response to environmental changes, e.g., altered patterns of rainfall and nutrients. Here, a wetland is considered as a complex adaptive system where plants can adapt their strategies and influence one another. The system is subject to stochastic rainfall, which controls the dynamics of water level, soil moisture, and alternation between aerobic and anaerobic conditions in soil. Based on our previous work, a plant unit is characterized by three traits, namely biomass nitrogen content, specific leaf area, and allocation to rhizome. These traits control the basic functions of plants such as assimilation, respiration, and nutrient uptake, while affecting their environment through litter chemistry, root oxygenation, and thus soil microbial dynamics. The outcome of this evolutionary game, i.e., the best-performing plant traits against the backdrop of these interactions and feedbacks, is analyzed and its implications on important roles of wetlands in supporting our sustainability such as carbon sequestration in biosphere, nutrient cycling, and repository of biodiversity are discussed.

  2. Incorporating H2 Dynamics and Inhibition into a Microbially Based Methanogenesis Model for Restored Wetland Sediments

    Science.gov (United States)

    Pal, David; Jaffe, Peter

    2015-04-01

    Estimates of global CH4 emissions from wetlands indicate that wetlands are the largest natural source of CH4 to the atmosphere. In this paper, we propose that there is a missing component to these models that should be addressed. CH4 is produced in wetland sediments from the microbial degradation of organic carbon through multiple fermentation steps and methanogenesis pathways. There are multiple sources of carbon for methananogenesis; in vegetated wetland sediments, microbial communities consume root exudates as a major source of organic carbon. In many methane models propionate is used as a model carbon molecule. This simple sugar is fermented into acetate and H2, acetate is transformed to methane and CO2, while the H2 and CO2 are used to form an additional CH4 molecule. The hydrogenotrophic pathway involves the equilibrium of two dissolved gases, CH4 and H2. In an effort to limit CH4 emissions from wetlands, there has been growing interest in finding ways to limit plant transport of soil gases through root systems. Changing planted species, or genetically modifying new species of plants may control this transport of soil gases. While this may decrease the direct emissions of methane, there is little understanding about how H2 dynamics may feedback into overall methane production. The results of an incubation study were combined with a new model of propionate degradation for methanogenesis that also examines other natural parameters (i.e. gas transport through plants). This presentation examines how we would expect this model to behave in a natural field setting with changing sulfate and carbon loading schemes. These changes can be controlled through new plant species and other management practices. Next, we compare the behavior of two variations of this model, with or without the incorporation of H2 interactions, with changing sulfate, carbon loading and root volatilization. Results show that while the models behave similarly there may be a discrepancy of nearly

  3. A computer model to forecast wetland vegetation changes resulting from restoration and protection in coastal Louisiana

    Science.gov (United States)

    Visser, Jenneke M.; Duke-Sylvester, Scott M.; Carter, Jacoby; Broussard, Whitney P.

    2013-01-01

    The coastal wetlands of Louisiana are a unique ecosystem that supports a diversity of wildlife as well as a diverse community of commercial interests of both local and national importance. The state of Louisiana has established a 5-year cycle of scientific investigation to provide up-to-date information to guide future legislation and regulation aimed at preserving this critical ecosystem. Here we report on a model that projects changes in plant community distribution and composition in response to environmental conditions. This model is linked to a suite of other models and requires input from those that simulate the hydrology and morphology of coastal Louisiana. Collectively, these models are used to assess how alternative management plans may affect the wetland ecosystem through explicit spatial modeling of the physical and biological processes affected by proposed modifications to the ecosystem. We have also taken the opportunity to advance the state-of-the-art in wetland plant community modeling by using a model that is more species-based in its description of plant communities instead of one based on aggregated community types such as brackish marsh and saline marsh. The resulting model provides an increased level of ecological detail about how wetland communities are expected to respond. In addition, the output from this model provides critical inputs for estimating the effects of management on higher trophic level species though a more complete description of the shifts in habitat.

  4. Artificial Neural Network Based Model of Photovoltaic Cell

    Directory of Open Access Journals (Sweden)

    Messaouda Azzouzi

    2017-03-01

    Full Text Available This work concerns the modeling of a photovoltaic system and the prediction of the sensitivity of electrical parameters (current, power of the six types of photovoltaic cells based on voltage applied between terminals using one of the best known artificial intelligence technique which is the Artificial Neural Networks. The results of the modeling and prediction have been well shown as a function of number of iterations and using different learning algorithms to obtain the best results. 

  5. Integrated conceptual ecological model and habitat indices for the southwest Florida coastal wetlands

    Science.gov (United States)

    Wingard, G. Lynn; Lorenz, J. L.

    2014-01-01

    The coastal wetlands of southwest Florida that extend from Charlotte Harbor south to Cape Sable, contain more than 60,000 ha of mangroves and 22,177 ha of salt marsh. These coastal wetlands form a transition zone between the freshwater and marine environments of the South Florida Coastal Marine Ecosystem (SFCME). The coastal wetlands provide diverse ecosystem services that are valued by society and thus are important to the economy of the state. Species from throughout the region spend part of their life cycle in the coastal wetlands, including many marine and coastal-dependent species, making this zone critical to the ecosystem health of the Everglades and the SFCME. However, the coastal wetlands are increasingly vulnerable due to rising sea level, changes in storm intensity and frequency, land use, and water management practices. They are at the boundary of the region covered by the Comprehensive Everglades Restoration Plan (CERP), and thus are impacted by both CERP and marine resource management decisions. An integrated conceptual ecological model (ICEM) for the southwest coastal wetlands of Florida was developed that illustrates the linkages between drivers, pressures, ecological process, and ecosystem services. Five ecological indicators are presented: (1) mangrove community structure and spatial extent; (2) waterbirds; (3) prey-base fish and macroinvertebrates; (4) crocodilians; and (5) periphyton. Most of these indicators are already used in other areas of south Florida and the SFCME, and therefore will allow metrics from the coastal wetlands to be used in system-wide assessments that incorporate the entire Greater Everglades Ecosystem.

  6. Inferring tidal wetland stability from channel sediment fluxes: observations and a conceptual model

    Science.gov (United States)

    Ganju, Neil K.; Nidzieko, Nicholas J.; Kirwan, Matthew L.

    2013-01-01

    Anthropogenic and climatic forces have modified the geomorphology of tidal wetlands over a range of timescales. Changes in land use, sediment supply, river flow, storminess, and sea level alter the layout of tidal channels, intertidal flats, and marsh plains; these elements define wetland complexes. Diagnostically, measurements of net sediment fluxes through tidal channels are high-temporal resolution, spatially integrated quantities that indicate (1) whether a complex is stable over seasonal timescales and (2) what mechanisms are leading to that state. We estimated sediment fluxes through tidal channels draining wetland complexes on the Blackwater and Transquaking Rivers, Maryland, USA. While the Blackwater complex has experienced decades of degradation and been largely converted to open water, the Transquaking complex has persisted as an expansive, vegetated marsh. The measured net export at the Blackwater complex (1.0 kg/s or 0.56 kg/m2/yr over the landward marsh area) was caused by northwesterly winds, which exported water and sediment on the subtidal timescale; tidally forced net fluxes were weak and precluded landward transport of suspended sediment from potential seaward sources. Though wind forcing also exported sediment at the Transquaking complex, strong tidal forcing and proximity to a turbidity maximum led to an import of sediment (0.031 kg/s or 0.70 kg/m2/yr). This resulted in a spatially averaged accretion of 3.9 mm/yr, equaling the regional relative sea level rise. Our results suggest that in areas where seaward sediment supply is dominant, seaward wetlands may be more capable of withstanding sea level rise over the short term than landward wetlands. We propose a conceptual model to determine a complex's tendency toward stability or instability based on sediment source, wetland channel location, and transport mechanisms. Wetlands with a reliable portfolio of sources and transport mechanisms appear better suited to offset natural and

  7. Inferring tidal wetland stability from channel sediment fluxes: Observations and a conceptual model

    Science.gov (United States)

    Ganju, Neil K.; Nidzieko, Nicholas J.; Kirwan, Matthew L.

    2013-12-01

    and climatic forces have modified the geomorphology of tidal wetlands over a range of timescales. Changes in land use, sediment supply, river flow, storminess, and sea level alter the layout of tidal channels, intertidal flats, and marsh plains; these elements define wetland complexes. Diagnostically, measurements of net sediment fluxes through tidal channels are high-temporal resolution, spatially integrated quantities that indicate (1) whether a complex is stable over seasonal timescales and (2) what mechanisms are leading to that state. We estimated sediment fluxes through tidal channels draining wetland complexes on the Blackwater and Transquaking Rivers, Maryland, USA. While the Blackwater complex has experienced decades of degradation and been largely converted to open water, the Transquaking complex has persisted as an expansive, vegetated marsh. The measured net export at the Blackwater complex (1.0 kg/s or 0.56 kg/m2/yr over the landward marsh area) was caused by northwesterly winds, which exported water and sediment on the subtidal timescale; tidally forced net fluxes were weak and precluded landward transport of suspended sediment from potential seaward sources. Though wind forcing also exported sediment at the Transquaking complex, strong tidal forcing and proximity to a turbidity maximum led to an import of sediment (0.031 kg/s or 0.70 kg/m2/yr). This resulted in a spatially averaged accretion of 3.9 mm/yr, equaling the regional relative sea level rise. Our results suggest that in areas where seaward sediment supply is dominant, seaward wetlands may be more capable of withstanding sea level rise over the short term than landward wetlands. We propose a conceptual model to determine a complex's tendency toward stability or instability based on sediment source, wetland channel location, and transport mechanisms. Wetlands with a reliable portfolio of sources and transport mechanisms appear better suited to offset natural and anthropogenic loss.

  8. Modeling of primary production of phytoplankton in the wetland Jaboque, Bogotá D.C.

    Directory of Open Access Journals (Sweden)

    Julio Eduardo Beltrán Vargas

    2016-02-01

    Full Text Available A dynamic simulation model is presented to explain the general behavior of the  primary production of phytoplankton in the wetland Jaboque - Bogota, Colombia, in three sections with differential physical and chemical characteristics. The model takes into account the physicochemical variables, the basin area, depth, annual rainfall, water temperature, pH and concentration of chlorophyll _a. The dynamic modeling is based on differential equations and the Euler integration method is used, the modeling was developed using Stella 9.1® computer program. The model allows quantifying the primary production of phytoplankton in wetland Jaboque from chlorophyll _a monthly average concentration for each section. The results of the Ppf modeling show that Ppf concentration variations  in each section of the wetland follow a reverse pattern to the bimodal behavior of precipitation. A high degree of correspondence between the values of chlorophyll_a Ppf field and modeled in the following manner r2 = 0.86 for the first section and r2 = 0.86 and r2 = 0.79 for the remaining sections was found. Error determination was 0,57 relative to the first section and 0,35; 0,46, indicating that the results are not overstated. The model shows in general terms the functional aspects of behavior Ppf and its relation to the process of eutrophication, and it allows recommendations for the management and restoration of wetlands.

  9. A review of models and micrometeorological methods used to estimate wetland evapotranspiration

    Science.gov (United States)

    Drexler, J.Z.; Snyder, R.L.; Spano, D.; Paw, U.K.T.

    2004-01-01

    Within the past decade or so, the accuracy of evapotranspiration (ET) estimates has improved due to new and increasingly sophisticated methods. Yet despite a plethora of choices concerning methods, estimation of wetland ET remains insufficiently characterized due to the complexity of surface characteristics and the diversity of wetland types. In this review, we present models and micrometeorological methods that have been used to estimate wetland ET and discuss their suitability for particular wetland types. Hydrological, soil monitoring and lysimetric methods to determine ET are not discussed. Our review shows that, due to the variability and complexity of wetlands, there is no single approach that is the best for estimating wetland ET. Furthermore, there is no single foolproof method to obtain an accurate, independent measure of wetland ET. Because all of the methods reviewed, with the exception of eddy covariance and LIDAR, require measurements of net radiation (Rn) and soil heat flux (G), highly accurate measurements of these energy components are key to improving measurements of wetland ET. Many of the major methods used to determine ET can be applied successfully to wetlands of uniform vegetation and adequate fetch, however, certain caveats apply. For example, with accurate Rn and G data and small Bowen ratio (??) values, the Bowen ratio energy balance method can give accurate estimates of wetland ET. However, large errors in latent heat flux density can occur near sunrise and sunset when the Bowen ratio ?? ??? - 1??0. The eddy covariance method provides a direct measurement of latent heat flux density (??E) and sensible heat flux density (II), yet this method requires considerable expertise and expensive instrumentation to implement. A clear advantage of using the eddy covariance method is that ??E can be compared with Rn-G H, thereby allowing for an independent test of accuracy. The surface renewal method is inexpensive to replicate and, therefore, shows

  10. Mode Choice Modeling Using Artificial Neural Networks

    OpenAIRE

    Edara, Praveen Kumar

    2003-01-01

    Artificial intelligence techniques have produced excellent results in many diverse fields of engineering. Techniques such as neural networks and fuzzy systems have found their way into transportation engineering. In recent years, neural networks are being used instead of regression techniques for travel demand forecasting purposes. The basic reason lies in the fact that neural networks are able to capture complex relationships and learn from examples and also able to adapt when new data becom...

  11. Modelling nitrogen transformation and removal in mara river basin wetlands upstream of lake Victoria

    Science.gov (United States)

    Mayo, Aloyce W.; Muraza, Marwa; Norbert, Joel

    2018-06-01

    Lake Victoria, the largest lake in Africa, is a resource of social-economic potential in East Africa. This lake receives water from numerous tributaries including Mara River, which contributes about 4.8% of the total Lake water inflow. Unfortunately, Mara River basin faces environmental problems because of intensive settlement, agriculture, overgrazing in the basin and mining activities, which has lead to water pollution in the river, soil erosion and degradation, decreased soil fertility, loss of vegetation cover, decreased water infiltration capacity and increased sedimentation. One of the pollutants carried by the river includes nitrogen, which has contributed to ecological degradation of the Lake Victoria. Therefore this research work was intended to determine the effectiveness of Mara River wetland for removal of nitrogen and to establish nitrogen removal mechanisms in the wetland. To predict nitrogen removal in the wetland, the dynamics of nitrogen transformation was studied using a conceptual numerical model that takes into account of various processes in the system using STELLA II version 9.0®2006 software. Samples of model input from water, plants and sediments were taken for 45 days and were analyzed for pH, temperature, and DO in situ and chemical parameters such as NH3-N, Org-N, NO2-N, and NO3-N were analyzed in the laboratory in accordance with Standard methods. For plants, the density, dominance, biomass productivity and TN were determined and for sediments TN was analyzed. Inflow into the wetland was determined using stage-discharge relationship and was found to be 734,400 m3/day and the average wetland volume was 1,113,500 m3. Data collected by this study were used for model calibration of nitrogen transformation in this wetland while data from another wetland were used for model validation. It was found that about 37.8% of total nitrogen was removed by the wetland system largely through sedimentation (26.6%), plant uptake (6.6%) and

  12. Artificial cilia : a physical model for ciliary propulsion

    OpenAIRE

    Babataheri , Avin

    2009-01-01

    Most microorganisms use cilia or flagella as a means of propulsion. These low Reynolds number swimming mechanisms have been studied theoretically and experimentally on living organisms. However, so far very few physical experimental models have been realised. We describe here the fabrication of microscopic artificial cilia, actuated by a magnetic field. These artificial cilia share with real cilia a large aspect ratio, great flexibility, and the actuation by a magnetic torque distributed alon...

  13. Teaching methodology for modeling reference evapotranspiration with artificial neural networks

    OpenAIRE

    Martí, Pau; Pulido Calvo, Inmaculada; Gutiérrez Estrada, Juan Carlos

    2015-01-01

    [EN] Artificial neural networks are a robust alternative to conventional models for estimating different targets in irrigation engineering, among others, reference evapotranspiration, a key variable for estimating crop water requirements. This paper presents a didactic methodology for introducing students in the application of artificial neural networks for reference evapotranspiration estimation using MatLab c . Apart from learning a specific application of this software wi...

  14. Neuro-Based Artificial Intelligence Model for Loan Decisions

    OpenAIRE

    Shorouq F. Eletter; Saad G. Yaseen; Ghaleb A. Elrefae

    2010-01-01

    Problem statement: Despite the increase in consumer loans defaults and competition in the banking market, most of the Jordanian commercial banks are reluctant to use artificial intelligence software systems for supporting loan decisions. Approach: This study developed a proposed model that identifies artificial neural network as an enabling tool for evaluating credit applications to support loan decisions in the Jordanian Commercial banks. A multi-layer feed-forward neural network with backpr...

  15. Comparing Neural Networks and ARMA Models in Artificial Stock Market

    Czech Academy of Sciences Publication Activity Database

    Krtek, Jiří; Vošvrda, Miloslav

    2011-01-01

    Roč. 18, č. 28 (2011), s. 53-65 ISSN 1212-074X R&D Projects: GA ČR GD402/09/H045 Institutional research plan: CEZ:AV0Z10750506 Keywords : neural networks * vector ARMA * artificial market Subject RIV: AH - Economics http://library.utia.cas.cz/separaty/2011/E/krtek-comparing neural networks and arma models in artificial stock market.pdf

  16. Technical note: Comparison of methane ebullition modelling approaches used in terrestrial wetland models

    Science.gov (United States)

    Peltola, Olli; Raivonen, Maarit; Li, Xuefei; Vesala, Timo

    2018-02-01

    Emission via bubbling, i.e. ebullition, is one of the main methane (CH4) emission pathways from wetlands to the atmosphere. Direct measurement of gas bubble formation, growth and release in the peat-water matrix is challenging and in consequence these processes are relatively unknown and are coarsely represented in current wetland CH4 emission models. In this study we aimed to evaluate three ebullition modelling approaches and their effect on model performance. This was achieved by implementing the three approaches in one process-based CH4 emission model. All the approaches were based on some kind of threshold: either on CH4 pore water concentration (ECT), pressure (EPT) or free-phase gas volume (EBG) threshold. The model was run using 4 years of data from a boreal sedge fen and the results were compared with eddy covariance measurements of CH4 fluxes.Modelled annual CH4 emissions were largely unaffected by the different ebullition modelling approaches; however, temporal variability in CH4 emissions varied an order of magnitude between the approaches. Hence the ebullition modelling approach drives the temporal variability in modelled CH4 emissions and therefore significantly impacts, for instance, high-frequency (daily scale) model comparison and calibration against measurements. The modelling approach based on the most recent knowledge of the ebullition process (volume threshold, EBG) agreed the best with the measured fluxes (R2 = 0.63) and hence produced the most reasonable results, although there was a scale mismatch between the measurements (ecosystem scale with heterogeneous ebullition locations) and model results (single horizontally homogeneous peat column). The approach should be favoured over the two other more widely used ebullition modelling approaches and researchers are encouraged to implement it into their CH4 emission models.

  17. Stochastic Differential Equations in Artificial Pancreas Modelling

    DEFF Research Database (Denmark)

    Duun-Henriksen, Anne Katrine

    Type 1 diabetes accounts for approximately 5% of the total diabetes population. It is caused by the destruction of insulin producing β-cells in the pancreas. Various treatment strategies are available today, some of which include advanced technological devices such as an insulin pump and a contin......Type 1 diabetes accounts for approximately 5% of the total diabetes population. It is caused by the destruction of insulin producing β-cells in the pancreas. Various treatment strategies are available today, some of which include advanced technological devices such as an insulin pump...... of the insulin pump and the CGM has paved the way for a fully automatic treatment regime, the artificial pancreas. The idea is to connect the CGM with the insulin pump via a control algorithm running on e.g. the patients smart phone. The CGM observations are sent to the smart phone and based on this information...... of the system directly. The purpose of this PhD-project was to investigate the potential of SDEs in the artificial pancreas development. Especially, the emerging continuous monitoring of glucose levels makes SDEs highly applicable to this field. The current thesis aims at demonstrating and discussing...

  18. Modeling of Reservoir Inflow for Hydropower Dams Using Artificial ...

    African Journals Online (AJOL)

    The stream flow at the three hydropower reservoirs in Nigeria were modeled using hydro-meteorological parameters and Artificial Neural Network (ANN). The model revealed positive relationship between the observed and the modeled reservoir inflow with values of correlation coefficient of 0.57, 0.84 and 0.92 for Kainji, ...

  19. Understand the impacts of wetland restoration on peak flow and baseflow by coupling hydrologic and hydrodynamic models

    Science.gov (United States)

    Gao, H.; Sabo, J. L.

    2016-12-01

    Wetlands as the earth's kidneys provides various ecosystem services, such as absorbing pollutants, purifying freshwater, providing habitats for diverse ecosystems, sustaining species richness and biodiversity. From hydrologic perspective, wetlands can store storm-flood water in flooding seasons and release it afterwards, which will reduce flood peaks and reshape hydrograph. Therefore, as a green infrastructure and natural capital, wetlands provides a competent alternative to manage water resources in a green way, with potential to replace the widely criticized traditional gray infrastructure (i.e. dams and dikes) in certain cases. However, there are few systematic scientific tools to support our decision-making on site selection and allow us to quantitatively investigate the impacts of restored wetlands on hydrological process, not only in local scale but also in the view of entire catchment. In this study, we employed a topographic index, HAND (the Height Above the Nearest Drainage), to support our decision on potential site selection. Subsequently, a hydrological model (VIC, Variable Infiltration Capacity) was coupled with a macro-scale hydrodynamic model (CaMa-Flood, Catchment-Based Macro-scale Floodplain) to simulate the impact of wetland restoration on flood peaks and baseflow. The results demonstrated that topographic information is an essential factor to select wetland restoration location. Different reaches, wetlands area and the change of roughness coefficient should be taken into account while evaluating the impacts of wetland restoration. The simulated results also clearly illustrated that wetland restoration will increase the local storage and decrease the downstream peak flow which is beneficial for flood prevention. However, its impact on baseflow is ambiguous. Theoretically, restored wetlands will increase the baseflow due to the slower release of the stored flood water, but the increase of wetlands area may also increase the actual evaporation

  20. Geospatial scenario based modelling of urban and agricultural intrusions in Ramsar wetland Deepor Beel in Northeast India using a multi-layer perceptron neural network

    Science.gov (United States)

    Mozumder, Chitrini; Tripathi, Nitin K.

    2014-10-01

    In recent decades, the world has experienced unprecedented urban growth which endangers the green environment in and around urban areas. In this work, an artificial neural network (ANN) based model is developed to predict future impacts of urban and agricultural expansion on the uplands of Deepor Beel, a Ramsar wetland in the city area of Guwahati, Assam, India, by 2025 and 2035 respectively. Simulations were carried out for three different transition rates as determined from the changes during 2001-2011, namely simple extrapolation, Markov Chain (MC), and system dynamic (SD) modelling, using projected population growth, which were further investigated based on three different zoning policies. The first zoning policy employed no restriction while the second conversion restriction zoning policy restricted urban-agricultural expansion in the Guwahati Municipal Development Authority (GMDA) proposed green belt, extending to a third zoning policy providing wetland restoration in the proposed green belt. The prediction maps were found to be greatly influenced by the transition rates and the allowed transitions from one class to another within each sub-model. The model outputs were compared with GMDA land demand as proposed for 2025 whereby the land demand as produced by MC was found to best match the projected demand. Regarding the conservation of Deepor Beel, the Landscape Development Intensity (LDI) Index revealed that wetland restoration zoning policies may reduce the impact of urban growth on a local scale, but none of the zoning policies was found to minimize the impact on a broader base. The results from this study may assist the planning and reviewing of land use allocation within Guwahati city to secure ecological sustainability of the wetlands.

  1. Resource competition model predicts zonation and increasing nutrient use efficiency along a wetland salinity gradient

    Science.gov (United States)

    Schoolmaster, Donald; Stagg, Camille L.

    2018-01-01

    A trade-off between competitive ability and stress tolerance has been hypothesized and empirically supported to explain the zonation of species across stress gradients for a number of systems. Since stress often reduces plant productivity, one might expect a pattern of decreasing productivity across the zones of the stress gradient. However, this pattern is often not observed in coastal wetlands that show patterns of zonation along a salinity gradient. To address the potentially complex relationship between stress, zonation, and productivity in coastal wetlands, we developed a model of plant biomass as a function of resource competition and salinity stress. Analysis of the model confirms the conventional wisdom that a trade-off between competitive ability and stress tolerance is a necessary condition for zonation. It also suggests that a negative relationship between salinity and production can be overcome if (1) the supply of the limiting resource increases with greater salinity stress or (2) nutrient use efficiency increases with increasing salinity. We fit the equilibrium solution of the dynamic model to data from Louisiana coastal wetlands to test its ability to explain patterns of production across the landscape gradient and derive predictions that could be tested with independent data. We found support for a number of the model predictions, including patterns of decreasing competitive ability and increasing nutrient use efficiency across a gradient from freshwater to saline wetlands. In addition to providing a quantitative framework to support the mechanistic hypotheses of zonation, these results suggest that this simple model is a useful platform to further build upon, simulate and test mechanistic hypotheses of more complex patterns and phenomena in coastal wetlands.

  2. Monitoring and modeling wetland chloride concentrations in relationship to oil and gas development

    Science.gov (United States)

    Post van der Burg, Max; Tangen, Brian A.

    2015-01-01

    Extraction of oil and gas via unconventional methods is becoming an important aspect of energy production worldwide. Studying the effects of this development in countries where these technologies are being widely used may provide other countries, where development may be proposed, with some insight in terms of concerns associated with development. A fairly recent expansion of unconventional oil and gas development in North America provides such an opportunity. Rapid increases in energy development in North America have caught the attention of managers and scientists as a potential stressor for wildlife and their habitats. Of particular concern in the Northern Great Plains of the U.S. is the potential for chloride-rich produced water associated with unconventional oil and gas development to alter the water chemistry of wetlands. We describe a landscape scale modeling approach designed to examine the relationship between potential chloride contamination in wetlands and patterns of oil and gas development. We used a spatial Bayesian hierarchical modeling approach to assess multiple models explaining chloride concentrations in wetlands. These models included effects related to oil and gas wells (e.g. age of wells, number of wells) and surficial geology (e.g. glacial till, outwash). We found that the model containing the number of wells and the surficial geology surrounding a wetland best explained variation in chloride concentrations. Our spatial predictions showed regions of localized high chloride concentrations. Given the spatiotemporal variability of regional wetland water chemistry, we do not regard our results as predictions of contamination, but rather as a way to identify locations that may require more intensive sampling or further investigation. We suggest that an approach like the one outlined here could easily be extended to more of an adaptive monitoring approach to answer questions about chloride contamination risk that are of interest to managers.

  3. Assessing the cumulative impacts of geographically isolated wetlands on watershed hydrology using the SWAT model coupled with improved wetland modules.

    Science.gov (United States)

    Lee, S; Yeo, I-Y; Lang, M W; Sadeghi, A M; McCarty, G W; Moglen, G E; Evenson, G R

    2018-06-07

    Despite recognizing the importance of wetlands in the Coastal Plain of the Chesapeake Bay Watershed (CBW) in terms of ecosystem services, our understanding of wetland functions has mostly been limited to individual wetlands and overall catchment-scale wetland functions have rarely been investigated. This study is aimed at assessing the cumulative impacts of wetlands on watershed hydrology for an agricultural watershed within the Coastal Plain of the CBW using the Soil and Water Assessment Tool (SWAT). We employed two improved wetland modules for enhanced representation of physical processes and spatial distribution of riparian wetlands (RWs) and geographically isolated wetlands (GIWs). This study focused on GIWs as their hydrological impacts on watershed hydrology are poorly understood and GIWs are poorly protected. Multiple wetland scenarios were prepared by removing all or portions of the baseline GIW condition indicated by the U.S. Fish and Wildlife Service National Wetlands Inventory geospatial dataset. We further compared the impacts of GIWs and RWs on downstream flow (i.e., streamflow at the watershed outlet). Our simulation results showed that GIWs strongly influenced downstream flow by altering water transport mechanisms in upstream areas. Loss of all GIWs reduced both water routed to GIWs and water infiltrated into the soil through the bottom of GIWs, leading to an increase in surface runoff of 9% and a decrease in groundwater flow of 7% in upstream areas. These changes resulted in increased variability of downstream flow in response to extreme flow conditions. GIW loss also induced an increase in month to month variability of downstream flow and a decrease in the baseflow contribution to streamflow. Loss of all GIWs was shown to cause a greater fluctuation of downstream flow than loss of all RWs for this study site, due to a greater total water storage capacity of GIWs. Our findings indicate that GIWs play a significant role in controlling hydrological

  4. Systems in Science: Modeling Using Three Artificial Intelligence Concepts.

    Science.gov (United States)

    Sunal, Cynthia Szymanski; Karr, Charles L.; Smith, Coralee; Sunal, Dennis W.

    2003-01-01

    Describes an interdisciplinary course focusing on modeling scientific systems. Investigates elementary education majors' applications of three artificial intelligence concepts used in modeling scientific systems before and after the course. Reveals a great increase in understanding of concepts presented but inconsistent application. (Author/KHR)

  5. An ecohydrological model for studying groundwater-vegetation interactions in wetlands

    Science.gov (United States)

    Chui, Ting Fong May; Low, Swee Yang; Liong, Shie-Yui

    2011-10-01

    SummaryDespite their importance to the natural environment, wetlands worldwide face drastic degradation from changes in land use and climatic patterns. To help preservation efforts and guide conservation strategies, a clear understanding of the dynamic relationship between coupled hydrology and vegetation systems in wetlands, and their responses to engineering works and climate change, is needed. An ecohydrological model was developed in this study to address this issue. The model combines a hydrology component based on the Richards' equation for characterizing variably saturated groundwater flow, with a vegetation component described by Lotka-Volterra equations tailored for plant growth. Vegetation is represented by two characteristic wetland herbaceous plant types which differ in their flood and drought resistances. Validation of the model on a study site in the Everglades demonstrated the capability of the model in capturing field-measured water table and transpiration dynamics. The model was next applied on a section of the Nee Soon swamp forest, a tropical wetland in Singapore, for studying the impact of possible drainage works on the groundwater hydrology and native vegetation. Drainage of 10 m downstream of the wetland resulted in a localized zone of influence within half a kilometer from the drainage site with significant adverse impacts on groundwater and biomass levels, indicating a strong need for conservation. Simulated water table-plant biomass relationships demonstrated the capability of the model in capturing the time-lag in biomass response to water table changes. To test the significance of taking plant growth into consideration, the performance of the model was compared to one that substituted the vegetation component with a pre-specified evapotranspiration rate. Unlike its revised counterpart, the original ecohydrological model explicitly accounted for the drainage-induced plant biomass decrease and translated the resulting reduced transpiration

  6. Uncertainties in modelling CH4 emissions from northern wetlands in glacial climates: effect of hydrological model and CH4 model structure

    Directory of Open Access Journals (Sweden)

    J. van Huissteden

    2009-07-01

    Full Text Available Methane (CH4 fluxes from northern wetlands may have influenced atmospheric CH4 concentrations at climate warming phases during the last 800 000 years and during the present global warming. Including these CH4 fluxes in earth system models is essential to understand feedbacks between climate and atmospheric composition. Attempts to model CH4 fluxes from wetlands have previously been undertaken using various approaches. Here, we test a process-based wetland CH4 flux model (PEATLAND-VU which includes details of soil-atmosphere CH4 transport. The model has been used to simulate CH4 emissions from continental Europe in previous glacial climates and the current climate. This paper presents results regarding the sensitivity of modeling glacial terrestrial CH4 fluxes to (a basic tuning parameters of the model, (b different approaches in modeling of the water table, and (c model structure. In order to test the model structure, PEATLAND-VU was compared to a simpler modeling approach based on wetland primary production estimated from a vegetation model (BIOME 3.5. The tuning parameters are the CH4 production rate from labile organic carbon and its temperature sensitivity. The modelled fluxes prove comparatively insensitive to hydrology representation, while sensitive to microbial parameters and model structure. Glacial climate emissions are also highly sensitive to assumptions about the extent of ice cover and exposed seafloor. Wetland expansion over low relief exposed seafloor areas have compensated for a decrease of wetland area due to continental ice cover.

  7. Evaluation of Artificial Intelligence Based Models for Chemical Biodegradability Prediction

    Directory of Open Access Journals (Sweden)

    Aleksandar Sabljic

    2004-12-01

    Full Text Available This study presents a review of biodegradability modeling efforts including a detailed assessment of two models developed using an artificial intelligence based methodology. Validation results for these models using an independent, quality reviewed database, demonstrate that the models perform well when compared to another commonly used biodegradability model, against the same data. The ability of models induced by an artificial intelligence methodology to accommodate complex interactions in detailed systems, and the demonstrated reliability of the approach evaluated by this study, indicate that the methodology may have application in broadening the scope of biodegradability models. Given adequate data for biodegradability of chemicals under environmental conditions, this may allow for the development of future models that include such things as surface interface impacts on biodegradability for example.

  8. Natural vs. artificial groundwater recharge, quantification through inverse modeling

    Directory of Open Access Journals (Sweden)

    H. Hashemi

    2013-02-01

    Full Text Available Estimating the change in groundwater recharge from an introduced artificial recharge system is important in order to evaluate future water availability. This paper presents an inverse modeling approach to quantify the recharge contribution from both an ephemeral river channel and an introduced artificial recharge system based on floodwater spreading in arid Iran. The study used the MODFLOW-2000 to estimate recharge for both steady- and unsteady-state conditions. The model was calibrated and verified based on the observed hydraulic head in observation wells and model precision, uncertainty, and model sensitivity were analyzed in all modeling steps. The results showed that in a normal year without extreme events, the floodwater spreading system is the main contributor to recharge with 80% and the ephemeral river channel with 20% of total recharge in the studied area. Uncertainty analysis revealed that the river channel recharge estimation represents relatively more uncertainty in comparison to the artificial recharge zones. The model is also less sensitive to the river channel. The results show that by expanding the artificial recharge system, the recharge volume can be increased even for small flood events, while the recharge through the river channel increases only for major flood events.

  9. Spectral model for long-term computation of thermodynamics and potential evaporation in shallow wetlands

    Science.gov (United States)

    de la Fuente, Alberto; Meruane, Carolina

    2017-09-01

    Altiplanic wetlands are unique ecosystems located in the elevated plateaus of Chile, Argentina, Peru, and Bolivia. These ecosystems are under threat due to changes in land use, groundwater extractions, and climate change that will modify the water balance through changes in precipitation and evaporation rates. Long-term prediction of the fate of aquatic ecosystems imposes computational constraints that make finding a solution impossible in some cases. In this article, we present a spectral model for long-term simulations of the thermodynamics of shallow wetlands in the limit case when the water depth tends to zero. This spectral model solves for water and sediment temperature, as well as heat, momentum, and mass exchanged with the atmosphere. The parameters of the model (water depth, thermal properties of the sediments, and surface albedo) and the atmospheric downscaling were calibrated using the MODIS product of the land surface temperature. Moreover, the performance of the daily evaporation rates predicted by the model was evaluated against daily pan evaporation data measured between 1964 and 2012. The spectral model was able to correctly represent both seasonal fluctuation and climatic trends observed in daily evaporation rates. It is concluded that the spectral model presented in this article is a suitable tool for assessing the global climate change effects on shallow wetlands whose thermodynamics is forced by heat exchanges with the atmosphere and modulated by the heat-reservoir role of the sediments.

  10. Ecosystem's Modeling of Bhoj Wetland - A Base For Economic Valuation and Sustainable Management

    Science.gov (United States)

    Verma, M.; Bakshi, N.; Nair, R.

    terms of productivity losses and health impacts? How feedback can be taken from these impacts to revise or develop management policies and to seek participation of stakeholders to check wet- land degradation or losses? What type of benefits accrues to people from this wetland? What is the willingness of the people to pay to conserve this important water body? Lake degradation is due to multiple causes hence lakeSs restoration requires multi- ple interventions. To suggest such interventions and above all their prioritizations, an 1 ecosystem model for the lake has been developed following the systemSs dynamics approach. The main objective of the Ecosystem Modeling of the wetland was to under- stand the changes in the hydrology of the wetland first on account of certain changes in the conditions of the surroundings such that valuation process can be followed with current and future scenarios of the lakes hydrology in hand. A sophisticated computer software called as STELLA was used for the modeling exercise. The model used wa- ter quality parameters to show the impact of flow of sewage on dissolved oxygen, bio-chemical oxygen demand, pH, total hardness, total alkalinity, bacterial count and growth of weeds. A base scenario has been created and various simulation runs have been performed for the pre and ongoing restoration activities for next 25 years so as to represent the health of the wetlandSs ecosystem. These scenarios have then been used in the valuation exercise to estimate the conservation value of the lake. Various valuation techniques like contingent valuation, production function approach, hedonic pricing and supply cost have been used to capture the economic values as perceived by different stakeholders. These scenarios and the valuation exercises further throw light on the prioritization of future policy intervention for sustainable management of this urban wetland. Key words: Ecosystem Services, Water Quality Parameters, EcosystemSs Modeling, Economic

  11. A Comparison of Modeling Approaches in Simulating Chlorinated Ethene Removal in a Constructed Wetland by a Microbial Consortia

    National Research Council Canada - National Science Library

    Campbell, Jason

    2002-01-01

    The purpose of this study is to compare different approaches to modeling the reductive dechlorination of chlorinated ethenes in the anaerobic region of an upward flow constructed wetland by microbial consortia...

  12. Biodegradation and kinetics of organic compounds and heavy metals in an artificial wetland system (AWS) by using water hyacinths as a biological filter.

    Science.gov (United States)

    Rodríguez-Espinosa, P F; Mendoza-Pérez, J A; Tabla-Hernandez, J; Martínez-Tavera, E; Monroy-Mendieta, M M

    2018-01-02

    The objective of the present study was to investigate the ability of water hyacinth (Eichhornia crassipes) to absorb organic compounds (potassium hydrogen phthalate, sodium tartrate, malathion, 2,4-dichlorophenoxy acetic acid (2,4-D), and piroxicam). For the aforementioned purpose, an artificial wetland system (AWS) was constructed and filled with water hyacinth collected from the Valsequillo Reservoir, Puebla, Mexico. Potassium hydrogen phthalate and sodium tartrate were measured in terms of chemical oxygen demand (COD) and biological oxygen demand (BOD). The present study indicated that the water hyacinths absorbed nearly 1.8-16.6 g of COD kg -1 dm (dry mass of water hyacinth), while the absorbance efficiency of BOD was observed to be 45.8%. The results also indicated that the maximum absorbance efficiency of malathion, 2,4-D, and piroxicam was observed to be 67.6%, 58.3%, and 99.1%, respectively. The kinetics of organic compounds fitted different orders as malathion followed a zeroth-order reaction, while 2,4-D and piroxicam followed the first-order reactions. Preliminary assessment of absorption of heavy metals by the water hyacinth in the AWS was observed to be (all values in mg g -1 ) 7 (Ni), 13.4 (Cd), 16.3 (Pb), and 17.5 (Zn) of dry biomass, thus proving its feasibility to depurate wastewater.

  13. EMG analysis and modelling of flat bench press using artificial ...

    African Journals Online (AJOL)

    The objective of this study was to evaluate the contribution of particular muscle groups during the Flat Bench Press (FBP) with different external loads. Additionally, the authors attempted to determine whether regression models or Artificial Neural Networks (ANNs) can predict FBP results more precisely and whether they can ...

  14. Introducing Artificial Neural Networks through a Spreadsheet Model

    Science.gov (United States)

    Rienzo, Thomas F.; Athappilly, Kuriakose K.

    2012-01-01

    Business students taking data mining classes are often introduced to artificial neural networks (ANN) through point and click navigation exercises in application software. Even if correct outcomes are obtained, students frequently do not obtain a thorough understanding of ANN processes. This spreadsheet model was created to illuminate the roles of…

  15. An artificial ecosystem model used in the study of social, economic and technological dynamics: An artificial electrical energy market

    International Nuclear Information System (INIS)

    Arjona, D.

    1998-01-01

    This paper will present the artificial ecosystem as a tool, in the development of multi agent models for the simulation of economic and technological dynamics (as well as other possible applications). This tool is based on the mechanics of an artificial society and consists of autonomous artificial agents that interact with individuals that have different characteristics and behavior and other that have a similar conduct to their own. Initial conditions are assumed not to be controllable, however they can be influenced. The importance of the concept of the ecosystem is in understanding great units in the light of their own components which are relevant for the analysis and become interdependent among themselves and with other essential components that hold the total operation of the system. Ideas for the development of a simulation model based on autonomous intelligent agents are presented. These agents will have a brain that is based on artificial intelligence technologies. The Sand Kings Simulation Model, an artificial ecosystem model developed by the author, is described as well as the application of artificial intelligence to this artificial life model. An application to a real life problem is also offered as an artificial energy market that is currently being developed by the author is described

  16. Hydrological-niche models predict water plant functional group distributions in diverse wetland types.

    Science.gov (United States)

    Deane, David C; Nicol, Jason M; Gehrig, Susan L; Harding, Claire; Aldridge, Kane T; Goodman, Abigail M; Brookes, Justin D

    2017-06-01

    Human use of water resources threatens environmental water supplies. If resource managers are to develop policies that avoid unacceptable ecological impacts, some means to predict ecosystem response to changes in water availability is necessary. This is difficult to achieve at spatial scales relevant for water resource management because of the high natural variability in ecosystem hydrology and ecology. Water plant functional groups classify species with similar hydrological niche preferences together, allowing a qualitative means to generalize community responses to changes in hydrology. We tested the potential for functional groups in making quantitative prediction of water plant functional group distributions across diverse wetland types over a large geographical extent. We sampled wetlands covering a broad range of hydrogeomorphic and salinity conditions in South Australia, collecting both hydrological and floristic data from 687 quadrats across 28 wetland hydrological gradients. We built hydrological-niche models for eight water plant functional groups using a range of candidate models combining different surface inundation metrics. We then tested the predictive performance of top-ranked individual and averaged models for each functional group. Cross validation showed that models achieved acceptable predictive performance, with correct classification rates in the range 0.68-0.95. Model predictions can be made at any spatial scale that hydrological data are available and could be implemented in a geographical information system. We show the response of water plant functional groups to inundation is consistent enough across diverse wetland types to quantify the probability of hydrological impacts over regional spatial scales. © 2017 by the Ecological Society of America.

  17. Results of a modeling workshop concerning preservation and protection of wetlands in North Dakota

    Science.gov (United States)

    Andrews, Austin K.; Auble, Gregor T.; Ellison, Richard A.; Hamilton, David B.; Roelle, James E.

    1981-01-01

    In a recently signed letter, the Governor of North Dakota and the Assistant Secretary of the Interior for Fish and Wildlife and Parks charged a joint state-federal study group with examination of two separate questions: 1) mitigation for the Garrison Diversion Project; and 2) planning for long-range protection and preservation of fish and wildlife habitat in North Dakota. The cochair for this study group (the Secretary of the Interior's Field Representative, Denver, Colorado, and the Natural Resources Coordinator for North Dakota) further articulated the charge concerning the second of these two questions to include three steps: 1) development of a general plan for preservation and protection of migratory waterfowl and their associated wetland habitat; 2) a comprehensive analysis of alternative strategies, including opportunities and constraints, for achieving the goals articulated in Step 1; and 3) design of a coordinated state-federal public information program to assist in plan implementation. In order to obtain input from a variety of interests, the joint study group initiated step 2 activities with a five-day workshop in Bismarck, N. D.; December 8-12, 1980. The objectives of the workshop were: 1) to identify alternative strategies for preserving and enhancing waterfowl production habitat in North Dakota; 2) to identify opportunities and constraints associated with those alternatives; and 3) to promote communication and understanding of the implications of those alternatives for all affected parties. To achieve these objectives, the workshop utilized a group of concepts and techniques collectively known as Adaptive Environmental Assessment (AEA). Developed by Dr. C. S. Holling and his co-workers at the University of British Columbia, the AEA process involves planners, managers, scientists, and other interested parties in a structures atmosphere whose focus is the construction and examination of a computerized simulation model of the resource system under

  18. A Mechanistically Informed User-Friendly Model to Predict Greenhouse Gas (GHG) Fluxes and Carbon Storage from Coastal Wetlands

    Science.gov (United States)

    Abdul-Aziz, O. I.; Ishtiaq, K. S.

    2015-12-01

    We present a user-friendly modeling tool on MS Excel to predict the greenhouse gas (GHG) fluxes and estimate potential carbon sequestration from the coastal wetlands. The dominant controls of wetland GHG fluxes and their relative mechanistic linkages with various hydro-climatic, sea level, biogeochemical and ecological drivers were first determined by employing a systematic data-analytics method, including Pearson correlation matrix, principal component and factor analyses, and exploratory partial least squares regressions. The mechanistic knowledge and understanding was then utilized to develop parsimonious non-linear (power-law) models to predict wetland carbon dioxide (CO2) and methane (CH4) fluxes based on a sub-set of climatic, hydrologic and environmental drivers such as the photosynthetically active radiation, soil temperature, water depth, and soil salinity. The models were tested with field data for multiple sites and seasons (2012-13) collected from the Waquoit Bay, MA. The model estimated the annual wetland carbon storage by up-scaling the instantaneous predicted fluxes to an extended growing season (e.g., May-October) and by accounting for the net annual lateral carbon fluxes between the wetlands and estuary. The Excel Spreadsheet model is a simple ecological engineering tool for coastal carbon management and their incorporation into a potential carbon market under a changing climate, sea level and environment. Specifically, the model can help to determine appropriate GHG offset protocols and monitoring plans for projects that focus on tidal wetland restoration and maintenance.

  19. Effect of bacteria density and accumulated inert solids on the effluent pollutant concentrations predicted by the constructed wetlands model BIO_PORE

    OpenAIRE

    Samsó Campà, Roger; Blazquez, Jordi; Agullo Chaler, Nuria; Grau Barceló, Joan; Torres Cámara, Ricardo; García Serrano, Joan

    2015-01-01

    Constructed wetlands are a widely adopted technology for the treatment of wastewater in small communities. The understanding of their internal functioning has increased at an unprecedented pace over recent years, in part thanks to the use of mathematical models. BIO_PORE model is one of the most recent models developed for constructed wetlands. This model was built in the COMSOL Multiphysics (TM) software and implements the biokinetic expressions of Constructed Wetlands Model 1 (CWM1) to desc...

  20. Wetlands: The changing regulatory landscape

    International Nuclear Information System (INIS)

    Glick, R.M.

    1993-01-01

    Protection of wetlands became a national issue in 1988 when President George Bush pledged no net loss of wetlands in the US under his open-quotes environmental presidency.close quotes As wetlands became a national issue, the job of protecting them became an obligation for many groups, including hydro-power developers. Now, when a site selected for development includes an area that may be classified as a wetland, the developer quickly discovers the importance of recognizing and protecting these natural habitats. Federal legislation severely limits development of wetland, and most states increase the restrictions with their own wetlands regulations. The difficulty of defining wetlands complicates federal and state enforcement. Land that appears to be dry may in fact be classified as a wetland. So, even if a site appears dry, potential hydro developers must confirm whether or not any jurisdictional wetlands are present. Regulated lands include much more than marshes and swamps. Further complicating the definition of wetlands, a recent court decision found that even artificially created wetlands, such as man-made ponds, may be subject to regulation. Hydro developers must be aware of current regulatory requirements before they consider development of any site that may contain wetlands. To be certain that a site is open-quotes buildableclose quotes from the standpoint of wetlands regulation, a developer must verify (with the help of state agencies) that the property does not contain any jurisdictional wetlands. If it does, the regulatory process before development becomes much more complicated. For the short term, uncertainty abounds and extreme caution is in order. Because the regulatory process has become so complex and an agreeable definition of wetlands so elusive, the trend among the Corps and collaborating agencies is to constrict nationwide permits in favor of narrowing the jurisdictional definition of wetlands

  1. Validation and Comparison of a Model of the Effect of Sea-Level Rise on Coastal Wetlands.

    Science.gov (United States)

    Mogensen, Laura A; Rogers, Kerrylee

    2018-01-22

    Models are used to project coastal wetland distribution under future sea-level rise scenarios to assist decision-making. Model validation and comparison was used to investigate error and uncertainty in the Sea Level Affecting Marshes Model, a readily available model with minimal validation, particularly for wetlands beyond North America. Accurate parameterisation is required to improve the performance of the model, and indeed any spatial model. Consideration of tidal attenuation further enhances model performance, particularly for coastal wetlands located within estuaries along wave-dominated coastlines. The model does not simulate vegetation changes that are known to occur, particularly when sedimentation exceeds rates of sea-level rise resulting in shoreline progradation. Model performance was reasonable over decadal timescales, decreasing as the time-scale of retrospection increased due to compounding of errors. Comparison with other deterministic models showed reasonable agreement by 2100. However, given the uncertainty of the future and the unpredictable nature of coastal wetlands, it is difficult to ascertain which model could be realistic enough to meet its intended purpose. Model validation and comparison are useful for assessing model efficacy and parameterisation, and should be applied before application of any spatially explicit model of coastal wetland response to sea-level rise.

  2. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif

    2013-05-01

    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  3. Constructed Wetlands

    Science.gov (United States)

    these systems can improve water quality, engineers and scientists construct systems that replicate the functions of natural wetlands. Constructed wetlands are treatment systems that use natural processes

  4. Modelling transport of water and solutes in future wetlands in Forsmark

    Energy Technology Data Exchange (ETDEWEB)

    Vikstroem, Maria; Gustafsson, Lars-Goeran [DHI Water and Environment AB, Vaexjoe (Sweden)

    2006-03-15

    The Forsmark area consists of a number of natural wetlands. As a part of the evaluation of wetlands in the safety assessment for the area, possible future wetlands are being studied with respect to hydrology and transport mechanisms. A sensitivity analyses is performed to point out the governing parameters for the wetland hydraulics. The analysis of future wetlands is carried out using the hydrological model system Mike SHE. Mike SHE has been used to describe the near-surface hydrology for a regional model area in Forsmark. Three types of areas have been chosen. Today's lake Bolundfjaerden is because of its shallow depth likely to develop into a mire in the future. As it is situated in the downstream part of the regional model area, the runoff to the lake from upstream surface water system is significant. Lake Eckarfjaerden is situated in the upstream part of the catchment at a higher altitude and with a smaller inflow. Lake Puttan is situated above a planned layout of the repository and has a potential to receive discharges from a repository. It also lies in the downstream part of a large discharge area. The topography of the future mires is assumed to be flat, up to today's mean water level in each lake. To transport the surface runoff through the wetland, streams or water courses are assumed to form within the peat. The analyses of future wetlands in the Forsmark area show that the hydraulic conditions that exists today will somewhat alter as the peat is formed. For Bolundsfjaerden, where there during present conditions are weak discharge areas, a recharge area has formed during the summer. This can be explained by the amount of surface water that forms on the surface which increases the head elevation in the upper soil layers. The same holds for Eckarfjaerden, while Puttan after the peat has developed still is a discharge area due to its naturally strong discharge position close to the sea. Different vegetation and development stages for the peat have

  5. Modelling transport of water and solutes in future wetlands in Forsmark

    International Nuclear Information System (INIS)

    Vikstroem, Maria; Gustafsson, Lars-Goeran

    2006-03-01

    The Forsmark area consists of a number of natural wetlands. As a part of the evaluation of wetlands in the safety assessment for the area, possible future wetlands are being studied with respect to hydrology and transport mechanisms. A sensitivity analyses is performed to point out the governing parameters for the wetland hydraulics. The analysis of future wetlands is carried out using the hydrological model system Mike SHE. Mike SHE has been used to describe the near-surface hydrology for a regional model area in Forsmark. Three types of areas have been chosen. Today's lake Bolundfjaerden is because of its shallow depth likely to develop into a mire in the future. As it is situated in the downstream part of the regional model area, the runoff to the lake from upstream surface water system is significant. Lake Eckarfjaerden is situated in the upstream part of the catchment at a higher altitude and with a smaller inflow. Lake Puttan is situated above a planned layout of the repository and has a potential to receive discharges from a repository. It also lies in the downstream part of a large discharge area. The topography of the future mires is assumed to be flat, up to today's mean water level in each lake. To transport the surface runoff through the wetland, streams or water courses are assumed to form within the peat. The analyses of future wetlands in the Forsmark area show that the hydraulic conditions that exists today will somewhat alter as the peat is formed. For Bolundsfjaerden, where there during present conditions are weak discharge areas, a recharge area has formed during the summer. This can be explained by the amount of surface water that forms on the surface which increases the head elevation in the upper soil layers. The same holds for Eckarfjaerden, while Puttan after the peat has developed still is a discharge area due to its naturally strong discharge position close to the sea. Different vegetation and development stages for the peat have been

  6. Modeling sediment accumulation in North American playa wetlands in response to climate change, 1940-2100

    Science.gov (United States)

    Burris, Lucy; Skagen, Susan K.

    2013-01-01

    Playa wetlands on the west-central Great Plains of North America are vulnerable to sediment infilling from upland agriculture, putting at risk several important ecosystem services as well as essential habitats and food resources of diverse wetland-dependent biota. Climate predictions for this semi-arid area indicate reduced precipitation which may alter rates of erosion, runoff, and sedimentation of playas. We forecasted erosion rates, sediment depths, and resultant playa wetland depths across the west-central Great Plains and examined the relative roles of land use context and projected changes in precipitation in the sedimentation process. We estimated erosion with the Revised Universal Soil Loss Equation (RUSLE) using historic values and downscaled precipitation predictions from three general circulation models and three emissions scenarios. We calibrated RUSLE results using field sediment measurements. RUSLE is appealing for regional scale modeling because it uses climate forecasts with monthly resolution and other widely available values including soil texture, slope and land use. Sediment accumulation rates will continue near historic levels through 2070 and will be sufficient to cause most playas (if not already filled) to fill with sediment within the next 100 years in the absence of mitigation. Land use surrounding the playa, whether grassland or tilled cropland, is more influential in sediment accumulation than climate-driven precipitation change.

  7. Modelling wetland-groundwater interactions in the boreal Kälväsvaara esker, Northern Finland

    Science.gov (United States)

    Jaros, Anna; Rossi, Pekka; Ronkanen, Anna-Kaisa; Kløve, Bjørn

    2016-04-01

    Many types of boreal peatland ecosystems such as alkaline fens, aapa mires and Fennoscandia spring fens rely on the presence of groundwater. In these ecosystems groundwater creates unique conditions for flora and fauna by providing water, nutrients and constant water temperature enriching local biodiversity. The groundwater-peatland interactions and their dynamics are not, however, in many cases fully understood and their measurement and quantification is difficult due to highly heterogeneous structure of peatlands and large spatial extend of these ecosystems. Understanding of these interactions and their changes due to anthropogenic impact on groundwater resources would benefit the protection of the groundwater dependent peatlands. The groundwater-peatland interactions were investigated using the fully-integrated physically-based groundwater-surface water code HydroGeoSphere in a case study of the Kälväsvaara esker aquifer, Northern Finland. The Kälväsvaara is a geologically complex esker and it is surrounded by vast aapa mire system including alkaline and springs fens. In addition, numerous small springs occur in the discharge zone of the esker. In order to quantify groundwater-peatland interactions a simple steady-state model was built and results were evaluated using expected trends and field measurements. The employed model reproduced relatively well spatially distributed hydrological variables such as soil water content, water depths and groundwater-surface water exchange fluxes within the wetland and esker areas. The wetlands emerged in simulations as a result of geological and topographical conditions. They could be identified by high saturation levels at ground surface and by presence of shallow ponded water over some areas. The model outputs exhibited also strong surface water-groundwater interactions in some parts of the aapa system. These areas were noted to be regions of substantial diffusive groundwater discharge by the earlier studies. In

  8. Prototyping an online wetland ecosystem services model using open model sharing standards

    Science.gov (United States)

    Feng, M.; Liu, S.; Euliss, N.H.; Young, Caitlin; Mushet, D.M.

    2011-01-01

    Great interest currently exists for developing ecosystem models to forecast how ecosystem services may change under alternative land use and climate futures. Ecosystem services are diverse and include supporting services or functions (e.g., primary production, nutrient cycling), provisioning services (e.g., wildlife, groundwater), regulating services (e.g., water purification, floodwater retention), and even cultural services (e.g., ecotourism, cultural heritage). Hence, the knowledge base necessary to quantify ecosystem services is broad and derived from many diverse scientific disciplines. Building the required interdisciplinary models is especially challenging as modelers from different locations and times may develop the disciplinary models needed for ecosystem simulations, and these models must be identified and made accessible to the interdisciplinary simulation. Additional difficulties include inconsistent data structures, formats, and metadata required by geospatial models as well as limitations on computing, storage, and connectivity. Traditional standalone and closed network systems cannot fully support sharing and integrating interdisciplinary geospatial models from variant sources. To address this need, we developed an approach to openly share and access geospatial computational models using distributed Geographic Information System (GIS) techniques and open geospatial standards. We included a means to share computational models compliant with Open Geospatial Consortium (OGC) Web Processing Services (WPS) standard to ensure modelers have an efficient and simplified means to publish new models. To demonstrate our approach, we developed five disciplinary models that can be integrated and shared to simulate a few of the ecosystem services (e.g., water storage, waterfowl breeding) that are provided by wetlands in the Prairie Pothole Region (PPR) of North America.

  9. Integrated landscape-based approach of remote sensing, GIS, and physical modelling to study the hydrological connectivity of wetlands to the downstream water: progress and challenge

    Science.gov (United States)

    Yeo, I. Y.

    2015-12-01

    We report the recent progress on our effort to improve the mapping of wetland dynamics and the modelling of its functioning and hydrological connection to the downstream waters. Our study focused on the Coastal Plain of the Chesapeake Bay Watershed (CBW), the Delmarva Peninsula, where the most of wetlands in CBW are densely distributed. The wetland ecosystem plays crucial roles in improving water quality and ecological integrity for the downstream waters and the Chesapeake Bay, and headwater wetlands in the region, such as Delmarva Bay, are now subject to the legal protection under the Clean Water Rules. We developed new wetland maps using time series Landsat images and a highly accurate LiDAR map over last 30 years. These maps show the changes in surface water fraction at a 30-m grid cell at annual time scale. Using GIS, we analyse these maps to characterize changing dynamics of wetland inundation due to the physical environmental factors (e.g., weather variability, tide) and assessed the hydrological connection of wetlands to the downstream water at the watershed scale. Focusing on the two adjacent watersheds in the upper region of the Choptank River Basin, we study how wetland inundation dynamics and the hydrologic linkage of wetlands to downstream water would vary by the local hydrogeological setting and attempt to identify the key landscape factors affecting the wetland ecosystems and functioning. We then discuss the potential of using remote sensing products to improve the physical modelling of wetlands from our experience with SWAT (Soil and Water Assessment Tool).

  10. Uncertainties in modelling CH4 emissions from northern wetlands in glacial climates: the role of vegetation parameters

    Directory of Open Access Journals (Sweden)

    J. van Huissteden

    2011-10-01

    Full Text Available Marine Isotope Stage 3 (MIS 3 interstadials are marked by a sharp increase in the atmospheric methane (CH4 concentration, as recorded in ice cores. Wetlands are assumed to be the major source of this CH4, although several other hypotheses have been advanced. Modelling of CH4 emissions is crucial to quantify CH4 sources for past climates. Vegetation effects are generally highly generalized in modelling past and present-day CH4 fluxes, but should not be neglected. Plants strongly affect the soil-atmosphere exchange of CH4 and the net primary production of the vegetation supplies organic matter as substrate for methanogens. For modelling past CH4 fluxes from northern wetlands, assumptions on vegetation are highly relevant since paleobotanical data indicate large differences in Last Glacial (LG wetland vegetation composition as compared to modern wetland vegetation. Besides more cold-adapted vegetation, Sphagnum mosses appear to be much less dominant during large parts of the LG than at present, which particularly affects CH4 oxidation and transport. To evaluate the effect of vegetation parameters, we used the PEATLAND-VU wetland CO2/CH4 model to simulate emissions from wetlands in continental Europe during LG and modern climates. We tested the effect of parameters influencing oxidation during plant transport (fox, vegetation net primary production (NPP, parameter symbol Pmax, plant transport rate (Vtransp, maximum rooting depth (Zroot and root exudation rate (fex. Our model results show that modelled CH4 fluxes are sensitive to fox and Zroot in particular. The effects of Pmax, Vtransp and fex are of lesser relevance. Interactions with water table modelling are significant for Vtransp. We conducted experiments with different wetland vegetation types for Marine Isotope Stage 3 (MIS 3 stadial and interstadial climates and the present-day climate, by coupling PEATLAND-VU to high resolution climate model simulations for Europe. Experiments assuming

  11. Uncertainties in modelling CH4 emissions from northern wetlands in glacial climates: the role of vegetation parameters

    Science.gov (United States)

    Berrittella, C.; van Huissteden, J.

    2011-10-01

    Marine Isotope Stage 3 (MIS 3) interstadials are marked by a sharp increase in the atmospheric methane (CH4) concentration, as recorded in ice cores. Wetlands are assumed to be the major source of this CH4, although several other hypotheses have been advanced. Modelling of CH4 emissions is crucial to quantify CH4 sources for past climates. Vegetation effects are generally highly generalized in modelling past and present-day CH4 fluxes, but should not be neglected. Plants strongly affect the soil-atmosphere exchange of CH4 and the net primary production of the vegetation supplies organic matter as substrate for methanogens. For modelling past CH4 fluxes from northern wetlands, assumptions on vegetation are highly relevant since paleobotanical data indicate large differences in Last Glacial (LG) wetland vegetation composition as compared to modern wetland vegetation. Besides more cold-adapted vegetation, Sphagnum mosses appear to be much less dominant during large parts of the LG than at present, which particularly affects CH4 oxidation and transport. To evaluate the effect of vegetation parameters, we used the PEATLAND-VU wetland CO2/CH4 model to simulate emissions from wetlands in continental Europe during LG and modern climates. We tested the effect of parameters influencing oxidation during plant transport (fox), vegetation net primary production (NPP, parameter symbol Pmax), plant transport rate (Vtransp), maximum rooting depth (Zroot) and root exudation rate (fex). Our model results show that modelled CH4 fluxes are sensitive to fox and Zroot in particular. The effects of Pmax, Vtransp and fex are of lesser relevance. Interactions with water table modelling are significant for Vtransp. We conducted experiments with different wetland vegetation types for Marine Isotope Stage 3 (MIS 3) stadial and interstadial climates and the present-day climate, by coupling PEATLAND-VU to high resolution climate model simulations for Europe. Experiments assuming dominance of

  12. Multi-state succession in wetlands: a novel use of state and transition models

    Science.gov (United States)

    Zweig, Christa L.; Kitchens, Wiley M.

    2009-01-01

    The complexity of ecosystems and mechanisms of succession are often simplified by linear and mathematical models used to understand and predict system behavior. Such models often do not incorporate multivariate, nonlinear feedbacks in pattern and process that include multiple scales of organization inherent within real-world systems. Wetlands are ecosystems with unique, nonlinear patterns of succession due to the regular, but often inconstant, presence of water on the landscape. We develop a general, nonspatial state and transition (S and T) succession conceptual model for wetlands and apply the general framework by creating annotated succession/management models and hypotheses for use in impact analysis on a portion of an imperiled wetland. The S and T models for our study area, Water Conservation Area 3A South (WCA3), Florida, USA, included hydrologic and peat depth values from multivariate analyses and classification and regression trees. We used the freeware Vegetation Dynamics Development Tool as an exploratory application to evaluate our S and T models with different management actions (equal chance [a control condition], deeper conditions, dry conditions, and increased hydrologic range) for three communities: slough, sawgrass (Cladium jamaicense), and wet prairie. Deeper conditions and increased hydrologic range behaved similarly, with the transition of community states to deeper states, particularly for sawgrass and slough. Hydrology is the primary mechanism for multi-state transitions within our study period, and we show both an immediate and lagged effect on vegetation, depending on community state. We consider these S and T succession models as a fraction of the framework for the Everglades. They are hypotheses for use in adaptive management, represent the community response to hydrology, and illustrate which aspects of hydrologic variability are important to community structure. We intend for these models to act as a foundation for further restoration

  13. Modelling of artificial radioactivity migration in environment: a survey

    International Nuclear Information System (INIS)

    Bignoli, G.; Bertozzi, G.

    1979-01-01

    The aim of this report is to present a compilation and description of models to assess the environmental behaviour and effects of accidental and routine releases of artificial radioactivity from nuclear power facilities. About 60 models are described and a card is given for each one, to indicate in summarized form its features and data content. This collection is intended to help in developing specific personal models by assembling different parts chosen among the most suitable ones of different models of various degrees of sophistication

  14. Incorporation of oxygen contribution by plant roots into classical dissolved oxygen deficit model for a subsurface flow treatment wetland.

    Science.gov (United States)

    Bezbaruah, Achintya N; Zhang, Tian C

    2009-01-01

    It has been long established that plants play major roles in a treatment wetland. However, the role of plants has not been incorporated into wetland models. This study tries to incorporate wetland plants into a biochemical oxygen demand (BOD) model so that the relative contributions of the aerobic and anaerobic processes to meeting BOD can be quantitatively determined. The classical dissolved oxygen (DO) deficit model has been modified to simulate the DO curve for a field subsurface flow constructed wetland (SFCW) treating municipal wastewater. Sensitivities of model parameters have been analyzed. Based on the model it is predicted that in the SFCW under study about 64% BOD are degraded through aerobic routes and 36% is degraded anaerobically. While not exhaustive, this preliminary work should serve as a pointer for further research in wetland model development and to determine the values of some of the parameters used in the modified DO deficit and associated BOD model. It should be noted that nitrogen cycle and effects of temperature have not been addressed in these models for simplicity of model formulation. This paper should be read with this caveat in mind.

  15. Modelling hydrologic and hydrodynamic processes in basins with large semi-arid wetlands

    Science.gov (United States)

    Fleischmann, Ayan; Siqueira, Vinícius; Paris, Adrien; Collischonn, Walter; Paiva, Rodrigo; Pontes, Paulo; Crétaux, Jean-François; Bergé-Nguyen, Muriel; Biancamaria, Sylvain; Gosset, Marielle; Calmant, Stephane; Tanimoun, Bachir

    2018-06-01

    Hydrological and hydrodynamic models are core tools for simulation of large basins and complex river systems associated to wetlands. Recent studies have pointed towards the importance of online coupling strategies, representing feedbacks between floodplain inundation and vertical hydrology. Especially across semi-arid regions, soil-floodplain interactions can be strong. In this study, we included a two-way coupling scheme in a large scale hydrological-hydrodynamic model (MGB) and tested different model structures, in order to assess which processes are important to be simulated in large semi-arid wetlands and how these processes interact with water budget components. To demonstrate benefits from this coupling over a validation case, the model was applied to the Upper Niger River basin encompassing the Niger Inner Delta, a vast semi-arid wetland in the Sahel Desert. Simulation was carried out from 1999 to 2014 with daily TMPA 3B42 precipitation as forcing, using both in-situ and remotely sensed data for calibration and validation. Model outputs were in good agreement with discharge and water levels at stations both upstream and downstream of the Inner Delta (Nash-Sutcliffe Efficiency (NSE) >0.6 for most gauges), as well as for flooded areas within the Delta region (NSE = 0.6; r = 0.85). Model estimates of annual water losses across the Delta varied between 20.1 and 30.6 km3/yr, while annual evapotranspiration ranged between 760 mm/yr and 1130 mm/yr. Evaluation of model structure indicated that representation of both floodplain channels hydrodynamics (storage, bifurcations, lateral connections) and vertical hydrological processes (floodplain water infiltration into soil column; evapotranspiration from soil and vegetation and evaporation of open water) are necessary to correctly simulate flood wave attenuation and evapotranspiration along the basin. Two-way coupled models are necessary to better understand processes in large semi-arid wetlands. Finally, such coupled

  16. Estimation model of soil freeze-thaw erosion in Silingco watershed wetland of Northern Tibet.

    Science.gov (United States)

    Kong, Bo; Yu, Huan

    2013-01-01

    The freeze-thaw (FT) erosion is a type of soil erosion like water erosion and wind erosion. Limited by many factors, the grading evaluation of soil FT erosion quantities is not well studied. Based on the comprehensive analysis of the evaluation indices of soil FT erosion, we for the first time utilized the sensitivity of microwave remote sensing technology to soil moisture for identification of FT state. We established an estimation model suitable to evaluate the soil FT erosion quantity in Silingco watershed wetland of Northern Tibet using weighted summation method of six impact factors including the annual FT cycle days, average diurnal FT phase-changed water content, average annual precipitation, slope, aspect, and vegetation coverage. Finally, with the support of GIS, we classified soil FT erosion quantity in Silingco watershed wetland. The results showed that soil FT erosion are distributed in broad areas of Silingco watershed wetland. Different soil FT erosions with different intensities have evidently different spatial and geographical distributions.

  17. Quantifying geographic variation in the climatic drivers of midcontinent wetlands with a spatially varying coefficient model.

    Science.gov (United States)

    Roy, Christian

    2015-01-01

    The wetlands in the Prairie Pothole Region and in the Great Plains are notorious for their sensitivity to weather variability. These wetlands have been the focus of considerable attention because of their ecological importance and because of the expected impact of climate change. Few models in the literature, however, take into account spatial variation in the importance of wetland drivers. This is surprising given the importance spatial heterogeneity in geomorphology and climatic conditions have in the region. In this paper, I use spatially-varying coefficients to assess the variation in ecological drivers in a number of ponds observed over a 50-year period (1961-2012). I included the number of ponds observed the year before on a log scale, the log of total precipitation, and mean maximum temperature during the four previous seasons as explanatory variables. I also included a temporal component to capture change in the number of ponds due to anthropogenic disturbance. Overall, fall and spring precipitation were most important in pond abundance in the west, whereas winter and summer precipitation were the most important drivers in the east. The ponds in the east of the survey area were also more dependent on pond abundance during the previous year than those in the west. Spring temperature during the previous season influenced pond abundance; while the temperature during the other seasons had a limited effect. The ponds in the southwestern part of the survey area have been increasing independently of climatic conditions, whereas the ponds in the northeast have been steadily declining. My results underline the importance of accounting the spatial heterogeneity in environmental drivers, when working at large spatial scales. In light of my results, I also argue that assessing the impacts of climate change on wetland abundance in the spring, without more accurate climatic forecasting, will be difficult.

  18. Quantifying geographic variation in the climatic drivers of midcontinent wetlands with a spatially varying coefficient model.

    Directory of Open Access Journals (Sweden)

    Christian Roy

    Full Text Available The wetlands in the Prairie Pothole Region and in the Great Plains are notorious for their sensitivity to weather variability. These wetlands have been the focus of considerable attention because of their ecological importance and because of the expected impact of climate change. Few models in the literature, however, take into account spatial variation in the importance of wetland drivers. This is surprising given the importance spatial heterogeneity in geomorphology and climatic conditions have in the region. In this paper, I use spatially-varying coefficients to assess the variation in ecological drivers in a number of ponds observed over a 50-year period (1961-2012. I included the number of ponds observed the year before on a log scale, the log of total precipitation, and mean maximum temperature during the four previous seasons as explanatory variables. I also included a temporal component to capture change in the number of ponds due to anthropogenic disturbance. Overall, fall and spring precipitation were most important in pond abundance in the west, whereas winter and summer precipitation were the most important drivers in the east. The ponds in the east of the survey area were also more dependent on pond abundance during the previous year than those in the west. Spring temperature during the previous season influenced pond abundance; while the temperature during the other seasons had a limited effect. The ponds in the southwestern part of the survey area have been increasing independently of climatic conditions, whereas the ponds in the northeast have been steadily declining. My results underline the importance of accounting the spatial heterogeneity in environmental drivers, when working at large spatial scales. In light of my results, I also argue that assessing the impacts of climate change on wetland abundance in the spring, without more accurate climatic forecasting, will be difficult.

  19. Measurement and modelling of evaporation from a coastal wetland in Maputaland, South Africa

    Directory of Open Access Journals (Sweden)

    A. D. Clulow

    2012-09-01

    Full Text Available The surface renewal (SR method was used to determine the long-term (12 months total evaporation (ET from the Mfabeni Mire with calibration using eddy covariance during two window periods of approximately one week each. The SR method was found to be inexpensive, reliable and with low power requirements for unattended operation.

    Despite maximum ET rates of up to 6.0 mm day−1, the average summer (October to March ET was lower (3.2 mm day−1 due to early morning cloud cover that persisted until nearly midday at times. This reduced the daily available energy, and the ET was lower than expected despite the available water and high average wind speeds. In winter (May to September, there was less cloud cover but the average ET was only 1.8 mm day−1 due to plant senescence. In general ET was suppressed by the inflow of humid air (low vapour pressure deficit and the comparatively low leaf area index of the wetland vegetation. The accumulated ET over 12 months was 900 mm. Daily ET estimates were compared to the Priestley-Taylor model results and a calibration α = 1.0 (R2 = 0.96 was obtained for the site. A monthly crop factor (Kc was determined for the standardised FAO-56 Penman-Monteith. However, Kc was variable in some months and should be used with caution for daily ET modelling.

    These results represent not only some of the first long-term measurements of ET from a wetland in southern Africa, but also one of the few studies of actual ET in a subtropical peatland in the Southern Hemisphere. The study provides wetland ecologists and hydrologists with guidelines for the use of two internationally applied models for the estimation of wetland ET within a coastal, subtropical environment and shows that wetlands are not necessarily high water users.

  20. Integrated Modeling of Groundwater and Surface Water Interactions in a Manmade Wetland

    Directory of Open Access Journals (Sweden)

    Guobiao Huang Gour-Tsyh Yeh

    2012-01-01

    Full Text Available A manmade pilot wetland in south Florida, the Everglades Nutrient Removal (ENR project, was modeled with a physics-based integrated approach using WASH123D (Yeh et al. 2006. Storm water is routed into the treatment wetland for phosphorus removal by plant and sediment uptake. It overlies a highly permeable surficial groundwater aquifer. Strong surface water and groundwater interactions are a key component of the hydrologic processes. The site has extensive field measurement and monitoring tools that provide point scale and distributed data on surface water levels, groundwater levels, and the physical range of hydraulic parameters and hydrologic fluxes. Previous hydrologic and hydrodynamic modeling studies have treated seepage losses empirically by some simple regression equations and, only surface water flows are modeled in detail. Several years of operational data are available and were used in model historical matching and validation. The validity of a diffusion wave approximation for two-dimensional overland flow (in the region with very flat topography was also tested. The uniqueness of this modeling study is notable for (1 the point scale and distributed comparison of model results with observed data; (2 model parameters based on available field test data; and (3 water flows in the study area include two-dimensional overland flow, hydraulic structures/levees, three-dimensional subsurface flow and one-dimensional canal flow and their interactions. This study demonstrates the need and the utility of a physics-based modeling approach for strong surface water and groundwater interactions.

  1. Risk prediction model: Statistical and artificial neural network approach

    Science.gov (United States)

    Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

    2017-04-01

    Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

  2. Modelling of word usage frequency dynamics using artificial neural network

    International Nuclear Information System (INIS)

    Maslennikova, Yu S; Bochkarev, V V; Voloskov, D S

    2014-01-01

    In this paper the method for modelling of word usage frequency time series is proposed. An artificial feedforward neural network was used to predict word usage frequencies. The neural network was trained using the maximum likelihood criterion. The Google Books Ngram corpus was used for the analysis. This database provides a large amount of data on frequency of specific word forms for 7 languages. Statistical modelling of word usage frequency time series allows finding optimal fitting and filtering algorithm for subsequent lexicographic analysis and verification of frequency trend models

  3. Hydrological Modelling of Small Scale Processes in a Wetland Habitat

    DEFF Research Database (Denmark)

    Johansen, Ole; Jensen, Jacob Birk; Pedersen, Morten Lauge

    2009-01-01

    Numerical modelling of the hydrology in a Danish rich fen area has been conducted. By collecting various data in the field the model has been successfully calibrated and the flow paths as well as the groundwater discharge distribution have been simulated in details. The results of this work have...... shown that distributed numerical models can be applied to local scale problems and that natural springs, ditches, the geological conditions as well as the local topographic variations have a significant influence on the flow paths in the examined rich fen area....

  4. Artificial intelligence support for scientific model-building

    Science.gov (United States)

    Keller, Richard M.

    1992-01-01

    Scientific model-building can be a time-intensive and painstaking process, often involving the development of large and complex computer programs. Despite the effort involved, scientific models cannot easily be distributed and shared with other scientists. In general, implemented scientific models are complex, idiosyncratic, and difficult for anyone but the original scientific development team to understand. We believe that artificial intelligence techniques can facilitate both the model-building and model-sharing process. In this paper, we overview our effort to build a scientific modeling software tool that aids the scientist in developing and using models. This tool includes an interactive intelligent graphical interface, a high-level domain specific modeling language, a library of physics equations and experimental datasets, and a suite of data display facilities.

  5. Evaluation of articulation simulation system using artificial maxillectomy models.

    Science.gov (United States)

    Elbashti, M E; Hattori, M; Sumita, Y I; Taniguchi, H

    2015-09-01

    Acoustic evaluation is valuable for guiding the treatment of maxillofacial defects and determining the effectiveness of rehabilitation with an obturator prosthesis. Model simulations are important in terms of pre-surgical planning and pre- and post-operative speech function. This study aimed to evaluate the acoustic characteristics of voice generated by an articulation simulation system using a vocal tract model with or without artificial maxillectomy defects. More specifically, we aimed to establish a speech simulation system for maxillectomy defect models that both surgeons and maxillofacial prosthodontists can use in guiding treatment planning. Artificially simulated maxillectomy defects were prepared according to Aramany's classification (Classes I-VI) in a three-dimensional vocal tract plaster model of a subject uttering the vowel /a/. Formant and nasalance acoustic data were analysed using Computerized Speech Lab and the Nasometer, respectively. Formants and nasalance of simulated /a/ sounds were successfully detected and analysed. Values of Formants 1 and 2 for the non-defect model were 675.43 and 976.64 Hz, respectively. Median values of Formants 1 and 2 for the defect models were 634.36 and 1026.84 Hz, respectively. Nasalance was 11% in the non-defect model, whereas median nasalance was 28% in the defect models. The results suggest that an articulation simulation system can be used to help surgeons and maxillofacial prosthodontists to plan post-surgical defects that will be facilitate maxillofacial rehabilitation. © 2015 John Wiley & Sons Ltd.

  6. Linking hydrology, ecosystem function, and livelihood sustainability in African papyrus wetlands using a Bayesian Network Model

    Science.gov (United States)

    van Dam, A.; Gettel, G. M.; Kipkemboi, J.; Rahman, M. M.

    2011-12-01

    Papyrus wetlands in East Africa provide ecosystem services supporting the livelihoods of millions but are rapidly degrading due to economic development. For ecosystem conservation, an integrated understanding of the natural and social processes driving ecosystem change is needed. This research focuses on integrating the causal relationships between hydrology, ecosystem function, and livelihood sustainability in Nyando wetland, western Kenya. Livelihood sustainability is based on ecosystem services that include plant and animal harvest for building material and food, conversion of wetlands to crop and grazing land, water supply, and water quality regulation. Specific objectives were: to integrate studies of hydrology, ecology, and livelihood activities using a Bayesian Network (BN) model and include stakeholder involvement in model development. The BN model (Netica 4.16) had 35 nodes with seven decision nodes describing demography, economy, papyrus market, and rainfall, and two target nodes describing ecosystem function (defined by groundwater recharge, nutrient and sediment retention, and biodiversity) and livelihood sustainability (drinking water supply, crop production, livestock production, and papyrus yield). The conditional probability tables were populated using results of ecohydrological and socio-economic field work and consultations with stakeholders. The model was evaluated for an average year with decision node probabilities set according to data from research, expert opinion, and stakeholders' views. Then, scenarios for dry and wet seasons and for economic development (low population growth and unemployment) and policy development (more awareness of wetland value) were evaluated. In an average year, the probability for maintaining a "good" level of sediment and nutrient retention functions, groundwater recharge, and biodiversity was about 60%. ("Good" is defined by expert opinion based on ongoing field research.) In the dry season, the probability was

  7. Simulating Microwave Scattering for Wetland Vegetation in Poyang Lake, Southeast China, Using a Coherent Scattering Model

    Directory of Open Access Journals (Sweden)

    Jingjuan Liao

    2015-07-01

    Full Text Available We developed a polarimetric coherent electromagnetic scattering model for Poyang Lake wetland vegetation. Realistic canopy structures including curved leaves and the lodging situation of the vegetation were taken into account, and the situation at the ground surface was established using an Advanced Integral Equation Model combined with Oh’s 2002 model. This new model can reasonably describe the coherence effect caused by the phase differences of the electromagnetic fields scattered from different particles by different scattering mechanisms. We obtained good agreement between the modeling results and C-band data from the Radarsat-2 satellite. A simulation of scattering from the vegetation in Poyang Lake showed that direct vegetation scattering and the single-ground-bounce mechanism are the dominant scattering mechanisms in the C-band and L-band, while the effects of the double-ground-bounce mechanism are very small. We note that the curvature of the leaves and the lodging characteristics of the vegetation cannot be ignored in the modeling process. Monitoring soil moisture in the Poyang Lake wetland with the C-band data was not feasible because of the density and depth of Poyang Lake vegetation. When the density of Poyang Lake Carex increases, the backscattering coefficient either decreases or remains stable.

  8. Evolvable mathematical models: A new artificial Intelligence paradigm

    Science.gov (United States)

    Grouchy, Paul

    We develop a novel Artificial Intelligence paradigm to generate autonomously artificial agents as mathematical models of behaviour. Agent/environment inputs are mapped to agent outputs via equation trees which are evolved in a manner similar to Symbolic Regression in Genetic Programming. Equations are comprised of only the four basic mathematical operators, addition, subtraction, multiplication and division, as well as input and output variables and constants. From these operations, equations can be constructed that approximate any analytic function. These Evolvable Mathematical Models (EMMs) are tested and compared to their Artificial Neural Network (ANN) counterparts on two benchmarking tasks: the double-pole balancing without velocity information benchmark and the challenging discrete Double-T Maze experiments with homing. The results from these experiments show that EMMs are capable of solving tasks typically solved by ANNs, and that they have the ability to produce agents that demonstrate learning behaviours. To further explore the capabilities of EMMs, as well as to investigate the evolutionary origins of communication, we develop NoiseWorld, an Artificial Life simulation in which interagent communication emerges and evolves from initially noncommunicating EMM-based agents. Agents develop the capability to transmit their x and y position information over a one-dimensional channel via a complex, dialogue-based communication scheme. These evolved communication schemes are analyzed and their evolutionary trajectories examined, yielding significant insight into the emergence and subsequent evolution of cooperative communication. Evolved agents from NoiseWorld are successfully transferred onto physical robots, demonstrating the transferability of EMM-based AIs from simulation into physical reality.

  9. Super capacitor modeling with artificial neural network (ANN)

    Energy Technology Data Exchange (ETDEWEB)

    Marie-Francoise, J.N.; Gualous, H.; Berthon, A. [Universite de Franche-Comte, Lab. en Electronique, Electrotechnique et Systemes (L2ES), UTBM, INRETS (LRE T31) 90 - Belfort (France)

    2004-07-01

    This paper presents super-capacitors modeling using Artificial Neural Network (ANN). The principle consists on a black box nonlinear multiple inputs single output (MISO) model. The system inputs are temperature and current, the output is the super-capacitor voltage. The learning and the validation of the ANN model from experimental charge and discharge of super-capacitor establish the relationship between inputs and output. The learning and the validation of the ANN model use experimental results of 2700 F, 3700 F and a super-capacitor pack. Once the network is trained, the ANN model can predict the super-capacitor behaviour with temperature variations. The update parameters of the ANN model are performed thanks to Levenberg-Marquardt method in order to minimize the error between the output of the system and the predicted output. The obtained results with the ANN model of super-capacitor and experimental ones are in good agreement. (authors)

  10. Modeling the impacts of wetland restoration in former cranberry farms on nitrogen removal

    Science.gov (United States)

    In population-dense Massachusetts (USA) acquiring historical wetlands for ecological restoration efforts can be difficult and expensive. Retiring cranberry bogs create a rare opportunity to restore historical wetlands. Environmental managers face important decisions about how to ...

  11. Diatom-based models for inferring water chemistry and hydrology in temporary depressional wetlands

    CSIR Research Space (South Africa)

    Riato, L

    2017-08-01

    Full Text Available Information on the response of temporary depressional wetland diatoms to human-induced disturbances is a limited and important component for the development of temporary wetland biological assessments in human-modified landscapes. Establishing a...

  12. Hydroecological impacts of climate change modelled for a lowland UK wetland

    Science.gov (United States)

    House, Andrew; Acreman, Mike; Sorensen, James; Thompson, Julian

    2015-04-01

    Conservation management of wetlands often rests on modifying hydrological functions to establish or maintain desired flora and fauna. Hence the ability to predict the impacts of climate change is highly beneficial. Here, the physically based, distributed model MIKE SHE was used to simulate hydrology for the Lambourn Observatory at Boxford, UK. This comprises a 10 ha lowland riparian wetland protected for conservation, where the degree of variability in the peat, gravel and chalk geology has clouded hydrological understanding. Notably, a weathered layer on the chalk aquifer surface seals it from overlying deposits, yet is highly spatially heterogeneous. Long-term monitoring yielded observations of groundwater and surface water levels for model calibration and validation. Simulated results were consistent with observed data and reproduced the effects of seasonal fluctuations and in-channel macrophyte growth. The adjacent river and subsidiary channel were found to act as head boundaries, exerting a general control on water levels across the site. Discrete areas of groundwater upwellings caused raised water levels at distinct locations within the wetland. These were concurrent to regions where the weathered chalk layer is absent. To assess impacts of climate change, outputs from the UK Climate Projections 2009 ensemble of global climate models for the 2080s are used to obtain monthly percentage changes in climate variables. Changes in groundwater levels were taken from a regional model of the Chalk aquifer. Values of precipitation and evapotranspiration were seen to increase, whilst groundwater levels decreased, resulting in the greater dominance of precipitation. The discrete areas of groundwater upwelling were seen to diminish or disappear. Simulated water levels were linked to specific requirements of wetland plants using water table depth zone diagrams. Increasing depth of winter and summer groundwater levels leads to a loss of Glyceria maxima and Phragmites

  13. Assessing wetland loss impacts on watershed hydrology using an improved modeling approach

    Science.gov (United States)

    Despite the importance of wetland impacts on water cycling, the Chesapeake Bay Watershed (CBW) has experienced significant wetland losses. The resultant environmental degradation has not been fully characterized. Our aim is to assess wetland loss impacts on watershed hydrology for an agricultural wa...

  14. Modeling the Hydrologic Processes of a Depressional Forested Wetland in South Carolina, U.S.A.

    Science.gov (United States)

    Ge Sun; Timothy Callahan; Jennifer E. Pyzoha; Carl C. Trettin; Devendra M. Amatya

    2004-01-01

    Depressional forested wetlands or geographically isolated wetlands such as cypress swamps and Carolina bays are common land features in the Atlantic Coastal Plain of the southeastern US. Those wetlands play important roles in providing wildlife habitats, water quality improvement, and carbon sequestration. Great stresses have been imposed on those important ecosystems...

  15. Global warming and prairie wetlands

    International Nuclear Information System (INIS)

    Poiani, K.A.; Johnson, W.C.

    1991-01-01

    In this article, the authors discuss current understanding and projections of global warming; review wetland vegetation dynamics to establish the strong relationship among climate, wetland hydrology, vegetation patterns and waterfowl habitat; discuss the potential effects of a greenhouse warming on these relationships; and illustrate the potential effects of climate change on wetland habitat by using a simulation model

  16. Model of hydrological behaviour of the anthropized semiarid wetland of Las Tablas de Daimiel National Park (Spain) based on surface water-groundwater interactions

    Science.gov (United States)

    Aguilera, H.; Castaño, S.; Moreno, L.; Jiménez-Hernández, M. E.; de la Losa, A.

    2013-05-01

    Las Tablas de Daimiel National Park (TDNP) in Spain is one of the most important semiarid wetlands of the Mediterranean area. The inversion of the regional groundwater flow, primarily due to overexploitation and inadequate aquifer management, has led to degradation. The system has turned from a groundwater discharge zone into a recharge zone, and has remained mostly dry since the 1980s. High heterogeneity and complexity, enhanced by anthropogenic management action, hampers prediction of the surface-groundwater system response to flooding events. This study analyses these interactions and provides empirical evidence to define a conceptual model of flooding-infiltration-groundwater dynamics through the application of a few simple analysis tools to basic hydrological data. Relevant surface water-groundwater interactions are mainly localized in the left (west) margin of TDNP, as confirmed by the fast responses to flooding observed in the hydrochemic, hydrodynamic and isotopic data. During drying periods, small artificial and/or low-flow natural floods are followed by infiltration of evaporated poor-quality ponding water into saline low-permeability layers. The results allow an improved understanding of the hydrological behaviour essential to support efficient management practices. The relative simplicity of the methodology allows for its application in other similar complex groundwater-linked wetlands where detailed knowledge of local geology is still absent.

  17. An analysis of urban collisions using an artificial intelligence model.

    Science.gov (United States)

    Mussone, L; Ferrari, A; Oneta, M

    1999-11-01

    Traditional studies on road accidents estimate the effect of variables (such as vehicular flows, road geometry, vehicular characteristics), and the calculation of the number of accidents. A descriptive statistical analysis of the accidents (those used in the model) over the period 1992-1995 is proposed. The paper describes an alternative method based on the use of artificial neural networks (ANN) in order to work out a model that relates to the analysis of vehicular accidents in Milan. The degree of danger of urban intersections using different scenarios is quantified by the ANN model. Methodology is the first result, which allows us to tackle the modelling of urban vehicular accidents by the innovative use of ANN. Other results deal with model outputs: intersection complexity may determine a higher accident index depending on the regulation of intersection. The highest index for running over of pedestrian occurs at non-signalised intersections at night-time.

  18. Qualitative models to predict impacts of human interventions in a wetland ecosystem

    Directory of Open Access Journals (Sweden)

    S. Loiselle

    2002-07-01

    Full Text Available The large shallow wetlands that dominate much of the South American continent are rich in biodiversity and complexity. Many of these undamaged ecosystems are presently being examined for their potential economic utility, putting pressure on local authorities and the conservation community to find ways of correctly utilising the available natural resources without compromising the ecosystem functioning and overall integrity. Contrary to many northern hemisphere ecosystems, there have been little long term ecological studies of these systems, leading to a lack of quantitative data on which to construct ecological or resource use models. As a result, decision makers, even well meaning ones, have difficulty in determining if particular economic activities can potentially cause significant damage to the ecosystem and how one should go about monitoring the impacts of such activities. While the direct impact of many activities is often known, the secondary indirect impacts are usually less clear and can depend on local ecological conditions.

    The use of qualitative models is a helpful tool to highlight potential feedback mechanisms and secondary effects of management action on ecosystem integrity. The harvesting of a single, apparently abundant, species can have indirect secondary effects on key trophic and abiotic compartments. In this paper, loop model analysis is used to qualitatively examine secondary effects of potential economic activities in a large wetland area in northeast Argentina, the Esteros del Ibera. Based on interaction with local actors together with observed ecological information, loop models were constructed to reflect relationships between biotic and abiotic compartments. A series of analyses were made to study the effect of different economic scenarios on key ecosystem compartments. Important impacts on key biotic compartments (phytoplankton, zooplankton, ichthyofauna, aquatic macrophytes and on the abiotic environment

  19. Conceptual Models for Ecosystem Management through the Participation of Local Social Actors: the Río Cruces Wetland Conflict

    Directory of Open Access Journals (Sweden)

    Luisa E. Delgado

    2009-06-01

    Full Text Available In 2004, the emigration and death of black-necked swans (Cygnus melancoryphus from the Río Cruces wetland (Valdivia, Chile triggered one of the largest ecosocial conflicts in Chilean history. The main local social actors of this still unsolved conflict are the Chilean government, a pulp-mill company, and a local nongovernmental organization. The central issues of the conflict are disagreement over the reason for the swans' migration, the need to restore the black-necked swan population in the wetland, and the relationship between economic development and wetland conservation. We applied a physical, ecological, and social system approach to generate conceptual or qualitative ecosystem models representing the perceptions of all social actors. Our results showed that each actor group perceived the ecosystem in a different and, in some cases, divergent way. Furthermore, all of them carried only partial representations of the wetland and the conflict. We linked all the models to generate an integrated view of the Río Cruces wetland ecosystem. We propose that this approach can be replicated as a tool for generating synthetic, integrated conceptual models of ecosystems, even in the presence of strong divergence and a lack of consensus among social actors.

  20. Developing Remote Sensing Products for Monitoring and Modeling Great Lakes Coastal Wetland Vulnerability to Climate Change and Land Use

    Science.gov (United States)

    Bourgeau-Chavez, L. L.; Miller, M. E.; Battaglia, M.; Banda, E.; Endres, S.; Currie, W. S.; Elgersma, K. J.; French, N. H. F.; Goldberg, D. E.; Hyndman, D. W.

    2014-12-01

    Spread of invasive plant species in the coastal wetlands of the Great Lakes is degrading wetland habitat, decreasing biodiversity, and decreasing ecosystem services. An understanding of the mechanisms of invasion is crucial to gaining control of this growing threat. To better understand the effects of land use and climatic drivers on the vulnerability of coastal zones to invasion, as well as to develop an understanding of the mechanisms of invasion, research is being conducted that integrates field studies, process-based ecosystem and hydrological models, and remote sensing. Spatial data from remote sensing is needed to parameterize the hydrological model and to test the outputs of the linked models. We will present several new remote sensing products that are providing important physiological, biochemical, and landscape information to parameterize and verify models. This includes a novel hybrid radar-optical technique to delineate stands of invasives, as well as natural wetland cover types; using radar to map seasonally inundated areas not hydrologically connected; and developing new algorithms to estimate leaf area index (LAI) using Landsat. A coastal map delineating wetland types including monocultures of the invaders (Typha spp. and Phragmites austrailis) was created using satellite radar (ALOS PALSAR, 20 m resolution) and optical data (Landsat 5, 30 m resolution) fusion from multiple dates in a Random Forests classifier. These maps provide verification of the integrated model showing areas at high risk of invasion. For parameterizing the hydrological model, maps of seasonal wetness are being developed using spring (wet) imagery and differencing that with summer (dry) imagery to detect the seasonally wet areas. Finally, development of LAI remote sensing high resolution algorithms for uplands and wetlands is underway. LAI algorithms for wetlands have not been previously developed due to the difficulty of a water background. These products are being used to

  1. Training Spiking Neural Models Using Artificial Bee Colony

    Science.gov (United States)

    Vazquez, Roberto A.; Garro, Beatriz A.

    2015-01-01

    Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy. PMID:25709644

  2. Predictive occurrence models for coastal wetland plant communities: delineating hydrologic response surfaces with multinomial logistic regression

    Science.gov (United States)

    Snedden, Gregg A.; Steyer, Gregory D.

    2013-01-01

    Understanding plant community zonation along estuarine stress gradients is critical for effective conservation and restoration of coastal wetland ecosystems. We related the presence of plant community types to estuarine hydrology at 173 sites across coastal Louisiana. Percent relative cover by species was assessed at each site near the end of the growing season in 2008, and hourly water level and salinity were recorded at each site Oct 2007–Sep 2008. Nine plant community types were delineated with k-means clustering, and indicator species were identified for each of the community types with indicator species analysis. An inverse relation between salinity and species diversity was observed. Canonical correspondence analysis (CCA) effectively segregated the sites across ordination space by community type, and indicated that salinity and tidal amplitude were both important drivers of vegetation composition. Multinomial logistic regression (MLR) and Akaike's Information Criterion (AIC) were used to predict the probability of occurrence of the nine vegetation communities as a function of salinity and tidal amplitude, and probability surfaces obtained from the MLR model corroborated the CCA results. The weighted kappa statistic, calculated from the confusion matrix of predicted versus actual community types, was 0.7 and indicated good agreement between observed community types and model predictions. Our results suggest that models based on a few key hydrologic variables can be valuable tools for predicting vegetation community development when restoring and managing coastal wetlands.

  3. Predictive occurrence models for coastal wetland plant communities: Delineating hydrologic response surfaces with multinomial logistic regression

    Science.gov (United States)

    Snedden, Gregg A.; Steyer, Gregory D.

    2013-02-01

    Understanding plant community zonation along estuarine stress gradients is critical for effective conservation and restoration of coastal wetland ecosystems. We related the presence of plant community types to estuarine hydrology at 173 sites across coastal Louisiana. Percent relative cover by species was assessed at each site near the end of the growing season in 2008, and hourly water level and salinity were recorded at each site Oct 2007-Sep 2008. Nine plant community types were delineated with k-means clustering, and indicator species were identified for each of the community types with indicator species analysis. An inverse relation between salinity and species diversity was observed. Canonical correspondence analysis (CCA) effectively segregated the sites across ordination space by community type, and indicated that salinity and tidal amplitude were both important drivers of vegetation composition. Multinomial logistic regression (MLR) and Akaike's Information Criterion (AIC) were used to predict the probability of occurrence of the nine vegetation communities as a function of salinity and tidal amplitude, and probability surfaces obtained from the MLR model corroborated the CCA results. The weighted kappa statistic, calculated from the confusion matrix of predicted versus actual community types, was 0.7 and indicated good agreement between observed community types and model predictions. Our results suggest that models based on a few key hydrologic variables can be valuable tools for predicting vegetation community development when restoring and managing coastal wetlands.

  4. Modeling the potential impacts of climate change on the water table level of selected forested wetlands in the southeastern United States

    OpenAIRE

    Zhu, Jie; Sun, Ge; Li, Wenhong; Zhang, Yu; Miao, Guofang; Noormets, Asko; McNulty, Steve G.; King, John S.; Kumar, Mukesh; Wang, Xuan

    2017-01-01

    The southeastern United States hosts extensive forested wetlands, providing ecosystem services including carbon sequestration, water quality improvement, groundwater recharge, and wildlife habitat. However, these wetland ecosystems are dependent on local climate and hydrology, and are therefore at risk due to climate and land use change. This study develops site-specific empirical hydrologic models for five forested wetlands with different characteristics by analyzing long-t...

  5. Application of Artificial Intelligence for Bridge Deterioration Model

    Directory of Open Access Journals (Sweden)

    Zhang Chen

    2015-01-01

    Full Text Available The deterministic bridge deterioration model updating problem is well established in bridge management, while the traditional methods and approaches for this problem require manual intervention. An artificial-intelligence-based approach was presented to self-updated parameters of the bridge deterioration model in this paper. When new information and data are collected, a posterior distribution was constructed to describe the integrated result of historical information and the new gained information according to Bayesian theorem, which was used to update model parameters. This AI-based approach is applied to the case of updating parameters of bridge deterioration model, which is the data collected from bridges of 12 districts in Shanghai from 2004 to 2013, and the results showed that it is an accurate, effective, and satisfactory approach to deal with the problem of the parameter updating without manual intervention.

  6. Transport modeling of sorbing tracers in artificial fractures

    International Nuclear Information System (INIS)

    Keum, Dong Kwon; Baik, Min Hoon; Park, Chung Kyun; Cho, Young Hwan; Hahn, Phil Soo.

    1998-02-01

    This study was performed as part of a fifty-man year attachment program between AECL (Atomic Energy Canada Limited) and KAERI. Three kinds of computer code, HDD, POMKAP and VAMKAP, were developed to predict transport of contaminants in fractured rock. MDDM was to calculate the mass transport of contaminants in a single fracture using a simple hydrodynamic dispersion diffusion model. POMKAP was to predict the mass transport of contaminants by a two-dimensional variable aperture model. In parallel with modeling, the validation of models was also performed through the analysis of the migration experimental data obtained in acrylic plastic and granite artificial fracture system at the Whiteshell laboratories, AECL, Canada. (author). 34 refs., 11 tabs., 76 figs

  7. Transport modeling of sorbing tracers in artificial fractures

    Energy Technology Data Exchange (ETDEWEB)

    Keum, Dong Kwon; Baik, Min Hoon; Park, Chung Kyun; Cho, Young Hwan; Hahn, Phil Soo

    1998-02-01

    This study was performed as part of a fifty-man year attachment program between AECL (Atomic Energy Canada Limited) and KAERI. Three kinds of computer code, HDD, POMKAP and VAMKAP, were developed to predict transport of contaminants in fractured rock. MDDM was to calculate the mass transport of contaminants in a single fracture using a simple hydrodynamic dispersion diffusion model. POMKAP was to predict the mass transport of contaminants by a two-dimensional variable aperture model. In parallel with modeling, the validation of models was also performed through the analysis of the migration experimental data obtained in acrylic plastic and granite artificial fracture system at the Whiteshell laboratories, AECL, Canada. (author). 34 refs., 11 tabs., 76 figs.

  8. A Developed Artificial Bee Colony Algorithm Based on Cloud Model

    Directory of Open Access Journals (Sweden)

    Ye Jin

    2018-04-01

    Full Text Available The Artificial Bee Colony (ABC algorithm is a bionic intelligent optimization method. The cloud model is a kind of uncertainty conversion model between a qualitative concept T ˜ that is presented by nature language and its quantitative expression, which integrates probability theory and the fuzzy mathematics. A developed ABC algorithm based on cloud model is proposed to enhance accuracy of the basic ABC algorithm and avoid getting trapped into local optima by introducing a new select mechanism, replacing the onlooker bees’ search formula and changing the scout bees’ updating formula. Experiments on CEC15 show that the new algorithm has a faster convergence speed and higher accuracy than the basic ABC and some cloud model based ABC variants.

  9. Constructing wetlands: measuring and modeling feedbacks of oxidation processes between plants and clay-rich material

    Science.gov (United States)

    Saaltink, Rémon; Dekker, Stefan C.; Griffioen, Jasper; Wassen, Martin J.

    2016-04-01

    Interest is growing in using soft sediment as a building material in eco-engineering projects. Wetland construction in the Dutch lake Markermeer is an example: here the option of dredging some of the clay-rich lake-bed sediment and using it to construct 10.000 ha of wetland will soon go under construction. Natural processes will be utilized during and after construction to accelerate ecosystem development. Knowing that plants can eco-engineer their environment via positive or negative biogeochemical plant-soil feedbacks, we conducted a six-month greenhouse experiment to identify the key biogeochemical processes in the mud when Phragmites australis is used as an eco-engineering species. We applied inverse biogeochemical modeling to link observed changes in pore water composition to biogeochemical processes. Two months after transplantation we observed reduced plant growth and shriveling as well as yellowing of foliage. The N:P ratios of plant tissue were low and were affected not by hampered uptake of N but by enhanced uptake of P. Plant analyses revealed high Fe concentrations in the leaves and roots. Sulfate concentrations rose drastically in our experiment due to pyrite oxidation; as reduction of sulfate will decouple Fe-P in reducing conditions, we argue that plant-induced iron toxicity hampered plant growth, forming a negative feedback loop, while simultaneously there was a positive feedback loop, as iron toxicity promotes P mobilization as a result of reduced conditions through root death, thereby stimulating plant growth and regeneration. Given these two feedback mechanisms, we propose that when building wetlands from these mud deposits Fe-tolerant species are used rather than species that thrive in N-limited conditions. The results presented in this study demonstrate the importance of studying the biogeochemical properties of the building material and the feedback mechanisms between plant and soil prior to finalizing the design of the eco-engineering project.

  10. Modelling contrasting responses of wetland productivity to changes in water table depth

    Directory of Open Access Journals (Sweden)

    R. F. Grant

    2012-11-01

    Full Text Available Responses of wetland productivity to changes in water table depth (WTD are controlled by complex interactions among several soil and plant processes, and hence are site-specific rather than general in nature. Hydrological controls on wetland productivity were studied by representing these interactions in connected hummock and hollow sites in the ecosystem model ecosys, and by testing CO2 and energy fluxes from the model with those measured by eddy covariance (EC during years with contrasting WTD in a shrub fen at Lost Creek, WI. Modelled interactions among coupled processes for O2 transfer, O2 uptake, C oxidation, N mineralization, N uptake and C fixation by diverse microbial, root and mycorrhizal populations enabled the model to simulate complex responses of CO2 exchange to changes in WTD that depended on the WTD at which change was occurring. At the site scale, greater WTD caused the model to simulate greater CO2 influxes and effluxes over hummocks vs. hollows, as has been found at field sites. At the landscape scale, greater WTD caused the model to simulate greater diurnal CO2 influxes and effluxes under cooler weather when water tables were shallow, but also smaller diurnal CO2 influxes and effluxes under warmer weather when water tables were deeper, as was also apparent in the EC flux measurements. At an annual time scale, these diurnal responses to WTD in the model caused lower net primary productivity (NPP and heterotrophic respiration (Rh, but higher net ecosystem productivity (NEP = NPP − Rh, to be simulated in a cooler year with a shallower water table than in a warmer year with a deeper one. This difference in NEP was consistent with those estimated from gap-filled EC fluxes in years with different water tables at Lost Creek and at similar boreal fens elsewhere. In sensitivity tests of the model, annual NEP

  11. Forecasting municipal solid waste generation using artificial intelligence modelling approaches.

    Science.gov (United States)

    Abbasi, Maryam; El Hanandeh, Ali

    2016-10-01

    Municipal solid waste (MSW) management is a major concern to local governments to protect human health, the environment and to preserve natural resources. The design and operation of an effective MSW management system requires accurate estimation of future waste generation quantities. The main objective of this study was to develop a model for accurate forecasting of MSW generation that helps waste related organizations to better design and operate effective MSW management systems. Four intelligent system algorithms including support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and k-nearest neighbours (kNN) were tested for their ability to predict monthly waste generation in the Logan City Council region in Queensland, Australia. Results showed artificial intelligence models have good prediction performance and could be successfully applied to establish municipal solid waste forecasting models. Using machine learning algorithms can reliably predict monthly MSW generation by training with waste generation time series. In addition, results suggest that ANFIS system produced the most accurate forecasts of the peaks while kNN was successful in predicting the monthly averages of waste quantities. Based on the results, the total annual MSW generated in Logan City will reach 9.4×10(7)kg by 2020 while the peak monthly waste will reach 9.37×10(6)kg. Copyright © 2016 Elsevier Ltd. All rights reserved.

  12. The Effect of Climate Change on Wetlands and Waterfowl in Western Canada: Incorporating Cropping Decisions into a Bioeconomic Model

    NARCIS (Netherlands)

    Withey, P.; Kooten, van G.C.

    2013-01-01

    We extend an earlier bioeconomic model of optimal duck harvest and wetland retention in the Prairie Pothole Region of Western Canada to include cropping decisions. Instead of a single state equation, the model has two state equations representing the population dynamics of ducks and the amount of

  13. Modeling the impacts of climate variability and hurricane on carbon sequestration in a coastal forested wetland in South Carolina

    Science.gov (United States)

    Zhaohua Dai; Carl C. Trettin; Changsheng Li; Ge Sun; Devendra M. Amatya; Harbin Li

    2013-01-01

    The impacts of hurricane disturbance and climate variability on carbon dynamics in a coastal forested wetland in South Carolina of USA were simulated using the Forest-DNDC model with a spatially explicit approach. The model was validated using the measured biomass before and after Hurricane Hugo and the biomass inventories in 2006 and 2007, showed that the Forest-DNDC...

  14. Artificial Neural Network versus Linear Models Forecasting Doha Stock Market

    Science.gov (United States)

    Yousif, Adil; Elfaki, Faiz

    2017-12-01

    The purpose of this study is to determine the instability of Doha stock market and develop forecasting models. Linear time series models are used and compared with a nonlinear Artificial Neural Network (ANN) namely Multilayer Perceptron (MLP) Technique. It aims to establish the best useful model based on daily and monthly data which are collected from Qatar exchange for the period starting from January 2007 to January 2015. Proposed models are for the general index of Qatar stock exchange and also for the usages in other several sectors. With the help of these models, Doha stock market index and other various sectors were predicted. The study was conducted by using various time series techniques to study and analyze data trend in producing appropriate results. After applying several models, such as: Quadratic trend model, double exponential smoothing model, and ARIMA, it was concluded that ARIMA (2,2) was the most suitable linear model for the daily general index. However, ANN model was found to be more accurate than time series models.

  15. On prognostic models, artificial intelligence and censored observations.

    Science.gov (United States)

    Anand, S S; Hamilton, P W; Hughes, J G; Bell, D A

    2001-03-01

    The development of prognostic models for assisting medical practitioners with decision making is not a trivial task. Models need to possess a number of desirable characteristics and few, if any, current modelling approaches based on statistical or artificial intelligence can produce models that display all these characteristics. The inability of modelling techniques to provide truly useful models has led to interest in these models being purely academic in nature. This in turn has resulted in only a very small percentage of models that have been developed being deployed in practice. On the other hand, new modelling paradigms are being proposed continuously within the machine learning and statistical community and claims, often based on inadequate evaluation, being made on their superiority over traditional modelling methods. We believe that for new modelling approaches to deliver true net benefits over traditional techniques, an evaluation centric approach to their development is essential. In this paper we present such an evaluation centric approach to developing extensions to the basic k-nearest neighbour (k-NN) paradigm. We use standard statistical techniques to enhance the distance metric used and a framework based on evidence theory to obtain a prediction for the target example from the outcome of the retrieved exemplars. We refer to this new k-NN algorithm as Censored k-NN (Ck-NN). This reflects the enhancements made to k-NN that are aimed at providing a means for handling censored observations within k-NN.

  16. Calibrating the sqHIMMELI v1.0 wetland methane emission model with hierarchical modeling and adaptive MCMC

    Science.gov (United States)

    Susiluoto, Jouni; Raivonen, Maarit; Backman, Leif; Laine, Marko; Makela, Jarmo; Peltola, Olli; Vesala, Timo; Aalto, Tuula

    2018-03-01

    Estimating methane (CH4) emissions from natural wetlands is complex, and the estimates contain large uncertainties. The models used for the task are typically heavily parameterized and the parameter values are not well known. In this study, we perform a Bayesian model calibration for a new wetland CH4 emission model to improve the quality of the predictions and to understand the limitations of such models.The detailed process model that we analyze contains descriptions for CH4 production from anaerobic respiration, CH4 oxidation, and gas transportation by diffusion, ebullition, and the aerenchyma cells of vascular plants. The processes are controlled by several tunable parameters. We use a hierarchical statistical model to describe the parameters and obtain the posterior distributions of the parameters and uncertainties in the processes with adaptive Markov chain Monte Carlo (MCMC), importance resampling, and time series analysis techniques. For the estimation, the analysis utilizes measurement data from the Siikaneva flux measurement site in southern Finland. The uncertainties related to the parameters and the modeled processes are described quantitatively. At the process level, the flux measurement data are able to constrain the CH4 production processes, methane oxidation, and the different gas transport processes. The posterior covariance structures explain how the parameters and the processes are related. Additionally, the flux and flux component uncertainties are analyzed both at the annual and daily levels. The parameter posterior densities obtained provide information regarding importance of the different processes, which is also useful for development of wetland methane emission models other than the square root HelsinkI Model of MEthane buiLd-up and emIssion for peatlands (sqHIMMELI). The hierarchical modeling allows us to assess the effects of some of the parameters on an annual basis. The results of the calibration and the cross validation suggest that

  17. A Quantum Implementation Model for Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Ammar Daskin

    2018-02-01

    Full Text Available The learning process for multilayered neural networks with many nodes makes heavy demands on computational resources. In some neural network models, the learning formulas, such as the Widrow–Hoff formula, do not change the eigenvectors of the weight matrix while flatting the eigenvalues. In infinity, these iterative formulas result in terms formed by the principal components of the weight matrix, namely, the eigenvectors corresponding to the non-zero eigenvalues. In quantum computing, the phase estimation algorithm is known to provide speedups over the conventional algorithms for the eigenvalue-related problems. Combining the quantum amplitude amplification with the phase estimation algorithm, a quantum implementation model for artificial neural networks using the Widrow–Hoff learning rule is presented. The complexity of the model is found to be linear in the size of the weight matrix. This provides a quadratic improvement over the classical algorithms. Quanta 2018; 7: 7–18.

  18. Use of artificial neural networks for transport energy demand modeling

    International Nuclear Information System (INIS)

    Murat, Yetis Sazi; Ceylan, Halim

    2006-01-01

    The paper illustrates an artificial neural network (ANN) approach based on supervised neural networks for the transport energy demand forecasting using socio-economic and transport related indicators. The ANN transport energy demand model is developed. The actual forecast is obtained using a feed forward neural network, trained with back propagation algorithm. In order to investigate the influence of socio-economic indicators on the transport energy demand, the ANN is analyzed based on gross national product (GNP), population and the total annual average veh-km along with historical energy data available from 1970 to 2001. Comparing model predictions with energy data in testing period performs the model validation. The projections are made with two scenarios. It is obtained that the ANN reflects the fluctuation in historical data for both dependent and independent variables. The results obtained bear out the suitability of the adopted methodology for the transport energy-forecasting problem

  19. Modeling of methane emissions using artificial neural network approach

    Directory of Open Access Journals (Sweden)

    Stamenković Lidija J.

    2015-01-01

    Full Text Available The aim of this study was to develop a model for forecasting CH4 emissions at the national level, using Artificial Neural Networks (ANN with broadly available sustainability, economical and industrial indicators as their inputs. ANN modeling was performed using two different types of architecture; a Backpropagation Neural Network (BPNN and a General Regression Neural Network (GRNN. A conventional multiple linear regression (MLR model was also developed in order to compare model performance and assess which model provides the best results. ANN and MLR models were developed and tested using the same annual data for 20 European countries. The ANN model demonstrated very good performance, significantly better than the MLR model. It was shown that a forecast of CH4 emissions at the national level using the ANN model can be made successfully and accurately for a future period of up to two years, thereby opening the possibility to apply such a modeling technique which can be used to support the implementation of sustainable development strategies and environmental management policies. [Projekat Ministarstva nauke Republike Srbije, br. 172007

  20. Predicting chick body mass by artificial intelligence-based models

    Directory of Open Access Journals (Sweden)

    Patricia Ferreira Ponciano Ferraz

    2014-07-01

    Full Text Available The objective of this work was to develop, validate, and compare 190 artificial intelligence-based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside four climate-controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21-day-old chicks - with the variables dry-bulb air temperature, duration of thermal stress (days, chick age (days, and the daily body mass of chicks - was used for network training, validation, and tests of models based on artificial neural networks (ANNs and neuro-fuzzy networks (NFNs. The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age after they were subjected to the input variables, and they showed an R² of 0.9993 and a standard error of 4.62 g. The ANNs enable the simulation of different scenarios, which can assist in managerial decision-making, and they can be embedded in the heating control systems.

  1. Optimization of the artificial urinary sphincter: modelling and experimental validation

    International Nuclear Information System (INIS)

    Marti, Florian; Leippold, Thomas; John, Hubert; Blunschi, Nadine; Mueller, Bert

    2006-01-01

    The artificial urinary sphincter should be long enough to prevent strangulation effects of the urethral tissue and short enough to avoid the improper dissection of the surrounding tissue. To optimize the sphincter length, the empirical three-parameter urethra compression model is proposed based on the mechanical properties of the urethra: wall pressure, tissue response rim force and sphincter periphery length. In vitro studies using explanted animal or human urethras and different artificial sphincters demonstrate its applicability. The pressure of the sphincter to close the urethra is shown to be a linear function of the bladder pressure. The force to close the urethra depends on the sphincter length linearly. Human urethras display the same dependences as the urethras of pig, dog, sheep and calf. Quantitatively, however, sow urethras resemble best the human ones. For the human urethras, the mean wall pressure corresponds to (-12.6 ± 0.9) cmH 2 O and (-8.7 ± 1.1) cmH 2 O, the rim length to (3.0 ± 0.3) mm and (5.1 ± 0.3) mm and the rim force to (60 ± 20) mN and (100 ± 20) mN for urethra opening and closing, respectively. Assuming an intravesical pressure of 40 cmH 2 O, and an external pressure on the urethra of 60 cmH 2 O, the model leads to the optimized sphincter length of (17.3 ± 3.8) mm

  2. The Wheal Jane wetlands model for bioremediation of acid mine drainage

    International Nuclear Information System (INIS)

    Whitehead, P.G.; Cosby, B.J.; Prior, H.

    2005-01-01

    Acid mine drainage (AMD) is a widespread environmental problem associated with both working and abandoned mining operations. As part of an overall strategy to determine a long-term treatment option for AMD, a pilot passive treatment plant was constructed in 1994 at Wheal Jane Mine in Cornwall, UK. The plant consists of three separate systems, each containing aerobic reed beds, anaerobic cell and rock filters, and represents the largest European experimental facility of its kind. The systems only differ by the type of pretreatment utilised to increase the pH of the influent minewater (pH <4): lime dosed (LD), anoxic limestone drain (ALD) and lime free (LF), which receives no form of pretreatment. Historical data (1994-1997) indicate median Fe reduction between 55% and 92%, sulphate removal in the range of 3-38% and removal of target metals (cadmium, copper and zinc) below detection limits, depending on pretreatment and flow rates through the system. A new model to simulate the processes and dynamics of the wetlands systems is described, as well as the application of the model to experimental data collected at the pilot plant. The model is process based, and utilises reaction kinetic approaches based on experimental microbial techniques rather than an equilibrium approach to metal precipitation. The model is dynamic and utilises numerical integration routines to solve a set of differential equations that describe the behaviour of 20 variables over the 17 pilot plant cells on a daily basis. The model outputs at each cell boundary are evaluated and compared with the measured data, and the model is demonstrated to provide a good representation of the complex behaviour of the wetland system for a wide range of variables

  3. Methodological application so as to obtain digital elevation models DEM in wetland areas

    International Nuclear Information System (INIS)

    Quintero, Deiby A; Montoya V, Diana M; Betancur, Teresita

    2009-01-01

    In order to understand hydrological systems and the description of flow processes that occur among its components it is essential to have a physiographic description that morphometric and relief characteristics. When local studies are performed, the basic cartography available, in the best case 1:25,000 scale, tends not to obey the needs required to represent the water dynamics that characterize the interactions between streams, aquifers and lenticular water bodies in flat zones particularly in those where there are wetlands localized in ancient F100D plains of rivers. A lack of financial resources is the principal obstacle to acquiring; information that is current and sufficient for the scale of the project. Geomorphologic conditions of flat relief zones are a good alternative for the construction of the new data. Using the basic cartography available and the new data, it is possible to obtain DEMs that are improved and consistent with the dynamics of surface and groundwater flows in the hydrological system. To accomplish this one must use spatial modeling tools coupled with Geographic Information System - GIS. This article present a methodological application for the region surrounding the catchment of wetland Cienaga Colombia in the Bajo Cauca region of Antioquia.

  4. Modeling the potential impacts of climate change on the water table level of selected forested wetlands in the southeastern United States

    Directory of Open Access Journals (Sweden)

    J. Zhu

    2017-12-01

    Full Text Available The southeastern United States hosts extensive forested wetlands, providing ecosystem services including carbon sequestration, water quality improvement, groundwater recharge, and wildlife habitat. However, these wetland ecosystems are dependent on local climate and hydrology, and are therefore at risk due to climate and land use change. This study develops site-specific empirical hydrologic models for five forested wetlands with different characteristics by analyzing long-term observed meteorological and hydrological data. These wetlands represent typical cypress ponds/swamps, Carolina bays, pine flatwoods, drained pocosins, and natural bottomland hardwood ecosystems. The validated empirical models are then applied at each wetland to predict future water table changes using climate projections from 20 general circulation models (GCMs participating in Coupled Model Inter-comparison Project 5 (CMIP5 under the Representative Concentration Pathways (RCPs 4.5 and 8.5 scenarios. We show that combined future changes in precipitation and potential evapotranspiration would significantly alter wetland hydrology including groundwater dynamics by the end of the 21st century. Compared to the historical period, all five wetlands are predicted to become drier over time. The mean water table depth is predicted to drop by 4 to 22 cm in response to the decrease in water availability (i.e., precipitation minus potential evapotranspiration by the year 2100. Among the five examined wetlands, the depressional wetland in hot and humid Florida appears to be most vulnerable to future climate change. This study provides quantitative information on the potential magnitude of wetland hydrological response to future climate change in typical forested wetlands in the southeastern US.

  5. Modeling the potential impacts of climate change on the water table level of selected forested wetlands in the southeastern United States

    Science.gov (United States)

    Zhu, Jie; Sun, Ge; Li, Wenhong; Zhang, Yu; Miao, Guofang; Noormets, Asko; McNulty, Steve G.; King, John S.; Kumar, Mukesh; Wang, Xuan

    2017-12-01

    The southeastern United States hosts extensive forested wetlands, providing ecosystem services including carbon sequestration, water quality improvement, groundwater recharge, and wildlife habitat. However, these wetland ecosystems are dependent on local climate and hydrology, and are therefore at risk due to climate and land use change. This study develops site-specific empirical hydrologic models for five forested wetlands with different characteristics by analyzing long-term observed meteorological and hydrological data. These wetlands represent typical cypress ponds/swamps, Carolina bays, pine flatwoods, drained pocosins, and natural bottomland hardwood ecosystems. The validated empirical models are then applied at each wetland to predict future water table changes using climate projections from 20 general circulation models (GCMs) participating in Coupled Model Inter-comparison Project 5 (CMIP5) under the Representative Concentration Pathways (RCPs) 4.5 and 8.5 scenarios. We show that combined future changes in precipitation and potential evapotranspiration would significantly alter wetland hydrology including groundwater dynamics by the end of the 21st century. Compared to the historical period, all five wetlands are predicted to become drier over time. The mean water table depth is predicted to drop by 4 to 22 cm in response to the decrease in water availability (i.e., precipitation minus potential evapotranspiration) by the year 2100. Among the five examined wetlands, the depressional wetland in hot and humid Florida appears to be most vulnerable to future climate change. This study provides quantitative information on the potential magnitude of wetland hydrological response to future climate change in typical forested wetlands in the southeastern US.

  6. Evaluation of a hierarchy of models reveals importance of substrate limitation for predicting carbon dioxide and methane exchange in restored wetlands

    Science.gov (United States)

    Oikawa, P. Y.; Jenerette, G. D.; Knox, S. H.; Sturtevant, C.; Verfaillie, J.; Dronova, I.; Poindexter, C. M.; Eichelmann, E.; Baldocchi, D. D.

    2017-01-01

    Wetlands and flooded peatlands can sequester large amounts of carbon (C) and have high greenhouse gas mitigation potential. There is growing interest in financing wetland restoration using C markets; however, this requires careful accounting of both CO2 and CH4 exchange at the ecosystem scale. Here we present a new model, the PEPRMT model (Peatland Ecosystem Photosynthesis Respiration and Methane Transport), which consists of a hierarchy of biogeochemical models designed to estimate CO2 and CH4 exchange in restored managed wetlands. Empirical models using temperature and/or photosynthesis to predict respiration and CH4 production were contrasted with a more process-based model that simulated substrate-limited respiration and CH4 production using multiple carbon pools. Models were parameterized by using a model-data fusion approach with multiple years of eddy covariance data collected in a recently restored wetland and a mature restored wetland. A third recently restored wetland site was used for model validation. During model validation, the process-based model explained 70% of the variance in net ecosystem exchange of CO2 (NEE) and 50% of the variance in CH4 exchange. Not accounting for high respiration following restoration led to empirical models overestimating annual NEE by 33-51%. By employing a model-data fusion approach we provide rigorous estimates of uncertainty in model predictions, accounting for uncertainty in data, model parameters, and model structure. The PEPRMT model is a valuable tool for understanding carbon cycling in restored wetlands and for application in carbon market-funded wetland restoration, thereby advancing opportunity to counteract the vast degradation of wetlands and flooded peatlands.

  7. Modeling of an industrial drying process by artificial neural networks

    Directory of Open Access Journals (Sweden)

    E. Assidjo

    2008-09-01

    Full Text Available A suitable method is needed to solve the nonquality problem in the grated coconut industry due to the poor control of product humidity during the process. In this study the possibility of using an artificial neural network (ANN, precisely a Multilayer Perceptron, for modeling the drying step of the production of grated coconut process is highlighted. Drying must confer to the product a final moisture of 3%. Unfortunately, under industrial conditions, this moisture varies from 1.9 to 4.8 %. In order to control this parameter and consequently reduce the proportion of the product that does not meet the humidity specification, a 9-4-1 neural network architecture was established using data gathered from an industrial plant. This Multilayer Perceptron can satisfactorily model the process with less bias, ranging from -0.35 to 0.34%, and can reduce the rate of rejected products from 92% to 3% during the first cycle of drying.

  8. An artificial neural network model for periodic trajectory generation

    Science.gov (United States)

    Shankar, S.; Gander, R. E.; Wood, H. C.

    A neural network model based on biological systems was developed for potential robotic application. The model consists of three interconnected layers of artificial neurons or units: an input layer subdivided into state and plan units, an output layer, and a hidden layer between the two outer layers which serves to implement nonlinear mappings between the input and output activation vectors. Weighted connections are created between the three layers, and learning is effected by modifying these weights. Feedback connections between the output and the input state serve to make the network operate as a finite state machine. The activation vector of the plan units of the input layer emulates the supraspinal commands in biological central pattern generators in that different plan activation vectors correspond to different sequences or trajectories being recalled, even with different frequencies. Three trajectories were chosen for implementation, and learning was accomplished in 10,000 trials. The fault tolerant behavior, adaptiveness, and phase maintenance of the implemented network are discussed.

  9. Assessment of wetland productive capacity from a remote-sensing-based model - A NASA/NMFS joint research project

    Science.gov (United States)

    Butera, M. K.; Frick, A. L.; Browder, J.

    1983-01-01

    NASA and the U.S. National Marine Fisheries Service have undertaken the development of Landsat Thematic Mapper (TM) technology for the evaluation of the usefulness of wetlands to estuarine fish and shellfish production. Toward this end, a remote sensing-based Productive Capacity model has been developed which characterizes the biological and hydrographic features of a Gulf Coast Marsh to predict detrital export. Regression analyses of TM simulator data for wetland plant production estimation are noted to more accurately estimate the percent of total vegetative cover than biomass, indicating that a nonlinear relationship may be involved.

  10. Variable recruitment fluidic artificial muscles: modeling and experiments

    International Nuclear Information System (INIS)

    Bryant, Matthew; Meller, Michael A; Garcia, Ephrahim

    2014-01-01

    We investigate taking advantage of the lightweight, compliant nature of fluidic artificial muscles to create variable recruitment actuators in the form of artificial muscle bundles. Several actuator elements at different diameter scales are packaged to act as a single actuator device. The actuator elements of the bundle can be connected to the fluidic control circuit so that different groups of actuator elements, much like individual muscle fibers, can be activated independently depending on the required force output and motion. This novel actuation concept allows us to save energy by effectively impedance matching the active size of the actuators on the fly based on the instantaneous required load. This design also allows a single bundled actuator to operate in substantially different force regimes, which could be valuable for robots that need to perform a wide variety of tasks and interact safely with humans. This paper proposes, models and analyzes the actuation efficiency of this actuator concept. The analysis shows that variable recruitment operation can create an actuator that reduces throttling valve losses to operate more efficiently over a broader range of its force–strain operating space. We also present preliminary results of the design, fabrication and experimental characterization of three such bioinspired variable recruitment actuator prototypes. (paper)

  11. Interactions of carbon and water cycles in north temperate wetlands: Modeling and observing the impact of a declining water table trend on regional biogeochemistry

    Science.gov (United States)

    Benjamin N. Sulman; Ankur R. Desai; D.S. Mackay; S. Samanta; B.D. Cook; N. Saliendra

    2008-01-01

    Terrestrial carbon fluxes represent a major source of uncertainty in estimates of future atmospheric greenhouse gas accumulation and consequently models of climate change. In the Upper Great Lakes states (Minnesota, Wisconsin, and Michigan), wetlands cover 14% of the land area, and compose up to one third of the land cover in the forest-wetland landscapes that dominate...

  12. New and noteworthy waterfowl records at artificial wetlands from Baja California Sur, Mexico Registros nuevos y sobresalientes de anátidos en humedales artificiales de Baja California Sur, México

    Directory of Open Access Journals (Sweden)

    Roberto Carmona

    2011-06-01

    Full Text Available We present 9 recent records of rare waterfowls in Baja California Sur, all of them in artificial wetlands: 3 freshwater sites and 1 concentration area for a saltworks. We present the first records of the Ross's Goose in the state. The remaining 8 species are: Black-bellied Whistling-Duck (breeding, Fulvous Whistling-Duck, Greater White-fronted Goose, Snow Goose, Cackling Goose, Tundra Swan, Mallard and Hooded Merganser. To this list we added an historical compilation of the records of these species in artificial sites of the state. The artificial wetlands are no replacement for their natural counterparts, they are nevertheless an important part of the region's landscape mosaic. As the records of the present work exemplify, this man-made habitat increases the regional species richness, and should be considered as important areas that need to be protected.Presentamos registros recientes de 9 especies de anátidos raros en Baja California Sur, todos ellos realizados en humedales creados por el hombre: 3 sitios dulceacuícolas y 1 área de concentración para la producción de sal. Se incluyen los primeros registros del ganso de Ross (Chen rossii para el estado. Las 8 especies restantes son: Dendrocygna autumnalis (anidación, D. bicolor, Anser albifrons, Chen caerulesens, Branta hutchinsii, Cygnus columbianus, Anas platyrhynchos y Lophodytes cucullatus. A la lista, agregamos una recopilación histórica de los registros de estas especies en humedales artificiales del estado. Aunque estos sitios no deben sustituir a sus contrapartes naturales, actualmente forman parte del mosaico paisajístico que ofrece la región; adicionalmente, incrementan la riqueza de especies de la región, por lo que es necesario brindarles protección.

  13. Artificial emotional model based on finite state machine

    Institute of Scientific and Technical Information of China (English)

    MENG Qing-mei; WU Wei-guo

    2008-01-01

    According to the basic emotional theory, the artificial emotional model based on the finite state machine(FSM) was presented. In finite state machine model of emotion, the emotional space included the basic emotional space and the multiple emotional spaces. The emotion-switching diagram was defined and transition function was developed using Markov chain and linear interpolation algorithm. The simulation model was built using Stateflow toolbox and Simulink toolbox based on the Matlab platform.And the model included three subsystems: the input one, the emotion one and the behavior one. In the emotional subsystem, the responses of different personalities to the external stimuli were described by defining personal space. This model takes states from an emotional space and updates its state depending on its current state and a state of its input (also a state-emotion). The simulation model realizes the process of switching the emotion from the neutral state to other basic emotions. The simulation result is proved to correspond to emotion-switching law of human beings.

  14. Ground Motion Prediction Model Using Artificial Neural Network

    Science.gov (United States)

    Dhanya, J.; Raghukanth, S. T. G.

    2018-03-01

    This article focuses on developing a ground motion prediction equation based on artificial neural network (ANN) technique for shallow crustal earthquakes. A hybrid technique combining genetic algorithm and Levenberg-Marquardt technique is used for training the model. The present model is developed to predict peak ground velocity, and 5% damped spectral acceleration. The input parameters for the prediction are moment magnitude ( M w), closest distance to rupture plane ( R rup), shear wave velocity in the region ( V s30) and focal mechanism ( F). A total of 13,552 ground motion records from 288 earthquakes provided by the updated NGA-West2 database released by Pacific Engineering Research Center are utilized to develop the model. The ANN architecture considered for the model consists of 192 unknowns including weights and biases of all the interconnected nodes. The performance of the model is observed to be within the prescribed error limits. In addition, the results from the study are found to be comparable with the existing relations in the global database. The developed model is further demonstrated by estimating site-specific response spectra for Shimla city located in Himalayan region.

  15. An artificial vector model for generating abnormal electrocardiographic rhythms

    International Nuclear Information System (INIS)

    Clifford, Gari D; Nemati, Shamim; Sameni, Reza

    2010-01-01

    We present generalizations of our previously published artificial models for generating multi-channel ECG to provide simulations of abnormal cardiac rhythms. Using a three-dimensional vectorcardiogram (VCG) formulation, we generate the normal cardiac dipole for a patient using a sum of Gaussian kernels, fitted to real VCG recordings. Abnormal beats are specified either as perturbations to the normal dipole or as new dipole trajectories. Switching between normal and abnormal beat types is achieved using a first-order Markov chain. Probability transitions can be learned from real data or modeled by coupling to heart rate and sympathovagal balance. Natural morphology changes from beat-to-beat are incorporated by varying the angular frequency of the dipole as a function of the inter-beat (RR) interval. The RR interval time series is generated using our previously described model whereby time- and frequency-domain heart rate (HR) and heart rate variability characteristics can be specified. QT-HR hysteresis is simulated by coupling the Gaussian kernels associated with the T-wave in the model with a nonlinear factor related to the local HR (determined from the last n RR intervals). Morphology changes due to respiration are simulated by introducing a rotation matrix couple to the respiratory frequency. We demonstrate an example of the use of this model by simulating HR-dependent T-wave alternans (TWA) with and without phase-switching due to ectopy. Application of our model also reveals previously unreported effects of common TWA estimation methods

  16. Analysis of wetland change in the Songhua River Basin from 1995 to 2008

    International Nuclear Information System (INIS)

    Yuan, L H; Jiang, W G; Liu, Y H; Luo, Z L; He, X H

    2014-01-01

    Wetlands in the Songhua River Basin in both 1995 and 2008 were mapped from land use/land cover maps generated from Landsat Thematic Mapper imagery. These maps were then divided into two categories, i.e. artificial wetland and natural wetland. From 1995 to 2008, the total area of wetland in the Songhua River Basin increased from 93 072.3 km 2 to 99 179.6 km 2 a net increase of 6107.3 km 2 . The area of natural wetland decreased by 4043.7 km 2 while the area of artificial wetland increased by 10 166.2 km 2 . Swamp wetland and paddy field wetland became the dominant wetlands and the swamp wetland in the east of the Heilong River system and the north of the Wusuli River system disappeared, being transformed into paddy field wetland. The diversity of wetland landscape is worsening and the distribution of wetland landscape is becoming more unbalanced; the fragmentation of natural wetland has intensified whereas the patch connectivity of artificial wetland has increased. Changes in natural wetlands were primarily caused by climate and socio-economic changes, while changes in artificial wetland were mainly caused by the growth of population and gross domestic product

  17. Simulating phosphorus removal from a vertical-flow constructed wetland grown with C alternifolius species

    Science.gov (United States)

    Ying Ouyang; Lihua Cui; Gary Feng; John Read

    2015-01-01

    Vertical flow constructed wetland (VFCW) is a promising technique for removal of excess nutrients and certain pollutants from wastewaters. The aim of this study was to develop a STELLA (structural thinking, experiential learning laboratory with animation) model for estimating phosphorus (P) removal in an artificial VFCW (i.e., a substrate column with six zones) grown...

  18. HIV lipodystrophy case definition using artificial neural network modelling

    DEFF Research Database (Denmark)

    Ioannidis, John P A; Trikalinos, Thomas A; Law, Matthew

    2003-01-01

    OBJECTIVE: A case definition of HIV lipodystrophy has recently been developed from a combination of clinical, metabolic and imaging/body composition variables using logistic regression methods. We aimed to evaluate whether artificial neural networks could improve the diagnostic accuracy. METHODS......: The database of the case-control Lipodystrophy Case Definition Study was split into 504 subjects (265 with and 239 without lipodystrophy) used for training and 284 independent subjects (152 with and 132 without lipodystrophy) used for validation. Back-propagation neural networks with one or two middle layers...... were trained and validated. Results were compared against logistic regression models using the same information. RESULTS: Neural networks using clinical variables only (41 items) achieved consistently superior performance than logistic regression in terms of specificity, overall accuracy and area under...

  19. Urban bat communities are affected by wetland size, quality, and pollution levels.

    Science.gov (United States)

    Straka, Tanja Maria; Lentini, Pia Eloise; Lumsden, Linda Faye; Wintle, Brendan Anthony; van der Ree, Rodney

    2016-07-01

    Wetlands support unique biota and provide important ecosystem services. These services are highly threatened due to the rate of loss and relative rarity of wetlands in most landscapes, an issue that is exacerbated in highly modified urban environments. Despite this, critical ecological knowledge is currently lacking for many wetland-dependent taxa, such as insectivorous bats, which can persist in urban areas if their habitats are managed appropriately. Here, we use a novel paired landscape approach to investigate the role of wetlands in urban bat conservation and examine local and landscape factors driving bat species richness and activity. We acoustically monitored bat activity at 58 urban wetlands and 35 nonwetland sites (ecologically similar sites without free-standing water) in the greater Melbourne area, southeastern Australia. We analyzed bat species richness and activity patterns using generalized linear mixed-effects models. We found that the presence of water in urban Melbourne was an important driver of bat species richness and activity at a landscape scale. Increasing distance to bushland and increasing levels of heavy metal pollution within the waterbody also negatively influenced bat richness and individual species activity. Areas with high levels of artificial night light had reduced bat species richness, and reduced activity for all species except those adapted to urban areas, such as the White-striped free-tailed bat (Austronomus australis). Increased surrounding tree cover and wetland size had a positive effect on bat species richness. Our findings indicate that wetlands form critical habitats for insectivorous bats in urban environments. Large, unlit, and unpolluted wetlands flanked by high tree cover in close proximity to bushland contribute most to the richness of the bat community. Our findings clarify the role of wetlands for insectivorous bats in urban areas and will also allow for the preservation, construction, and management of wetlands

  20. Artificial Systems and Models for Risk Covering Operations

    Directory of Open Access Journals (Sweden)

    Laurenţiu Mihai Treapăt

    2017-04-01

    Full Text Available Mainly, this paper focuses on the roles of artificial intelligence based systems and especially on risk-covering operations. In this context, the paper comes with theoretical explanations on real-life based examples and applications. From a general perspective, the paper enriches its value with a wide discussion on the related subject. The paper aims to revise the volatilities’ estimation models and the correlations between the various time series and also by presenting the Risk Metrics methodology, as explained is a case study. The advantages that the VaR estimation offers, consist of its ability to quantitatively and numerically express the risk level of a portfolio, at a certain moment in time and also the risk of on open position (in titles, in FX, commodities or granted loans, belonging to an economic agent or even individual; hence, its role in a more efficient capital allocation, in the assumed risk delimitation, and also as a performance measurement instrument. In this paper and the study case that completes our work, we aim to prove how we can prevent considerable losses and even bankruptcies if VaR is known and applied accordingly. For this reason, the universities inRomaniashould include or increase their curricula with the study of the VaR model as an artificial intelligence tool. The simplicity of the presented case study, most probably, is the strongest argument of the current work because it can be understood also by the readers that are not necessarily very experienced in the risk management field.

  1. DEVELOPING A HUMAN CONTROLLED MODEL FOR SAFE ARTIFICIAL INTELLIGENCE SYSTEMS

    OpenAIRE

    KÖSE, Utku

    2018-01-01

    Artificial Intelligence is known as one of the most effective research field of nowadays and the future. But rapid rise of Artificial Intelligence and its potential to solve all real world problems autonomously, it has caused also several anxieties. Some scientists think that intelligent systems can reach to a level, which is dangerous for the humankind so because of that some precautions should be taken. So, many sub-research fields like Machine Ethics or Artificial Intelligence Safety have ...

  2. Exploring Agricultural Drainage's Influence on Wetland and ...

    Science.gov (United States)

    Artificial agricultural drainage (i.e. surface ditches or subsurface tile) is an important agricultural management tool. Artificial drainage allows for timely fieldwork and adequate root aeration, resulting in greater crop yields for farmers. This practice is widespread throughout many regions of the United States and the network of artificial drainage is especially extensive in flat, poorly-drained regions like the glaciated Midwest. While beneficial for crop yields, agricultural drains often empty into streams within the natural drainage system. The increased network connectivity may lead to greater contributing area for watersheds, altered hydrology and increased conveyance of pollutants into natural water bodies. While studies and models at broader scales have implicated artificial drainage as an important driver of hydrological shifts and eutrophication, the actual spatial extent of artificial drainage is poorly known. Consequently, metrics of wetland and watershed connectivity within agricultural regions often fail to explicitly include artificial drainage. We use recent agricultural census data, soil drainage data, and land cover data to create estimates of potential agricultural drainage across the United States. We estimate that agricultural drainage in the US is greater than 31 million hectares and is concentrated in the upper Midwest Corn Belt, covering greater than 50% of available land for 114 counties. Estimated drainage values for numerous countie

  3. Empirical modeling of dynamic behaviors of pneumatic artificial muscle actuators.

    Science.gov (United States)

    Wickramatunge, Kanchana Crishan; Leephakpreeda, Thananchai

    2013-11-01

    Pneumatic Artificial Muscle (PAM) actuators yield muscle-like mechanical actuation with high force to weight ratio, soft and flexible structure, and adaptable compliance for rehabilitation and prosthetic appliances to the disabled as well as humanoid robots or machines. The present study is to develop empirical models of the PAM actuators, that is, a PAM coupled with pneumatic control valves, in order to describe their dynamic behaviors for practical control design and usage. Empirical modeling is an efficient approach to computer-based modeling with observations of real behaviors. Different characteristics of dynamic behaviors of each PAM actuator are due not only to the structures of the PAM actuators themselves, but also to the variations of their material properties in manufacturing processes. To overcome the difficulties, the proposed empirical models are experimentally derived from real physical behaviors of the PAM actuators, which are being implemented. In case studies, the simulated results with good agreement to experimental results, show that the proposed methodology can be applied to describe the dynamic behaviors of the real PAM actuators. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

  4. Development of a hydrogeological conceptual wetland model in the data-scarce north-eastern region of Kilombero Valley, Tanzania

    Science.gov (United States)

    Burghof, Sonja; Gabiri, Geofrey; Stumpp, Christine; Chesnaux, Romain; Reichert, Barbara

    2018-02-01

    Understanding groundwater/surface-water interactions in wetlands is crucial because wetlands provide not only a high potential for agricultural production, but also sensitive and valuable ecosystems. This is especially true for the Kilombero floodplain wetland in Tanzania, which represents a data-scarce region in terms of hydrological and hydrogeological data. A comprehensive approach combining hydrogeological with tracer-based assessments was conducted, in order to develop a conceptual hydrogeological wetland model of the area around the city of Ifakara in the north-eastern region of Kilombero catchment. Within the study site, a heterogeneous porous aquifer, with a range of hydraulic conductivities, is underlain by a fractured-rock aquifer. Groundwater chemistry is mainly influenced by silicate weathering and depends on groundwater residence times related to the hydraulic conductivities of the porous aquifer. Groundwater flows from the hillside to the river during most of the year. While floodwater close to the river is mainly derived from overbank flow of the river, floodwater at a greater distance from the river mainly originates from precipitation and groundwater discharge. Evaporation effects in floodwater increase with increasing distance from the river. In general, the contribution of flood and stream water to groundwater recharge is negligible. In terms of an intensification of agricultural activities in the wetland, several conclusions can be drawn from the conceptual model. Results of this study are valuable as a base for further research related to groundwater/surface-water interactions and the conceptual model can be used in the future to set up numerical flow and transport models.

  5. modeling of modeling of reservoir in reservoir in artificial neu

    African Journals Online (AJOL)

    eobe

    adequate inflow data modeling and forecasting is v essential for proper ... eed correct measurement, ates for future planning and .... (CCANN) were used to evaluate performance of ANNs in estimating ... appropriate low flow forecast model for the Meuse. River in Netherlands based on the comparison of output uncertainties ...

  6. Space Environment Modelling with the Use of Artificial Intelligence Methods

    Science.gov (United States)

    Lundstedt, H.; Wintoft, P.; Wu, J.-G.; Gleisner, H.; Dovheden, V.

    1996-12-01

    Space based technological systems are affected by the space weather in many ways. Several severe failures of satellites have been reported at times of space storms. Our society also increasingly depends on satellites for communication, navigation, exploration, and research. Predictions of the conditions in the satellite environment have therefore become very important. We will here present predictions made with the use of artificial intelligence (AI) techniques, such as artificial neural networks (ANN) and hybrids of AT methods. We are developing a space weather model based on intelligence hybrid systems (IHS). The model consists of different forecast modules, each module predicts the space weather on a specific time-scale. The time-scales range from minutes to months with the fundamental time-scale of 1-5 minutes, 1-3 hours, 1-3 days, and 27 days. Solar and solar wind data are used as input data. From solar magnetic field measurements, either made on the ground at Wilcox Solar Observatory (WSO) at Stanford, or made from space by the satellite SOHO, solar wind parameters can be predicted and modelled with ANN and MHD models. Magnetograms from WSO are available on a daily basis. However, from SOHO magnetograms will be available every 90 minutes. SOHO magnetograms as input to ANNs will therefore make it possible to even predict solar transient events. Geomagnetic storm activity can today be predicted with very high accuracy by means of ANN methods using solar wind input data. However, at present real-time solar wind data are only available during part of the day from the satellite WIND. With the launch of ACE in 1997, solar wind data will on the other hand be available during 24 hours per day. The conditions of the satellite environment are not only disturbed at times of geomagnetic storms but also at times of intense solar radiation and highly energetic particles. These events are associated with increased solar activity. Predictions of these events are therefore

  7. Artificial Neural Network L* from different magnetospheric field models

    Science.gov (United States)

    Yu, Y.; Koller, J.; Zaharia, S. G.; Jordanova, V. K.

    2011-12-01

    The third adiabatic invariant L* plays an important role in modeling and understanding the radiation belt dynamics. The popular way to numerically obtain the L* value follows the recipe described by Roederer [1970], which is, however, slow and computational expensive. This work focuses on a new technique, which can compute the L* value in microseconds without losing much accuracy: artificial neural networks. Since L* is related to the magnetic flux enclosed by a particle drift shell, global magnetic field information needed to trace the drift shell is required. A series of currently popular empirical magnetic field models are applied to create the L* data pool using 1 million data samples which are randomly selected within a solar cycle and within the global magnetosphere. The networks, trained from the above L* data pool, can thereby be used for fairly efficient L* calculation given input parameters valid within the trained temporal and spatial range. Besides the empirical magnetospheric models, a physics-based self-consistent inner magnetosphere model (RAM-SCB) developed at LANL is also utilized to calculate L* values and then to train the L* neural network. This model better predicts the magnetospheric configuration and therefore can significantly improve the L*. The above neural network L* technique will enable, for the first time, comprehensive solar-cycle long studies of radiation belt processes. However, neural networks trained from different magnetic field models can result in different L* values, which could cause mis-interpretation of radiation belt dynamics, such as where the source of the radiation belt charged particle is and which mechanism is dominant in accelerating the particles. Such a fact calls for attention to cautiously choose a magnetospheric field model for the L* calculation.

  8. Artificial neural network model of pork meat cubes osmotic dehydratation

    Directory of Open Access Journals (Sweden)

    Pezo Lato L.

    2013-01-01

    Full Text Available Mass transfer of pork meat cubes (M. triceps brachii, shaped as 1x1x1 cm, during osmotic dehydration (OD and under atmospheric pressure was investigated in this paper. The effects of different parameters, such as concentration of sugar beet molasses (60-80%, w/w, temperature (20-50ºC, and immersion time (1-5 h in terms of water loss (WL, solid gain (SG, final dry matter content (DM, and water activity (aw, were investigated using experimental results. Five artificial neural network (ANN models were developed for the prediction of WL, SG, DM, and aw in OD of pork meat cubes. These models were able to predict process outputs with coefficient of determination, r2, of 0.990 for SG, 0.985 for WL, 0.986 for aw, and 0.992 for DM compared to experimental measurements. The wide range of processing variables considered for the formulation of these models, and their easy implementation in a spreadsheet calculus make it very useful and practical for process design and control.

  9. COMPUTER MODELING IN THE DEVELOPMENT OF ARTIFICIAL VENTRICLES OF HEART

    Directory of Open Access Journals (Sweden)

    L. V. Belyaev

    2011-01-01

    Full Text Available In article modern researches of processes of development of artificial ventricles of heart are described. Advanta- ges of application computer (CAD/CAE technologies are shown by development of artificial ventricles of heart. The systems developed with application of the given technologies are submitted. 

  10. Model-Based Fault Diagnosis in Electric Drive Inverters Using Artificial Neural Network

    National Research Council Canada - National Science Library

    Masrur, Abul; Chen, ZhiHang; Zhang, Baifang; Jia, Hongbin; Murphey, Yi-Lu

    2006-01-01

    .... A normal model and various faulted models of the inverter-motor combination were developed, and voltages and current signals were generated from those models to train an artificial neural network for fault diagnosis...

  11. Determination of the hydraulic characteristics by means of integral parameters in a model of wetland with subsuperficial flow

    International Nuclear Information System (INIS)

    Vallejos, G.; Ponce Caballero, C.; Quintal Franco, C.; Mendez Novelo, R.

    2009-01-01

    The main objective of this study was to assess the portions of plug flow and death zones using tracer tests by empiric models as Wolf-Resnick and Dispersion in evaluate bed-packed reactors with horizontal subsurface flow, as a model of a constructed wetland. In order to assess the hydraulic behavior of systems such as packed-bed reactors and constructed wetlands both of subsurface flow, it is necessary to study and evaluate them modifying some variables while others remain constant. As well it is important to use mathematical models to describe, as precise as possible, the different phenomenon inside the systems, in such a way that these models bring information in an integral way to predict the behavior of the systems. (Author)

  12. Wetland Hydrology | Science Inventory | US EPA

    Science.gov (United States)

    This chapter discusses the state of the science in wetland hydrology by touching upon the major hydraulic and hydrologic processes in these complex ecosystems, their measurement/estimation techniques, and modeling methods. It starts with the definition of wetlands, their benefits and types, and explains the role and importance of hydrology on wetland functioning. The chapter continues with the description of wetland hydrologic terms and related estimation and modeling techniques. The chapter provides a quick but valuable information regarding hydraulics of surface and subsurface flow, groundwater seepage/discharge, and modeling groundwater/surface water interactions in wetlands. Because of the aggregated effects of the wetlands at larger scales and their ecosystem services, wetland hydrology at the watershed scale is also discussed in which we elaborate on the proficiencies of some of the well-known watershed models in modeling wetland hydrology. This chapter can serve as a useful reference for eco-hydrologists, wetland researchers and decision makers as well as watershed hydrology modelers. In this chapter, the importance of hydrology for wetlands and their functional role are discussed. Wetland hydrologic terms and the major components of water budget in wetlands and how they can be estimated/modeled are also presented. Although this chapter does not provide a comprehensive coverage of wetland hydrology, it provides a quick understanding of the basic co

  13. Bacterial DNA Sequence Compression Models Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Armando J. Pinho

    2013-08-01

    Full Text Available It is widely accepted that the advances in DNA sequencing techniques have contributed to an unprecedented growth of genomic data. This fact has increased the interest in DNA compression, not only from the information theory and biology points of view, but also from a practical perspective, since such sequences require storage resources. Several compression methods exist, and particularly, those using finite-context models (FCMs have received increasing attention, as they have been proven to effectively compress DNA sequences with low bits-per-base, as well as low encoding/decoding time-per-base. However, the amount of run-time memory required to store high-order finite-context models may become impractical, since a context-order as low as 16 requires a maximum of 17.2 x 109 memory entries. This paper presents a method to reduce such a memory requirement by using a novel application of artificial neural networks (ANN to build such probabilistic models in a compact way and shows how to use them to estimate the probabilities. Such a system was implemented, and its performance compared against state-of-the art compressors, such as XM-DNA (expert model and FCM-Mx (mixture of finite-context models , as well as with general-purpose compressors. Using a combination of order-10 FCM and ANN, similar encoding results to those of FCM, up to order-16, are obtained using only 17 megabytes of memory, whereas the latter, even employing hash-tables, uses several hundreds of megabytes.

  14. Electron beam lithographic modeling assisted by artificial intelligence technology

    Science.gov (United States)

    Nakayamada, Noriaki; Nishimura, Rieko; Miura, Satoru; Nomura, Haruyuki; Kamikubo, Takashi

    2017-07-01

    We propose a new concept of tuning a point-spread function (a "kernel" function) in the modeling of electron beam lithography using the machine learning scheme. Normally in the work of artificial intelligence, the researchers focus on the output results from a neural network, such as success ratio in image recognition or improved production yield, etc. In this work, we put more focus on the weights connecting the nodes in a convolutional neural network, which are naturally the fractions of a point-spread function, and take out those weighted fractions after learning to be utilized as a tuned kernel. Proof-of-concept of the kernel tuning has been demonstrated using the examples of proximity effect correction with 2-layer network, and charging effect correction with 3-layer network. This type of new tuning method can be beneficial to give researchers more insights to come up with a better model, yet it might be too early to be deployed to production to give better critical dimension (CD) and positional accuracy almost instantly.

  15. Further evaluation of wetland emission estimates from the JULES land surface model using SCIAMACHY and GOSAT atmospheric column methane measurements

    Science.gov (United States)

    Hayman, Garry; Comyn-Platt, Edward; McNorton, Joey; Chipperfield, Martyn; Gedney, Nicola

    2016-04-01

    The atmospheric concentration of methane began rising again in 2007 after a period of near-zero growth [1,2], with the largest increases observed over polar northern latitudes and the Southern Hemisphere in 2007 and in the tropics since then. The observed inter-annual variability in atmospheric methane concentrations and the associated changes in growth rates have variously been attributed to changes in different methane sources and sinks [2,3]. Wetlands are generally accepted as being the largest, but least well quantified, single natural source of CH4, with global emission estimates ranging from 142-284 Tg yr-1 [3]. The modelling of wetlands and their associated emissions of CH4 has become the subject of much current interest [4]. We have previously used the HadGEM2 chemistry-climate model to evaluate the wetland emission estimates derived using the UK community land surface model (JULES, the Joint UK Land Earth Simulator) against atmospheric observations of methane, including SCIAMACHY total methane columns [5] up to 2007. We have undertaken a series of new HadGEM2 runs using new JULES emission estimates extended in time to the end of 2012, thereby allowing comparison with both SCIAMACHY and GOSAT atmospheric column methane measurements. We will describe the results of these runs and the implications for methane wetland emissions. References [1] Rigby, M., et al.: Renewed growth of atmospheric methane. Geophys. Res. Lett., 35, L22805, 2008; [2] Nisbet, E.G., et al.: Methane on the Rise-Again, Science 343, 493, 2014; [3] Kirschke, S., et al.,: Three decades of global methane sources and sinks, Nature Geosciences, 6, 813-823, 2013; [4] Melton, J. R., et al.: Present state of global wetland extent and wetland methane modelling: conclusions from a model inter-comparison project (WETCHIMP), Biogeosciences, 10, 753-788, 2013; [5] Hayman, G.D., et al.: Comparison of the HadGEM2 climate-chemistry model against in situ and SCIAMACHY atmospheric methane data, Atmos. Chem

  16. Bayesian Model Averaging of Artificial Intelligence Models for Hydraulic Conductivity Estimation

    Science.gov (United States)

    Nadiri, A.; Chitsazan, N.; Tsai, F. T.; Asghari Moghaddam, A.

    2012-12-01

    This research presents a Bayesian artificial intelligence model averaging (BAIMA) method that incorporates multiple artificial intelligence (AI) models to estimate hydraulic conductivity and evaluate estimation uncertainties. Uncertainty in the AI model outputs stems from error in model input as well as non-uniqueness in selecting different AI methods. Using one single AI model tends to bias the estimation and underestimate uncertainty. BAIMA employs Bayesian model averaging (BMA) technique to address the issue of using one single AI model for estimation. BAIMA estimates hydraulic conductivity by averaging the outputs of AI models according to their model weights. In this study, the model weights were determined using the Bayesian information criterion (BIC) that follows the parsimony principle. BAIMA calculates the within-model variances to account for uncertainty propagation from input data to AI model output. Between-model variances are evaluated to account for uncertainty due to model non-uniqueness. We employed Takagi-Sugeno fuzzy logic (TS-FL), artificial neural network (ANN) and neurofuzzy (NF) to estimate hydraulic conductivity for the Tasuj plain aquifer, Iran. BAIMA combined three AI models and produced better fitting than individual models. While NF was expected to be the best AI model owing to its utilization of both TS-FL and ANN models, the NF model is nearly discarded by the parsimony principle. The TS-FL model and the ANN model showed equal importance although their hydraulic conductivity estimates were quite different. This resulted in significant between-model variances that are normally ignored by using one AI model.

  17. Estimating environmental conditions affecting protozoal pathogen removal in surface water wetland systems using a multi-scale, model-based approach.

    Science.gov (United States)

    Daniels, Miles E; Hogan, Jennifer; Smith, Woutrina A; Oates, Stori C; Miller, Melissa A; Hardin, Dane; Shapiro, Karen; Los Huertos, Marc; Conrad, Patricia A; Dominik, Clare; Watson, Fred G R

    2014-09-15

    Cryptosporidium parvum, Giardia lamblia, and Toxoplasma gondii are waterborne protozoal pathogens distributed worldwide and empirical evidence suggests that wetlands reduce the concentrations of these pathogens under certain environmental conditions. The goal of this study was to evaluate how protozoal removal in surface water is affected by the water temperature, turbidity, salinity, and vegetation cover of wetlands in the Monterey Bay region of California. To examine how protozoal removal was affected by these environmental factors, we conducted observational experiments at three primary spatial scales: settling columns, recirculating wetland mesocosm tanks, and an experimental research wetland (Molera Wetland). Simultaneously, we developed a protozoal transport model for surface water to simulate the settling columns, the mesocosm tanks, and the Molera Wetland. With a high degree of uncertainty expected in the model predictions and field observations, we developed the model within a Bayesian statistical framework. We found protozoal removal increased when water flowed through vegetation, and with higher levels of turbidity, salinity, and temperature. Protozoal removal in surface water was maximized (~0.1 hour(-1)) when flowing through emergent vegetation at 2% cover, and with a vegetation contact time of ~30 minutes compared to the effects of temperature, salinity, and turbidity. Our studies revealed that an increase in vegetated wetland area, with water moving through vegetation, would likely improve regional water quality through the reduction of fecal protozoal pathogen loads. Copyright © 2014 Elsevier B.V. All rights reserved.

  18. Modeling Common-Sense Decisions in Artificial Intelligence

    Science.gov (United States)

    Zak, Michail

    2010-01-01

    A methodology has been conceived for efficient synthesis of dynamical models that simulate common-sense decision- making processes. This methodology is intended to contribute to the design of artificial-intelligence systems that could imitate human common-sense decision making or assist humans in making correct decisions in unanticipated circumstances. This methodology is a product of continuing research on mathematical models of the behaviors of single- and multi-agent systems known in biology, economics, and sociology, ranging from a single-cell organism at one extreme to the whole of human society at the other extreme. Earlier results of this research were reported in several prior NASA Tech Briefs articles, the three most recent and relevant being Characteristics of Dynamics of Intelligent Systems (NPO -21037), NASA Tech Briefs, Vol. 26, No. 12 (December 2002), page 48; Self-Supervised Dynamical Systems (NPO-30634), NASA Tech Briefs, Vol. 27, No. 3 (March 2003), page 72; and Complexity for Survival of Living Systems (NPO- 43302), NASA Tech Briefs, Vol. 33, No. 7 (July 2009), page 62. The methodology involves the concepts reported previously, albeit viewed from a different perspective. One of the main underlying ideas is to extend the application of physical first principles to the behaviors of living systems. Models of motor dynamics are used to simulate the observable behaviors of systems or objects of interest, and models of mental dynamics are used to represent the evolution of the corresponding knowledge bases. For a given system, the knowledge base is modeled in the form of probability distributions and the mental dynamics is represented by models of the evolution of the probability densities or, equivalently, models of flows of information. Autonomy is imparted to the decisionmaking process by feedback from mental to motor dynamics. This feedback replaces unavailable external information by information stored in the internal knowledge base. Representation

  19. SWANN: The Snow Water Artificial Neural Network Modelling System

    Science.gov (United States)

    Broxton, P. D.; van Leeuwen, W.; Biederman, J. A.

    2017-12-01

    Snowmelt from mountain forests is important for water supply and ecosystem health. Along Arizona's Mogollon Rim, snowmelt contributes to rivers and streams that provide a significant water supply for hydro-electric power generation, agriculture, and human consumption in central Arizona. In this project, we are building a snow monitoring system for the Salt River Project (SRP), which supplies water and power to millions of customers in the Phoenix metropolitan area. We are using process-based hydrological models and artificial neural networks (ANNs) to generate information about both snow water equivalent (SWE) and snow cover. The snow-cover data is generated with ANNs that are applied to Landsat and MODIS satellite reflectance data. The SWE data is generated using a combination of gridded SWE estimates generated by process-based snow models and ANNs that account for variations in topography, forest cover, and solar radiation. The models are trained and evaluated with snow data from SNOTEL stations as well as from aerial LiDAR and field data that we collected this past winter in northern Arizona, as well as with similar data from other sites in the Southwest US. These snow data are produced in near-real time, and we have built a prototype decision support tool to deliver them to SRP. This tool is designed to provide daily-to annual operational monitoring of spatial and temporal changes in SWE and snow cover conditions over the entire Salt River Watershed (covering 17,000 km2), and features advanced web mapping capabilities and watershed analytics displayed as graphical data.

  20. Characterizing the Connectivity and Cumulative Effects of Wetlands on Downstream Hydrology: A Modeling Analysis

    Science.gov (United States)

    Geographically isolated wetlands (GIWs) are depressional landscape features entirely surrounded by uplands. While “GIW” may imply functional isolation from other surface waters, these systems exhibit a gradient of hydrologic, biological, and/or chemical connectivity. ...

  1. Nondestructive pavement evaluation using ILLI-PAVE based artificial neural network models.

    Science.gov (United States)

    2008-09-01

    The overall objective in this research project is to develop advanced pavement structural analysis models for more accurate solutions with fast computation schemes. Soft computing and modeling approaches, specifically the Artificial Neural Network (A...

  2. Developing an ecosystem model of a floating wetland for water quality improvement on a stormwater pond.

    Science.gov (United States)

    McAndrew, Brendan; Ahn, Changwoo

    2017-11-01

    An ecosystem model was developed to assist with designing and implementing a floating wetland (FW) for water quality management of urban stormwater ponds, focusing on nitrogen (N) removal. The model is comprised of three linked submodels: hydrology, plant growth, and nitrogen. The model was calibrated with the data that resulted from a FW constructed and implemented as part of an interdisciplinary pedagogical project on a university campus, titled "The Rain Project", which raised awareness of stormwater issues while investigating the potential application of green infrastructure for sustainable stormwater management. The FW had been deployed during the summer of 2015 (i.e., May through mid-September) on a major stormwater pond located at the center of the Fairfax Campus of George Mason University near Washington, D.C. We used the model to explore the impact of three design elements of FW (i.e., hydraulic residence time (HRT), surface area coverage, and primary productivity) on the function of FW. Model simulations showed enhanced N removal performance as HRT and surface area coverage increased. The relatively low macrophyte productivity observed indicates that, in the case of our pond and FW, N removal was very limited. The model results suggest that even full pond surface coverage would result in meager N removal (∼6%) at a HRT of one week. A FW with higher plant productivity, more representative of that reported in the literature, would require only 10% coverage to achieve similar N removal efficiency (∼7%). Therefore, macrophyte productivity appears to have a greater impact on FW performance on N removal than surface area coverage or pond HRT. The outcome of the study shows that this model, though limited in scope, may be useful in aiding the design of FW to augment the performance of degraded stormwater ponds in an effort to meet local water quality goals. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Modeling the Effect of Plants and Peat on Evapotranspiration in Constructed Wetlands

    Directory of Open Access Journals (Sweden)

    Florent Chazarenc

    2010-01-01

    Full Text Available Evapotranspiration (ET in constructed wetlands (CWs represents a major factor affecting hydrodynamics and treatment performances. The presence of high ET was shown to improve global treatment performances, however ET is affected by a wide range of parameters including plant development and CWs age. Our study aimed at modelling the effect of plants and peat on ET in CWs; since we hypothesized peat could behave like the presence of accumulated organic matter in old CWs. Treatment performances, hydraulic behaviour, and ET rates were measured in eight 1 m2 CWs mesocosm (1 unplanted, 1 unplanted with peat, 2 planted with Phragmites australis, 2 planted with Typha latifolia and 2 planted with Phragmites australis with peat. Two models were built using first order kinetics to simulate COD and TKN removal with ET as an input. The effect of peat was positive on ET and was related to the better growth conditions it offered to macrophytes. Removal efficiency in pilot units with larger ET was higher for TKN. On average, results show for COD a k20 value of 0.88 d-1 and 0.36 d-1 for TKN. We hypothesized that the main effect of ET was to concentrate effluent, thus enhancing degradation rates.

  4. Occupancy modeling of autonomously recorded vocalizations to predict distribution of rallids in tidal wetlands

    Science.gov (United States)

    Stiffler, Lydia L.; Anderson, James T.; Katzner, Todd

    2018-01-01

    Conservation and management for a species requires reliable information on its status, distribution, and habitat use. We identified occupancy and distributions of king (Rallus elegans) and clapper (R. crepitans) rail populations in marsh complexes along the Pamunkey and Mattaponi Rivers in Virginia, USA by modeling data on vocalizations recorded from autonomous recording units (ARUs). Occupancy probability for both species combined was 0.64 (95% CI: 0.53, 0.75) in marshes along the Pamunkey and 0.59 (0.45, 0.72) in marshes along the Mattaponi. Occupancy probability along the Pamunkey was strongly influenced by salinity, increasing logistically by a factor of 1.62 (0.6, 2.65) per parts per thousand of salinity. In contrast, there was not a strong salinity gradient on the Mattaponi and therefore vegetative community structure determined occupancy probability on that river. Estimated detection probability across both marshes was 0.63 (0.62, 0.65), but detection rates decreased as the season progressed. Monitoring wildlife within wetlands presents unique challenges for conservation managers. Our findings provide insight not only into how rails responded to environmental variation but also into the general utility of ARUs for occupancy modeling of the distribution and habitat associations of rails within tidal marsh systems.

  5. McGill wetland model: evaluation of a peatland carbon simulator developed for global assessments

    Directory of Open Access Journals (Sweden)

    F. St-Hilaire

    2010-11-01

    Full Text Available We developed the McGill Wetland Model (MWM based on the general structure of the Peatland Carbon Simulator (PCARS and the Canadian Terrestrial Ecosystem Model. Three major changes were made to PCARS: (1 the light use efficiency model of photosynthesis was replaced with a biogeochemical description of photosynthesis; (2 the description of autotrophic respiration was changed to be consistent with the formulation of photosynthesis; and (3 the cohort, multilayer soil respiration model was changed to a simple one box peat decomposition model divided into an oxic and anoxic zones by an effective water table, and a one-year residence time litter pool. MWM was then evaluated by comparing its output to the estimates of net ecosystem production (NEP, gross primary production (GPP and ecosystem respiration (ER from 8 years of continuous measurements at the Mer Bleue peatland, a raised ombrotrophic bog located in southern Ontario, Canada (index of agreement [dimensionless]: NEP = 0.80, GPP = 0.97, ER = 0.97; systematic RMSE [g C m−2 d−1]: NEP = 0.12, GPP = 0.07, ER = 0.14; unsystematic RMSE: NEP = 0.15, GPP = 0.27, ER = 0.23. Simulated moss NPP approximates what would be expected for a bog peatland, but shrub NPP appears to be underestimated. Sensitivity analysis revealed that the model output did not change greatly due to variations in water table because of offsetting responses in production and respiration, but that even a modest temperature increase could lead to converting the bog from a sink to a source of CO2. General weaknesses and further developments of MWM are discussed.

  6. Modeling of the height control system using artificial neural networks

    Directory of Open Access Journals (Sweden)

    A. R Tahavvor

    2016-09-01

    Full Text Available Introduction Automation of agricultural and machinery construction has generally been enhanced by intelligent control systems due to utility and efficiency rising, ease of use, profitability and upgrading according to market demand. A broad variety of industrial merchandise are now supplied with computerized control systems of earth moving processes to be performed by construction and agriculture field vehicle such as grader, backhoe, tractor and scraper machines. A height control machine which is used in measuring base thickness is consisted of two mechanical and electronic parts. The mechanical part is consisted of conveyor belt, main body, electrical engine and invertors while the electronic part is consisted of ultrasonic, wave transmitter and receiver sensor, electronic board, control set, and microcontroller. The main job of these controlling devices consists of the topographic surveying, cutting and filling of elevated and spotted low area, and these actions fundamentally dependent onthe machine's ability in elevation and thickness measurement and control. In this study, machine was first tested and then some experiments were conducted for data collection. Study of system modeling in artificial neural networks (ANN was done for measuring, controlling the height for bases by input variable input vectors such as sampling time, probe speed, conveyer speed, sound wave speed and speed sensor are finally the maximum and minimum probe output vector on various conditions. The result reveals the capability of this procedure for experimental recognition of sensors' behavior and improvement of field machine control systems. Inspection, calibration and response, diagnosis of the elevation control system in combination with machine function can also be evaluated by some extra development of this system. Materials and Methods Designing and manufacture of the planned apparatus classified in three dissimilar, mechanical and electronic module, courses of

  7. National Wetlands Inventory Lines

    Data.gov (United States)

    Minnesota Department of Natural Resources — Linear wetland features (including selected streams, ditches, and narrow wetland bodies) mapped as part of the National Wetlands Inventory (NWI). The National...

  8. Application of Artificial Bee Colony in Model Parameter Identification of Solar Cells

    Directory of Open Access Journals (Sweden)

    Rongjie Wang

    2015-07-01

    Full Text Available The identification of values of solar cell parameters is of great interest for evaluating solar cell performances. The algorithm of an artificial bee colony was used to extract model parameters of solar cells from current-voltage characteristics. Firstly, the best-so-for mechanism was introduced to the original artificial bee colony. Then, a method was proposed to identify parameters for a single diode model and double diode model using this improved artificial bee colony. Experimental results clearly demonstrate the effectiveness of the proposed method and its superior performance compared to other competing methods.

  9. The application of neural networks with artificial intelligence technique in the modeling of industrial processes

    International Nuclear Information System (INIS)

    Saini, K. K.; Saini, Sanju

    2008-01-01

    Neural networks are a relatively new artificial intelligence technique that emulates the behavior of biological neural systems in digital software or hardware. These networks can 'learn', automatically, complex relationships among data. This feature makes the technique very useful in modeling processes for which mathematical modeling is difficult or impossible. The work described here outlines some examples of the application of neural networks with artificial intelligence technique in the modeling of industrial processes.

  10. An expert system model for mapping tropical wetlands and peatlands reveals South America as the largest contributor.

    Science.gov (United States)

    Gumbricht, Thomas; Roman-Cuesta, Rosa Maria; Verchot, Louis; Herold, Martin; Wittmann, Florian; Householder, Ethan; Herold, Nadine; Murdiyarso, Daniel

    2017-09-01

    Wetlands are important providers of ecosystem services and key regulators of climate change. They positively contribute to global warming through their greenhouse gas emissions, and negatively through the accumulation of organic material in histosols, particularly in peatlands. Our understanding of wetlands' services is currently constrained by limited knowledge on their distribution, extent, volume, interannual flood variability and disturbance levels. We present an expert system approach to estimate wetland and peatland areas, depths and volumes, which relies on three biophysical indices related to wetland and peat formation: (1) long-term water supply exceeding atmospheric water demand; (2) annually or seasonally water-logged soils; and (3) a geomorphological position where water is supplied and retained. Tropical and subtropical wetlands estimates reach 4.7 million km 2 (Mkm 2 ). In line with current understanding, the American continent is the major contributor (45%), and Brazil, with its Amazonian interfluvial region, contains the largest tropical wetland area (800,720 km 2 ). Our model suggests, however, unprecedented extents and volumes of peatland in the tropics (1.7 Mkm 2 and 7,268 (6,076-7,368) km 3 ), which more than threefold current estimates. Unlike current understanding, our estimates suggest that South America and not Asia contributes the most to tropical peatland area and volume (ca. 44% for both) partly related to some yet unaccounted extended deep deposits but mainly to extended but shallow peat in the Amazon Basin. Brazil leads the peatland area and volume contribution. Asia hosts 38% of both tropical peat area and volume with Indonesia as the main regional contributor and still the holder of the deepest and most extended peat areas in the tropics. Africa hosts more peat than previously reported but climatic and topographic contexts leave it as the least peat-forming continent. Our results suggest large biases in our current understanding of

  11. Modeling the large-scale effects of surface moisture heterogeneity on wetland carbon fluxes in the West Siberian Lowland

    Directory of Open Access Journals (Sweden)

    T. J. Bohn

    2013-10-01

    Full Text Available We used a process-based model to examine the role of spatial heterogeneity of surface and sub-surface water on the carbon budget of the wetlands of the West Siberian Lowland over the period 1948–2010. We found that, while surface heterogeneity (fractional saturated area had little overall effect on estimates of the region's carbon fluxes, sub-surface heterogeneity (spatial variations in water table depth played an important role in both the overall magnitude and spatial distribution of estimates of the region's carbon fluxes. In particular, to reproduce the spatial pattern of CH4 emissions recorded by intensive in situ observations across the domain, in which very little CH4 is emitted north of 60° N, it was necessary to (a account for CH4 emissions from unsaturated wetlands and (b use spatially varying methane model parameters that reduced estimated CH4 emissions in the northern (permafrost half of the domain (and/or account for lower CH4 emissions under inundated conditions. Our results suggest that previous estimates of the response of these wetlands to thawing permafrost may have overestimated future increases in methane emissions in the permafrost zone.

  12. Constructing a Baseline Model of Alpine Wetlands of the Uinta Mountains, Utah, USA

    Science.gov (United States)

    Matyjasik, M.; Ford, R. L.; Bartholomew, L. M.; Welsh, S. B.; Hernandez, M.; Koerner, D.; Muir, M.

    2008-12-01

    Alpine wetlands of the Uinta Mountains, northeastern Utah, contain a variety of groundwater-dependent ecosystems. Unlike their counterparts in other areas of the Rocky Mountains, these systems have been relatively unstudied. The Reader Lakes area on the southern slope of the range was selected for detailed study because of its variety of wetland plant communities, homogenous bedrock geology, and minimal human impact. The primary goal of this interdisciplinary study is to establish the functional links between the geomorphology and hydrogeology of these high mountain wetlands and their constituent plant communities. In addition to traditional field studies and water chemistry, geospatial technologies are being used to organize and analyze both field data (water chemistry and wetland vegetation) and archived multispectral imagery (2006 NAIP images). The hydrology of these wetlands is dominated by groundwater discharge and their surface is dominated by string-and-flark morphology of various spatial scales, making these montane wetlands classic patterned fens. The drainage basin is organized into a series of large-scale stair-stepping wetlands, bounded by glacial moraines at their lower end. Wetlands are compartmentalized by a series of large strings (roughly perpendicular to the axial stream) and flarks. This pattern may be related to small ridges on the underlying ground moraine and possibly modified by beaver activity along the axial stream. Small-scale patterning occurs along the margins of the wetlands and in sloping-fen settings. The smaller-scale strings and flarks form a complex; self-regulating system in which water retention is enhanced and surface flow is minimized. Major plant communities have been identified within the wetlands for example: a Salix planifolia community associated with the peaty strings; Carex aquatilis, Carex limosa, and Eriophorum angustifolium communities associated with flarks; as well as a Sphagnum sp.- rich hummocky transition zone

  13. Advanced Artificial Science. The development of an artificial science and engineering research infrastructure to facilitate innovative computational modeling, analysis, and application to interdisciplinary areas of scientific investigation.

    Energy Technology Data Exchange (ETDEWEB)

    Saffer, Shelley (Sam) I.

    2014-12-01

    This is a final report of the DOE award DE-SC0001132, Advanced Artificial Science. The development of an artificial science and engineering research infrastructure to facilitate innovative computational modeling, analysis, and application to interdisciplinary areas of scientific investigation. This document describes the achievements of the goals, and resulting research made possible by this award.

  14. A Multidisciplinary Artificial Intelligence Model of an Affective Robot

    Directory of Open Access Journals (Sweden)

    Hooman Aghaebrahimi Samani

    2012-03-01

    Full Text Available A multidisciplinary approach to a novel artificial intelligence system for an affective robot is presented in this paper. The general objective of the system is to develop a robotic system which strives to achieve a high level of emotional bond between humans and robot by exploring human love. Such a relationship is a contingent process of attraction, affection and attachment from humans towards robots, and the belief of the vice versa from robots to humans. The advanced artificial intelligence of the system includes three modules, namely Probabilistic Love Assembly (PLA, based on the psychology of love, Artificial Endocrine System (AES, based on the physiology of love, and Affective State Transition (AST, based on emotions. The PLA module employs a Bayesian network to incorporate psychological parameters of affection in the robot. The AES module employs artificial emotional and biological hormones via a Dynamic Bayesian Network (DBN. The AST module uses a novel transition method for handling affective states of the robot. These three modules work together to manage emotional behaviours of the robot.

  15. Changes in bacteria composition and efficiency of constructed wetlands under sustained overloads: A modeling experiment.

    Science.gov (United States)

    Boano, F; Rizzo, A; Samsó, R; García, J; Revelli, R; Ridolfi, L

    2018-01-15

    The average organic and hydraulic loads that Constructed Wetlands (CWs) receive are key parameters for their adequate long-term functioning. However, over their lifespan they will inevitably be subject to either episodic or sustained overloadings. Despite that the consequences of sustained overloading are well known (e.g., clogging), the threshold of overloads that these systems can tolerate is difficult to determine. Moreover, the mechanisms that might sustain the buffering capacity (i.e., the reduction of peaks in nutrient load) during overloads are not well understood. The aim of this work is to evaluate the effect of sudden but sustained organic and hydraulic overloads on the general functioning of CWs. To that end, the mathematical model BIO_PORE was used to simulate five different scenarios, based on the features and operation conditions of a pilot CW system: a control simulation representing the average loads; 2 simulations representing +10% and +30% sustained organic overloads; one simulation representing a sustained +30% hydraulic overload; and one simulation with sustained organic and hydraulic overloads of +15% each. Different model outputs (e.g., total bacterial biomass and its spatial distribution, effluent concentrations) were compared among different simulations to evaluate the effects of such operation changes. Results reveal that overloads determine a temporary decrease in removal efficiency before microbial biomass adapts to the new conditions and COD removal efficiency is recovered. Increasing organic overloads cause stronger temporary decreases in COD removal efficiency compared to increasing hydraulic loads. The pace at which clogging develops increases by 10% for each 10% increase on the organic load. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. Wetlands and Sustainability

    Directory of Open Access Journals (Sweden)

    Richard Smardon

    2014-11-01

    Full Text Available This editorial provides an overview of the special issue “Wetlands and Sustainability”. In particular, the special issue contains a review of Paul Keddy’s book “Wetland Ecology” with specific reference to wetland sustainability. It also includes papers addressing wetland data acquisition via radar and remote sensing to better understand wetland system dynamics, hydrologic processes linked to wetland stress and restoration, coastal wetlands land use conflict/management, and wetland utilization for water quality treatment.

  17. Soil Porewater Salinity Response to Sea-level Rise in Tidal Freshwater Forested Wetlands: A Modeling Study

    Science.gov (United States)

    Stagg, C. L.; Wang, H.; Krauss, K.; Conrads, P. A.; Swarzenski, C.; Duberstein, J. A.; DeAngelis, D.

    2017-12-01

    There is a growing concern about the adverse effects of salt water intrusion via tidal rivers and creeks into tidal freshwater forested wetlands (TFFWs) due to rising sea levels and reduction of freshwater flow. The distribution and composition of plant species, vegetation productivity, and biogeochemical functions including carbon sequestration capacity and flux rates in TFFWs have been found to be affected by increasing river and soil porewater salinities, with significant shifts occurring at a porewater salinity threshold of 3 PSU. However, the drivers of soil porewater salinity, which impact the health and ecological functions of TFFWs remains unclear, limiting our capability of predicting the future impacts of saltwater intrusion on ecosystem services provided by TFFWs. In this study, we developed a soil porewater salinity model for TFFWs based on an existing salt and water balance model with modifications to several key features such as the feedback mechanisms of soil salinity on evapotranspiration reduction and hydraulic conductivity. We selected sites along the floodplains of two rivers, the Waccamaw River (SC, USA) and the Savannah River (GA and SC, USA) that represent landscape salinity gradients of both surface water and soil porewater from tidal influence of the Atlantic Ocean. These sites represent healthy, moderately and highly salt-impacted forests, and oligohaline marshes. The soil porewater salinity model was calibrated and validated using field data collected at these sites throughout 2008-2016. The model results agreed well with field measurements. Analyses of the preliminary simulation results indicate that the magnitude, seasonal and annual variability, and duration of threshold salinities (e.g., 3 PSU) tend to vary significantly with vegetation status and type (i.e., healthy, degraded forests, and oligohaline marshes), especially during drought conditions. The soil porewater salinity model could be coupled with a wetland soil biogeochemistry

  18. Headwaters, Wetlands, and Wildfires: Utilizing Landsat imagery, GIS, and Statistical Models for Mapping Wetlands in Northern Colorado's Cache la Poudre Watershed in the aftermath of the June 2012 High Park Fire

    Science.gov (United States)

    Chignell, S.; Skach, S.; Kessenich, B.; Weimer, A.; Luizza, M.; Birtwistle, A.; Evangelista, P.; Laituri, M.; Young, N.

    2013-12-01

    The June 2012 High Park Fire burned over 87,000 acres of forest and 259 homes to the west of Fort Collins, CO. The fire has had dramatic impacts on forest ecosystems; of particular concern are its effects on the Cache la Poudre watershed, as the Poudre River is one of the most important headwaters of the Colorado Front Range, providing important ecosystem and economic services before flowing into the South Platte, which in turn flows into the Missouri River. Within a week of the fire, the area received several days of torrential rains. This precipitation--in conjunction with steep riverbanks and the loss of vegetation by fire--caused soil and ash runoff to be deposited into the Poudre's channel, resulting in a river of choking mud and black sludge. Monitoring the effects of such wildfires is critical and requires establishing immediate baseline data to assess impacts over time. Of particular concern is the region's wetlands, which not only provide habitat for a rich array of flora and fauna, but help regulate river discharge, improve water quality, and aid in carbon sequestration. However, the high expense of field work and the changing nature of wetlands have left many of the area's wetland maps incomplete and in need of updating. Utilizing Landsat 5 and Landsat 8 imagery, ancillary GIS layers, and boosted regression trees modeling, the NASA DEVELOP team based at the North Central Climate Science Center at Colorado State University developed a methodology for wetland modeling within the Cache la Poudre watershed. These efforts produced a preliminary model of predicted wetlands across the landscape that correctly classified 89% of the withheld validation points and had a kappa value of approximately 0.78. This initial model is currently being refined and validated using the USGS Software for Assisted Habitat Modeling (SAHM) to run multiple models within three elevation-based 'life zones.' The ultimate goal of this ongoing project is to provide important spatial

  19. Temporal stability and transferability of models of willingness to pay for flood control and wetland conservation

    NARCIS (Netherlands)

    Brouwer, R.; Bateman, I.J.

    2005-01-01

    This study investigates the temporal stability and transferability of dichotomous choice willingness to pay responses and their determinants from two large-scale contingent valuation surveys in the area of flood control and wetland conservation. The study considers a time period between surveys

  20. Hydrologic connectivity between geographically isolated wetlands and surface water systems: A review of select modeling methods

    Science.gov (United States)

    Heather E. Golden; Charles R. Lane; Devendra M. Amatya; Karl W. Bandilla; Hadas Raanan Kiperwas Kiperwas; Christopher D. Knightes; Herbert. Ssegane

    2014-01-01

    Geographically isolated wetlands (GIW), depressional landscape features entirely surrounded by upland areas, provide a wide range of ecological functions and ecosystem services for human well-being. Current and future ecosystem management and decision-making rely on a solid scientific understanding of how hydrologic processes affect these important GIW services and...

  1. Compartment-based hydrodynamics and water quality modeling of a NorthernEverglades Wetland, Florida, USA

    Science.gov (United States)

    The last remaining large remnant of softwater wetlands in the US Florida Everglades lies within the Arthur R. Marshall Loxahatchee National Wildlife Refuge. However, Refuge water quality today is impacted by pumped stormwater inflows to the eutrophic and mineral-enriched 100-km c...

  2. Modeled CO2 Emissions from Coastal Wetland Transitions to Other Land Uses: Tidal Marshes, Mangrove Forests, and Seagrass Beds

    Directory of Open Access Journals (Sweden)

    Catherine E. Lovelock

    2017-05-01

    Full Text Available The sediments of coastal wetlands contain large stores of carbon which are vulnerable to oxidation once disturbed, resulting in high levels of CO2 emissions that may be avoided if coastal ecosystems are conserved or restored. We used a simple model to estimate CO2 emissions from mangrove forests, seagrass beds, and tidal marshes based on known decomposition rates for organic matter in these ecosystems under either oxic or anoxic conditions combined with assumptions of the proportion of sediment carbon being deposited in either oxic or anoxic environments following a disturbance of the habitat. Our model found that over 40 years after disturbance the cumulative CO2 emitted from tidal marshes, mangrove forests, and seagrass beds were ~70–80% of the initial carbon stocks in the top meter of the sediment. Comparison of our estimates of CO2 emissions with empirical studies suggests that (1 assuming 50% of organic material moves to an oxic environment after disturbance gives rise to estimates that are similar to CO2 emissions reported for tidal marshes; (2 field measurements of CO2 emissions in disturbed mangrove forests were generally higher than our modeled emissions that assumed 50% of organic matter was deposited in oxic conditions, suggesting higher proportions of organic matter may be exposed to oxic conditions after disturbance in mangrove ecosystems; and (3 the generally low observed rates of CO2 emissions from disturbed seagrasses compared to our estimates, assuming removal of 50% of the organic matter to oxic environments, suggests that lower proportions may be exposed to oxic conditions in seagrass ecosystems. There are significant gaps in our knowledge of the fate of wetland sediment carbon in the marine environment after disturbance. Greater knowledge of the distribution, form, decomposition, and emission rates of wetland sediment carbon after disturbance would help to improve models.

  3. Use of created cattail ( Typha) wetlands in mitigation strategies

    Science.gov (United States)

    Dobberteen, Ross A.; Nickerson, Norton H.

    1991-11-01

    In order to balance pressures for land-use development with protection of wetland resources, artificial wetlands have been constructed in an effort to replace lost ecosystems. Despite its regulatory appeal and prominent role in current mitigation strategies, it is unclear whether or not created systems actually compensate for lost wetland resources. Mitigation predictions that rely on artificial wetlands must be analyzed critically in terms of their efficacy. Destruction of wetlands due to burial by coal fly ash at a municipal landfill in Danvers, Massachusetts, USA, provided an opportunity to compare resulting growth of created cattail ( Typha) marshes with natural wetland areas. Once the appropriate cattail species was identified for growth under disturbed landfill conditions, two types of artificial wetlands were constructed. The two systems differed in their hydrologic attributes: while one had a surface water flow characteristic of most cattail wetlands, the second system mimicked soil and water conditions found in naturally occurring floating cattail marshes. Comparison of plant growth measurements for two years from the artificial systems with published values for natural cattail marshes revealed similar structure and growth patterns. Experiments are now in progress to investigate the ability of created cattail marshes to remove and accumulate heavy metals from polluted landfill leachate. Research of the type reported here must be pursued aggressively in order to document the performance of artificial wetlands in terms of plant structure and wetland functions. Such research should allow us to start to evaluate whether artificial systems actually compensate for lost wetlands by performing similar functions and providing the concomitant public benefits.

  4. MODELLING OF THIN LAYER DRYING KINETICS OF COCOA BEANS DURING ARTIFICIAL AND NATURAL DRYING

    Directory of Open Access Journals (Sweden)

    C.L. HII

    2008-04-01

    Full Text Available Drying experiments were conducted using air-ventilated oven and sun dryer to simulate the artificial and natural drying processes of cocoa beans. The drying data were fitted with several published thin layer drying models. A new model was introduced which is a combination of the Page and two-term drying model. Selection of the best model was investigated by comparing the determination of coefficient (R2, reduced chi-square (2 and root mean square error (RMSE between the experimental and predicted values. The results showed that the new model was found best described the artificial and natural drying kinetics of cocoa under the conditions tested.

  5. Fringe wetlands

    International Nuclear Information System (INIS)

    Lugo, A.E.

    1990-01-01

    Fringe wetlands are characterized by the dominance of few species, a clear species zonation, synchrony of ecological processes with episodic events, and simplicity in the structure of vegetation. The structure and ecosystem dynamics of fringe forested wetlands are presented with emphasis on saltwater wetlands because they have been studied more than freshwater ones. The study areas were Caribbean and Florida mangroves. Fringe wetlands are found on the water edge of oceans, inland estuaries, and lakes. Water motion in the fringe is bi-directional and perpendicular to the forest and due mostly to tidal energy in oceanic and estuarine fringes. in lakes, water moves in and out of the fringe under the influence of wind, waves, or seiches. some fringe forests are occasionally flushed by terrestrial runoff or aquifer discharge. In contrast, fringe forests located on small offshore islands or steep coastal shroes are isolated from terrestrial runoff or aquifer discharge, and their hydroperiod is controlled by tides and waves only. Literature reviews suggest that ecosystem parameters such as vegetation structure, tree growth, primary productivity, and organic matter in sediments respond proportionally to hydrologic energy. Human activity that impacts on fringe forested wetlands include harvesting of trees, oil pollution and eutrophication. 72 refs., 12 figs., 9 tabs

  6. An approach to hydrogeological modeling of a large system of groundwater-fed lakes and wetlands in the Nebraska Sand Hills, USA

    Science.gov (United States)

    Rossman, Nathan R.; Zlotnik, Vitaly A.; Rowe, Clinton M.

    2018-05-01

    The feasibility of a hydrogeological modeling approach to simulate several thousand shallow groundwater-fed lakes and wetlands without explicitly considering their connection with groundwater is investigated at the regional scale ( 40,000 km2) through an application in the semi-arid Nebraska Sand Hills (NSH), USA. Hydraulic heads are compared to local land-surface elevations from a digital elevation model (DEM) within a geographic information system to assess locations of lakes and wetlands. The water bodies are inferred where hydraulic heads exceed, or are above a certain depth below, the land surface. Numbers of lakes and/or wetlands are determined via image cluster analysis applied to the same 30-m grid as the DEM after interpolating both simulated and estimated heads. The regional water-table map was used for groundwater model calibration, considering MODIS-based net groundwater recharge data. Resulting values of simulated total baseflow to interior streams are within 1% of observed values. Locations, areas, and numbers of simulated lakes and wetlands are compared with Landsat 2005 survey data and with areas of lakes from a 1979-1980 Landsat survey and the National Hydrography Dataset. This simplified process-based modeling approach avoids the need for field-based morphology or water-budget data from individual lakes or wetlands, or determination of lake-groundwater exchanges, yet it reproduces observed lake-wetland characteristics at regional groundwater management scales. A better understanding of the NSH hydrogeology is attained, and the approach shows promise for use in simulations of groundwater-fed lake and wetland characteristics in other large groundwater systems.

  7. The Blackwater NWR inundation model. Rising sea level on a low-lying coast: land use planning for wetlands

    Science.gov (United States)

    Larsen, Curt; Clark, Inga; Guntenspergen, Glenn; Cahoon, Don; Caruso, Vincent; Hupp, Cliff; Yanosky, Tom

    2004-01-01

    The Blackwater National Wildlife Refuge (BNWR), on the Eastern Shore of Chesapeake Bay (figure 1), occupies an area less than 1 meter above sea level. The Refuge has been featured prominently in studies of the impact of sea level rise on coastal wetlands. Most notably, the refuge has been sited by the Intergovernmental Panel on Climate Change (IPCC) as a key example of 'wetland loss' attributable to rising sea level due to global temperature increase. Comparative studies of aerial photos taken since 1938 show an expanding area of open water in the central area of the refuge. The expanding area of open water can be shown to parallel the record of sea level rise over the past 60 years. The U.S. Fish and Wildlife Service (FWS) manages the refuge to support migratory waterfowl and to preserve endangered upland species. High marsh vegetation is critical to FWS waterfowl management strategies. A broad area once occupied by high marsh has decreased with rising sea level. The FWS needs a planning tool to help predict current and future areas of high marsh available for waterfowl. 'Wetland loss' is a relative term. It is dependant on the boundaries chosen for measurement. Wetland vegetation, zoned by elevation and salinity (figure 3), respond to rising sea level. Wetlands migrate inland and upslope and may vary in areas depending on the adjacent land slopes. Refuge managers need a geospatial tool that allows them to predict future areas that will be converted to high and intertidal marsh. Shifts in location and area of coverage must be anticipated. Viability of a current marsh area is also important. When will sea level rise make short-term management strategies to maintain an area impractical? The USGS has developed an inundation model for the BNWR centered on the refuge and surrounding areas. Such models are simple in concept, but they require a detailed topographic map upon which to superimpose future sea level positions. The new system of LIDAR mapping of land and

  8. Nitrogen dynamics model for a pilot field-scale novel dewatered alum sludge cake-based constructed wetland system.

    Science.gov (United States)

    Kumar, J L G; Zhao, Y Q; Hu, Y S; Babatunde, A O; Zhao, X H

    2015-01-01

    A model simulating the effluent nitrogen (N) concentration of treated animal farm wastewater in a pilot on-site constructed wetland (CW) system, using dewatered alum sludge cake (DASC) as wetland substrate, is presented. The N-model was developed based on the Structural Thinking Experiential Learning Laboratory with Animation software and is considering organic nitrogen, ammonia nitrogen (NH3) and nitrate nitrogen (NO3-N) as the major forms of nitrogen involved in the transformation chains. Ammonification (AMM), ammonia volatilization, nitrification (NIT), denitrification, plant uptake, plant decaying and uptake of inorganic nitrogen by algae and bacteria were considered in this model. pH, dissolved oxygen, temperature, precipitation, solar radiation and nitrogen concentrations were considered as forcing functions in the model. The model was calibrated by observed data with a reasonable agreement prior to its applications. The simulated effluent detritus nitrogen, NH4-N, NO3-N and TN had a considerably good agreement with the observed results. The mass balance analysis shows that NIT accounts for 65.60%, adsorption (ad) (11.90%), AMM (8.90%) followed by NH4-N (Plants) (5.90%) and NO3-N (Plants) (4.40%). The TN removal was found 52% of the total influent TN in the CW. This study suggested an improved overall performance of a DASC-based CW and efficient N removal from wastewater.

  9. Research on Artificial Spider Web Model for Farmland Wireless Sensor Network

    OpenAIRE

    Jun Wang; Song Gao; Shimin Zhao; Guang Hu; Xiaoli Zhang; Guowang Xie

    2018-01-01

    Through systematic analysis of the structural characteristics and invulnerability of spider web, this paper explores the possibility of combining the advantages of spider web such as network robustness and invulnerability with farmland wireless sensor network. A universally applicable definition and mathematical model of artificial spider web structure are established. The comparison between artificial spider web and traditional networks is discussed in detail. The simulation result shows tha...

  10. A Model of Artificial Genotype and Norm of Reaction in a Robotic System

    OpenAIRE

    Durán Bosch, Ángel Juan; Pascual del Pobil Ferré, Ángel

    2016-01-01

    The genes of living organisms serve as large stores of information for replicating their behavior and morphology over generations. The evolutionary view of genetics that has inspired artificial systems with a Mendelian approach does not take into account the interaction between species and with the environment to generate a particular phenotype. In this paper, a genotype model is suggested to shape the relationship with the phenotype and the environment in an artificial system. A method to ob...

  11. An artificial neural network for modeling reliability, availability and maintainability of a repairable system

    International Nuclear Information System (INIS)

    Rajpal, P.S.; Shishodia, K.S.; Sekhon, G.S.

    2006-01-01

    The paper explores the application of artificial neural networks to model the behaviour of a complex, repairable system. A composite measure of reliability, availability and maintainability parameters has been proposed for measuring the system performance. The artificial neural network has been trained using past data of a helicopter transportation facility. It is used to simulate behaviour of the facility under various constraints. The insights obtained from results of simulation are useful in formulating strategies for optimal operation of the system

  12. Carbon-14 Specific Activity Model Validation for Biota in Wetland Environments

    International Nuclear Information System (INIS)

    Yankovich, T.L.; Sharp, K.J.; Benz, M.L.; Carr, J.; Killey, R.W.D.

    2008-01-01

    In many cases, contaminants, such as radionuclides, can show highly localized spatial distributions in natural systems. Therefore, a key question for environmental assessment and monitoring becomes, how can these localized distributions of contaminants in the environment lead to organism exposure, and ultimately, the potential for effects to receptor biota? To address this question, an important first step is to conduct field surveys at sites of interest to map out the spatial distribution and extent of contaminants in areas that are being occupied and utilized by resident receptor biota. Work can then be conducted to establish predictive relationships between contaminant concentrations in biota tissues and those in environmental media with which biota interact, to gain an understanding of how representative ambient contaminant concentrations are of biota exposure. The objectives of this study were: - To conduct a field survey in a wetland ecosystem to characterize the spatial distribution of carbon- 14 ( 14 C), a radionuclide with dynamics in natural systems that can be described using a specific activity model; and - To determine whether 14 C concentrations in environmental media reflect those measured in tissues of resident flora and fauna. A detailed field campaign was carried out in summer 2001 to characterize the spatial distribution and areal coverage of 14 C in Duke Swamp, a wetland ecosystem on Atomic Energy of Canada Limited (AECL)'s Chalk River Laboratories (CRL) site that receives 14 C through releases from an up-gradient Waste Management Area (WMA), primarily through groundwater influx. Sampling of surface vegetation (dominantly comprised of Sphagnum moss) was conducted at a total of 69 locations, with complementary sampling of air, soil, fungi, aerial insects, ground-dwelling insects, amphibians, small mammals and snakes being carried out at a subset of five locations with varying 14 C concentrations. Concentrations of 14 C in resident Duke Swamp

  13. Artificial neural network model of pork meat cubes osmotic dehydration

    OpenAIRE

    Pezo, Lato L.; Ćurčić, Biljana Lj.; Filipović, Vladimir S.; Nićetin, Milica R.; Koprivica, Gordana B.; Mišljenović, Nevena M.; Lević, Ljubinko B.

    2013-01-01

    Mass transfer of pork meat cubes (M. triceps brachii), shaped as 1x1x1 cm, during osmotic dehydration (OD) and under atmospheric pressure was investigated in this paper. The effects of different parameters, such as concentration of sugar beet molasses (60-80%, w/w), temperature (20-50ºC), and immersion time (1-5 h) in terms of water loss (WL), solid gain (SG), final dry matter content (DM), and water activity (aw), were investigated using experimental results. Five artificial neural net...

  14. Mapping pan-Arctic CH4 emissions using an adjoint method by integrating process-based wetland and lake biogeochemical models and atmospheric CH4 concentrations

    Science.gov (United States)

    Tan, Z.; Zhuang, Q.; Henze, D. K.; Frankenberg, C.; Dlugokencky, E. J.; Sweeney, C.; Turner, A. J.

    2015-12-01

    Understanding CH4 emissions from wetlands and lakes are critical for the estimation of Arctic carbon balance under fast warming climatic conditions. To date, our knowledge about these two CH4 sources is almost solely built on the upscaling of discontinuous measurements in limited areas to the whole region. Many studies indicated that, the controls of CH4 emissions from wetlands and lakes including soil moisture, lake morphology and substrate content and quality are notoriously heterogeneous, thus the accuracy of those simple estimates could be questionable. Here we apply a high spatial resolution atmospheric inverse model (nested-grid GEOS-Chem Adjoint) over the Arctic by integrating SCIAMACHY and NOAA/ESRL CH4 measurements to constrain the CH4 emissions estimated with process-based wetland and lake biogeochemical models. Our modeling experiments using different wetland CH4 emission schemes and satellite and surface measurements show that the total amount of CH4 emitted from the Arctic wetlands is well constrained, but the spatial distribution of CH4 emissions is sensitive to priors. For CH4 emissions from lakes, our high-resolution inversion shows that the models overestimate CH4 emissions in Alaskan costal lowlands and East Siberian lowlands. Our study also indicates that the precision and coverage of measurements need to be improved to achieve more accurate high-resolution estimates.

  15. Modeling of Soil Water and Salt Dynamics and Its Effects on Root Water Uptake in Heihe Arid Wetland, Gansu, China

    Directory of Open Access Journals (Sweden)

    Huijie Li

    2015-05-01

    Full Text Available In the Heihe River basin, China, increased salinity and water shortages present serious threats to the sustainability of arid wetlands. It is critical to understand the interactions between soil water and salts (from saline shallow groundwater and the river and their effects on plant growth under the influence of shallow groundwater and irrigation. In this study, the Hydrus-1D model was used in an arid wetland of the Middle Heihe River to investigate the effects of the dynamics of soil water, soil salinization, and depth to water table (DWT as well as groundwater salinity on Chinese tamarisk root water uptake. The modeled soil water and electrical conductivity of soil solution (ECsw are in good agreement with the observations, as indicated by RMSE values (0.031 and 0.046 cm3·cm−3 for soil water content, 0.037 and 0.035 dS·m−1 for ECsw, during the model calibration and validation periods, respectively. The calibrated model was used in scenario analyses considering different DWTs, salinity levels and the introduction of preseason irrigation. The results showed that (I Chinese tamarisk root distribution was greatly affected by soil water and salt distribution in the soil profile, with about 73.8% of the roots being distributed in the 20–60 cm layer; (II root water uptake accounted for 91.0% of the potential maximal value when water stress was considered, and for 41.6% when both water and salt stress were considered; (III root water uptake was very sensitive to fluctuations of the water table, and was greatly reduced when the DWT was either dropped or raised 60% of the 2012 reference depth; (IV arid wetland vegetation exhibited a high level of groundwater dependence even though shallow groundwater resulted in increased soil salinization and (V preseason irrigation could effectively increase root water uptake by leaching salts from the root zone. We concluded that a suitable water table and groundwater salinity coupled with proper irrigation

  16. Artificial neural network models for biomass gasification in fluidized bed gasifiers

    DEFF Research Database (Denmark)

    Puig Arnavat, Maria; Hernández, J. Alfredo; Bruno, Joan Carles

    2013-01-01

    Artificial neural networks (ANNs) have been applied for modeling biomass gasification process in fluidized bed reactors. Two architectures of ANNs models are presented; one for circulating fluidized bed gasifiers (CFB) and the other for bubbling fluidized bed gasifiers (BFB). Both models determine...

  17. Development and validation of deterioration models for concrete bridge decks - phase 1 : artificial intelligence models and bridge management system.

    Science.gov (United States)

    2013-06-01

    This research documents the development and evaluation of artificial neural network (ANN) models to predict the condition ratings of concrete highway bridge decks in Michigan. Historical condition assessments chronicled in the national bridge invento...

  18. Artificially Structured Semiconductors to Model Novel Quantum Phenomena

    Energy Technology Data Exchange (ETDEWEB)

    Pinczuk, Aron [Columbia Univ., New York, NY (United States). Dept. of Applied Physics and Applied Mathematics; Wind, Shalom J. [Columbia Univ., New York, NY (United States). Dept. of Applied Physics and Applied Mathematics

    2018-01-13

    Award Period: September 1st, 2013 through February 15th, 2017 Submitted to the USDOE Office of Basic Energy Sciences By Aron Pinczuk and Shalom J. Wind Department of Applied Physics and Applied Mathematics Columbia University New York, NY 10027 January 2017 Award # DE-SC0010695 ABSTRACT Research in this project seeks to design, create and study a class of tunable artificial quantum structures in order to extend the range and scope of new and exciting physical phenomena and to explore the potential for new applications. Advanced nanofabrication was used to create an external potential landscape that acts as a lattice of confinement sites for electrons (and/or holes) in a two-dimensional electron gas in a high perfection semiconductor in such a manner that quantum interactions between different sites dictate the significant physics. Our current focus is on ‘artificial graphene’ (AG) in which a set of quantum dots (or sites) are patterned in a honeycomb lattice. The combination of leading edge nanofabrication with ultra-pure semiconductor materials in this project extends the frontier for small period, low-disorder AG systems, enabling the exploration of graphene physics in a semiconductor platform. TECHNICAL DESCRIPTION Contemporary condensed matter science has entered an era of discovery of new low-dimensional materials, such as graphene and other atomically thin materials, that exhibit exciting new physical phenomena that were previously inaccessible. Concurrent with the discovery and development of these new materials are impressive advancements in nanofabrication, which offer an ever-expanding toolbox for creating a myriad of high quality patterns at nanoscale dimensions. This project started about four years ago. Among its major achievements are the realizations of very small period artificial lattices with honeycomb topology in GaAs quantum wells. In our most recent work the periods of the ‘artificial graphene’ (AG) lattices extend down to 40 nm. These

  19. A Carbon Cycle Model for the Social-Ecological Process in Coastal Wetland: A Case Study on Gouqi Island, East China

    Directory of Open Access Journals (Sweden)

    Yanxia Li

    2017-01-01

    Full Text Available Coastal wetlands offer many important ecosystem services both in natural and in social systems. How to simultaneously decrease the destructive effects flowing from human activities and maintaining the sustainability of regional wetland ecosystems are an important issue for coastal wetlands zones. We use carbon credits as the basis for regional sustainable developing policy-making. With the case of Gouqi Island, a typical coastal wetlands zone that locates in the East China Sea, a carbon cycle model was developed to illustrate the complex social-ecological processes. Carbon-related processes in natural ecosystem, primary industry, secondary industry, tertiary industry, and residents on the island were identified in the model. The model showed that 36780 tons of carbon is released to atmosphere with the form of CO2, and 51240 tons of carbon is captured by the ecosystem in 2014 and the three major resources of carbon emission are transportation and tourism development and seawater desalination. Based on the carbon-related processes and carbon balance, we proposed suggestions on the sustainable development strategy of Gouqi Island as coastal wetlands zone.

  20. A Carbon Cycle Model for the Social-Ecological Process in Coastal Wetland: A Case Study on Gouqi Island, East China

    Science.gov (United States)

    Xiong, Lihu; Zhu, Wenjia

    2017-01-01

    Coastal wetlands offer many important ecosystem services both in natural and in social systems. How to simultaneously decrease the destructive effects flowing from human activities and maintaining the sustainability of regional wetland ecosystems are an important issue for coastal wetlands zones. We use carbon credits as the basis for regional sustainable developing policy-making. With the case of Gouqi Island, a typical coastal wetlands zone that locates in the East China Sea, a carbon cycle model was developed to illustrate the complex social-ecological processes. Carbon-related processes in natural ecosystem, primary industry, secondary industry, tertiary industry, and residents on the island were identified in the model. The model showed that 36780 tons of carbon is released to atmosphere with the form of CO2, and 51240 tons of carbon is captured by the ecosystem in 2014 and the three major resources of carbon emission are transportation and tourism development and seawater desalination. Based on the carbon-related processes and carbon balance, we proposed suggestions on the sustainable development strategy of Gouqi Island as coastal wetlands zone. PMID:28286690

  1. Non point source pollution modelling in the watershed managed by Integrated Conctructed Wetlands: A GIS approach.

    OpenAIRE

    Vyavahare, Nilesh

    2008-01-01

    The non-point source pollution has been recognised as main cause of eutrophication in Ireland (EPA Ireland, 2001). Integrated Constructed Wetland (ICW) is a management practice adopted in Annestown stream watershed, located in the south county of Waterford in Ireland, used to cleanse farmyard runoff. Present study forms the annual pollution budget for the Annestown stream watershed. The amount of pollution from non-point sources flowing into the stream was simulated by using GIS techniques; u...

  2. Standard representation and unified stability analysis for dynamic artificial neural network models.

    Science.gov (United States)

    Kim, Kwang-Ki K; Patrón, Ernesto Ríos; Braatz, Richard D

    2018-02-01

    An overview is provided of dynamic artificial neural network models (DANNs) for nonlinear dynamical system identification and control problems, and convex stability conditions are proposed that are less conservative than past results. The three most popular classes of dynamic artificial neural network models are described, with their mathematical representations and architectures followed by transformations based on their block diagrams that are convenient for stability and performance analyses. Classes of nonlinear dynamical systems that are universally approximated by such models are characterized, which include rigorous upper bounds on the approximation errors. A unified framework and linear matrix inequality-based stability conditions are described for different classes of dynamic artificial neural network models that take additional information into account such as local slope restrictions and whether the nonlinearities within the DANNs are odd. A theoretical example shows reduced conservatism obtained by the conditions. Copyright © 2017. Published by Elsevier Ltd.

  3. Improved Management of the Nile River Basin Through Modeling the Sudd, a Wetland with Vital Socioeconomic and Environmental Services

    Science.gov (United States)

    Di Vittorio, C.; Georgakakos, A. P.

    2017-12-01

    The Sudd is a vast, remote wetland in South Sudan and a vital component of the Nile River Basin. While decision support tools like the Nile Decision Support Tool (Nile DST) estimate the amount of water flowing through the Sudd, they do not account for other wetland processes that sustain the ecosystem diversity and the pastoral way of life for nearly two million people who live in the area (Howell et al. 1988). An accurate hydrologic model of the Sudd would enable policy makers to appreciate and manage it in a way that benefits local inhabitants as well as the 500 million people living within the Nile region (NBI, 2016). Currently, the most widely accepted model of the Sudd was developed by Sutcliffe and Parks (1999) and is a lumped mass balance model that accounts for key water fluxes. Estimates of the aerial extent of flooding obtained from satellite and airborne imagery on a few dates were used to calibrate the model parameters over the 1905-1983 period. During the AGU Fall 2016 meeting, we presented a method for deriving the dynamic flooding extents of the Sudd on a monthly temporal resolution from 2000-2015 using MODIS (Moderate Resolution Imaging Spectrometer) land surface reflectance data (Di Vittorio & Georgakakos, 2017 in press). In the study presented here, we have used this new information to evaluate the Sutcliffe and Park's model, highlight its shortcomings, and suggest alternative modeling approaches that are accurate enough to incorporate into water management models. The alternative modelling approaches include statistical and physically based models, and the incorporation of satellite-based hydrometeorlogical data sets. This improved hydrologic model will allow stakeholders in this sensitive world region to better understand how current and future climate and water management scenarios will impact the Sudd ecosystem and local economy. References: Di Vittorio, C. A., Georgakakos, A.P. (2017). Land cover classification and wetland inundation mapping

  4. Recognition of NEMP and LEMP signals based on auto-regression model and artificial neutral network

    International Nuclear Information System (INIS)

    Li Peng; Song Lijun; Han Chao; Zheng Yi; Cao Baofeng; Li Xiaoqiang; Zhang Xueqin; Liang Rui

    2010-01-01

    Auto-regression (AR) model, one power spectrum estimation method of stationary random signals, and artificial neutral network were adopted to recognize nuclear and lightning electromagnetic pulses. Self-correlation function and Burg algorithms were used to acquire the AR model coefficients as eigenvalues, and BP artificial neural network was introduced as the classifier with different numbers of hidden layers and hidden layer nodes. The results show that AR model is effective in those signals, feature extraction, and the Burg algorithm is more effective than the self-correlation function algorithm. (authors)

  5. Robust nonlinear autoregressive moving average model parameter estimation using stochastic recurrent artificial neural networks

    DEFF Research Database (Denmark)

    Chon, K H; Hoyer, D; Armoundas, A A

    1999-01-01

    In this study, we introduce a new approach for estimating linear and nonlinear stochastic autoregressive moving average (ARMA) model parameters, given a corrupt signal, using artificial recurrent neural networks. This new approach is a two-step approach in which the parameters of the deterministic...... part of the stochastic ARMA model are first estimated via a three-layer artificial neural network (deterministic estimation step) and then reestimated using the prediction error as one of the inputs to the artificial neural networks in an iterative algorithm (stochastic estimation step). The prediction...... error is obtained by subtracting the corrupt signal of the estimated ARMA model obtained via the deterministic estimation step from the system output response. We present computer simulation examples to show the efficacy of the proposed stochastic recurrent neural network approach in obtaining accurate...

  6. Spatially Distributed Assimilation of Remotely Sensed Leaf Area Index and Potential Evapotranspiration for Hydrologic Modeling in Wetland Landscapes

    Science.gov (United States)

    Rajib, A.; Evenson, G. R.; Golden, H. E.; Lane, C.

    2017-12-01

    Evapotranspiration (ET), a highly dynamic flux in wetland landscapes, regulates the accuracy of surface/sub-surface runoff simulation in a hydrologic model. Accordingly, considerable uncertainty in simulating ET-related processes remains, including our limited ability to incorporate realistic ground conditions, particularly those involved with complex land-atmosphere feedbacks, vegetation growth, and energy balances. Uncertainty persists despite using high resolution topography and/or detailed land use data. Thus, a good hydrologic model can produce right answers for wrong reasons. In this study, we develop an efficient approach for multi-variable assimilation of remotely sensed earth observations (EOs) into a hydrologic model and apply it in the 1700 km2 Pipestem Creek watershed in the Prairie Pothole Region of North Dakota, USA. Our goal is to employ EOs, specifically Leaf Area Index (LAI) and Potential Evapotranspiration (PET), as surrogates for the aforementioned processes without overruling the model's built-in physical/semi-empirical process conceptualizations. To do this, we modified the source code of an already-improved version of the Soil and Water Assessment Tool (SWAT) for wetland hydrology (Evenson et al. 2016 HP 30(22):4168) to directly assimilate remotely-sensed LAI and PET (obtained from the 500 m and 1 km Moderate Resolution Imaging Spectroradiometer (MODIS) gridded products, respectively) into each model Hydrologic Response Unit (HRU). Two configurations of the model, one with and one without EO assimilation, are calibrated against streamflow observations at the watershed outlet. Spatio-temporal changes in the HRU-level water balance, based on calibrated outputs, are evaluated using MODIS Actual Evapotranspiration (AET) as a reference. It is expected that the model configuration having remotely sensed LAI and PET, will simulate more realistic land-atmosphere feedbacks, vegetation growth and energy balance. As a result, this will decrease simulated

  7. Artificial intelligence in process control: Knowledge base for the shuttle ECS model

    Science.gov (United States)

    Stiffler, A. Kent

    1989-01-01

    The general operation of KATE, an artificial intelligence controller, is outlined. A shuttle environmental control system (ECS) demonstration system for KATE is explained. The knowledge base model for this system is derived. An experimental test procedure is given to verify parameters in the model.

  8. Pseudo dynamic transitional modeling of building heating energy demand using artificial neural network

    NARCIS (Netherlands)

    Paudel, S.; Elmtiri, M.; Kling, W.L.; Corre, le O.; Lacarriere, B.

    2014-01-01

    This paper presents the building heating demand prediction model with occupancy profile and operational heating power level characteristics in short time horizon (a couple of days) using artificial neural network. In addition, novel pseudo dynamic transitional model is introduced, which consider

  9. Development of surrogate models using artificial neural network for building shell energy labelling

    NARCIS (Netherlands)

    Melo, A.P.; Costola, D.; Lamberts, R.; Hensen, J.L.M.

    2014-01-01

    Surrogate models are an important part of building energy labelling programs, but these models still present low accuracy, particularly in cooling-dominated climates. The objective of this study was to evaluate the feasibility of using an artificial neural network (ANN) to improve the accuracy of

  10. Comparative nonlinear modeling of renal autoregulation in rats: Volterra approach versus artificial neural networks

    DEFF Research Database (Denmark)

    Chon, K H; Holstein-Rathlou, N H; Marsh, D J

    1998-01-01

    kernel estimation method based on Laguerre expansions. The results for the two types of artificial neural networks and the Volterra models are comparable in terms of normalized mean square error (NMSE) of the respective output prediction for independent testing data. However, the Volterra models obtained...

  11. Inferring energy sources in constructed wetlands through stable isotope analysis of microbial biofilms

    Energy Technology Data Exchange (ETDEWEB)

    Jurkowski, K.; Ciborowski, J. [Windsor Univ., ON (Canada); Daly, C. [Suncor Energy, Fort McMurray, AB (Canada)

    2010-07-01

    This study presented a novel method of sequestering the microbial biofilm in constructed wetland ecosystems. Artificial substrates were fixed within 8 wetlands differing in age and construction materials over a 2 year period at oil sands lease sites in northeastern Alberta. Autotrophic and heterotrophic biofilm samples were collected from both the subsurface and epibenthic zones of the pipe surfaces of each submerged substrate assembly. A mixing model of d13C, d15N and d34S isotopic signatures was used to assess the contribution of 4 potential nutrient sources of the biofilm. Samples included dominant living and senescent emergent as well as submergent macrophytes, particulate organic matter, dissolved organic carbon, and invertebrates. The samples were collected to compare the biofilm signatures of each wetland in relation to the heterotrophic processes caused by the assimilation of oil sands-derived hydrocarbons and autochthonous detrital pools.

  12. Inferring energy sources in constructed wetlands through stable isotope analysis of microbial biofilms

    International Nuclear Information System (INIS)

    Jurkowski, K.; Ciborowski, J.; Daly, C.

    2010-01-01

    This study presented a novel method of sequestering the microbial biofilm in constructed wetland ecosystems. Artificial substrates were fixed within 8 wetlands differing in age and construction materials over a 2 year period at oil sands lease sites in northeastern Alberta. Autotrophic and heterotrophic biofilm samples were collected from both the subsurface and epibenthic zones of the pipe surfaces of each submerged substrate assembly. A mixing model of d13C, d15N and d34S isotopic signatures was used to assess the contribution of 4 potential nutrient sources of the biofilm. Samples included dominant living and senescent emergent as well as submergent macrophytes, particulate organic matter, dissolved organic carbon, and invertebrates. The samples were collected to compare the biofilm signatures of each wetland in relation to the heterotrophic processes caused by the assimilation of oil sands-derived hydrocarbons and autochthonous detrital pools.

  13. A photoactivated artificial muscle model unit: reversible, photoinduced sliding of nanosheets.

    Science.gov (United States)

    Nabetani, Yu; Takamura, Hazuki; Hayasaka, Yuika; Shimada, Tetsuya; Takagi, Shinsuke; Tachibana, Hiroshi; Masui, Dai; Tong, Zhiwei; Inoue, Haruo

    2011-11-02

    A novel photoactivated artificial muscle model unit is reported. Here we show that organic/inorganic hybrid nanosheets reversibly slide horizontally on a giant scale and the interlayer spaces in the layered hybrid structure shrink and expand vertically by photoirradiation. The sliding movement of the system on a giant scale is the first example of an artificial muscle model unit having much similarity with that in natural muscle fibrils. In particular, our layered hybrid molecular system exhibits a macroscopic morphological change on a giant scale (~1500 nm) relative to the molecular size of ~1 nm by means of a reversible sliding mechanism.

  14. Determination of the Corona model parameters with artificial neural networks

    International Nuclear Information System (INIS)

    Ahmet, Nayir; Bekir, Karlik; Arif, Hashimov

    2005-01-01

    Full text : The aim of this study is to calculate new model parameters taking into account the corona of electrical transmission line wires. For this purpose, a neural network modeling proposed for the corona frequent characteristics modeling. Then this model was compared with the other model developed at the Polytechnic Institute of Saint Petersburg. The results of development of the specified corona model for calculation of its influence on the wave processes in multi-wires line and determination of its parameters are submitted. Results of obtained calculation equations are brought for electrical transmission line with allowance for superficial effect in the ground and wires with reference to developed corona model

  15. Associations between the molecular and optical properties of dissolved organic matter in the Florida Everglades, a model coastal wetland system

    Science.gov (United States)

    Wagner, Sasha; Jaffe, Rudolf; Cawley, Kaelin; Dittmar, Thorsten; Stubbins, Aron

    2015-11-01

    Optical properties are easy-to-measure proxies for dissolved organic matter (DOM) composition, source and reactivity. However, the molecular signature of DOM associated with such optical parameters remains poorly defined. The Florida coastal Everglades is a subtropical wetland with diverse vegetation (e.g., sawgrass prairies, mangrove forests, seagrass meadows) and DOM sources (e.g., terrestrial, microbial and marine). As such, the Everglades is an excellent model system from which to draw samples of diverse origin and composition to allow classically-defined optical properties to be linked to molecular properties of the DOM pool. We characterized a suite of seasonally- and spatially-collected DOM samples using optical measurements (EEM-PARAFAC, SUVA254, S275-295, S350-400, SR, FI, freshness index and HIX) and ultrahigh resolution mass spectrometry (FTICR-MS). Spearman’s rank correlations between FTICR-MS signal intensities of individual molecular formulae and optical properties determined which molecular formulae were associated with each PARAFAC component and optical index. The molecular families that tracked with the optical indices were generally in agreement with conventional biogeochemical interpretations. Therefore, although they represent only a small portion of the bulk DOM pool, absorbance and fluorescence measurements appear to be appropriate proxies for the aquatic cycling of both optically-active and associated optically-inactive DOM in coastal wetlands.

  16. Associations between the molecular and optical properties of dissolved organic matter in the Florida Everglades, a model coastal wetland system

    Directory of Open Access Journals (Sweden)

    Sasha eWagner

    2015-11-01

    Full Text Available Optical properties are easy-to-measure proxies for dissolved organic matter (DOM composition, source and reactivity. However, the molecular signature of DOM associated with such optical parameters remains poorly defined. The Florida coastal Everglades is a subtropical wetland with diverse vegetation (e.g., sawgrass prairies, mangrove forests, seagrass meadows and DOM sources (e.g., terrestrial, microbial and marine. As such, the Everglades is an excellent model system from which to draw samples of diverse origin and composition to allow classically-defined optical properties to be linked to molecular properties of the DOM pool. We characterized a suite of seasonally- and spatially-collected DOM samples using optical measurements (EEM-PARAFAC, SUVA254, S275-295, S350-400, SR, FI, freshness index and HIX and ultrahigh resolution mass spectrometry (FTICR-MS. Spearman’s rank correlations between FTICR-MS signal intensities of individual molecular formulae and optical properties determined which molecular formulae were associated with each PARAFAC component and optical index. The molecular families that tracked with the optical indices were generally in agreement with conventional biogeochemical interpretations. Therefore, although they represent only a small portion of the bulk DOM pool, absorbance and fluorescence measurements appear to be appropriate proxies for the aquatic cycling of both optically-active and associated optically-inactive DOM in coastal wetlands.

  17. Solid respirometry to characterize nitrification kinetics: a better insight for modelling nitrogen conversion in vertical flow constructed wetlands.

    Science.gov (United States)

    Morvannou, Ania; Choubert, Jean-Marc; Vanclooster, Marnik; Molle, Pascal

    2011-10-15

    We developed an original method to measure nitrification rates at different depths of a vertical flow constructed wetland (VFCW) with variable contents of organic matter (sludge, colonized gravel). The method was adapted for organic matter sampled in constructed wetland (sludge, colonized gravel) operated under partially saturated conditions and is based on respirometric principles. Measurements were performed on a reactor, containing a mixture of organic matter (sludge, colonized gravel) mixed with a bulking agent (wood), on which an ammonium-containing liquid was applied. The oxygen demand was determined from analysing oxygen concentration of the gas passing through the reactor with an on-line analyzer equipped with a paramagnetic detector. Within this paper we present the overall methodology, the factors influencing the measurement (sample volume, nature and concentration of the applied liquid, number of successive applications), and the robustness of the method. The combination of this new method with a mass balance approach also allowed determining the concentration and maximum growth rate of the autotrophic biomass in different layers of a VFCW. These latter parameters are essential inputs for the VFCW plant modelling. Copyright © 2011 Elsevier Ltd. All rights reserved.

  18. National Wetlands Inventory Points

    Data.gov (United States)

    Minnesota Department of Natural Resources — Wetland point features (typically wetlands that are too small to be as area features at the data scale) mapped as part of the National Wetlands Inventory (NWI). The...

  19. National Wetlands Inventory Polygons

    Data.gov (United States)

    Minnesota Department of Natural Resources — Wetland area features mapped as part of the National Wetlands Inventory (NWI). The National Wetlands Inventory is a national program sponsored by the US Fish and...

  20. Application of artificial neural network model for groundwater level forecasting in a river island with artificial influencing factors

    Science.gov (United States)

    Lee, Sanghoon; Yoon, Heesung; Park, Byeong-Hak; Lee, Kang-Kun

    2017-04-01

    Groundwater use has been increased for various purposes like agriculture, industry or drinking water in recent years, the issue related to sustainability on the groundwater use also has been raised. Accordingly, forecasting the groundwater level is of great importance for planning sustainable use of groundwater. In a small island surrounded by the Han River, South Korea, seasonal fluctuation of the groundwater level is characterized by multiple factors such as recharge/discharge event of the Paldang dam, Water Curtain Cultivation (WCC) during the winter season, operation of Groundwater Heat Pump System (GWHP). For a period when the dam operation is only occurred in the study area, a prediction of the groundwater level can be easily achieved by a simple cross-correlation model. However, for a period when the WCC and the GWHP systems are working together, the groundwater level prediction is challenging due to its unpredictable operation of the two systems. This study performed Artificial Neural Network (ANN) model to forecast the groundwater level in the river area reflecting the various predictable/unpredictable factors. For constructing the ANN models, two monitoring wells, YSN1 and YSO8, which are located near the injection and abstraction wells for the GWHP system were selected, respectively. By training with the groundwater level data measured in January 2015 to August 2015, response of groundwater level by each of the surface water level, the WCC and the GWHP system were evaluated. Consequentially, groundwater levels in December 2015 to March 2016 were predicted by ANN models, providing optimal fits in comparison to the observed water levels. This study suggests that the ANN model is a useful tool to forecast the groundwater level in terms of the management of groundwater. Acknowledgement : Financial support was provided by the "R&D Project on Environmental Management of Geologic CO2 Storage" from the KEITI (Project Number: 2014001810003) This research was

  1. Modeling Broadband Microwave Structures by Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    V. Otevrel

    2004-06-01

    Full Text Available The paper describes the exploitation of feed-forward neural networksand recurrent neural networks for replacing full-wave numerical modelsof microwave structures in complex microwave design tools. Building aneural model, attention is turned to the modeling accuracy and to theefficiency of building a model. Dealing with the accuracy, we describea method of increasing it by successive completing a training set.Neural models are mutually compared in order to highlight theiradvantages and disadvantages. As a reference model for comparisons,approximations based on standard cubic splines are used. Neural modelsare used to replace both the time-domain numeric models and thefrequency-domain ones.

  2. Modeling and prediction of Turkey's electricity consumption using Artificial Neural Networks

    International Nuclear Information System (INIS)

    Kavaklioglu, Kadir; Ozturk, Harun Kemal; Canyurt, Olcay Ersel; Ceylan, Halim

    2009-01-01

    Artificial Neural Networks are proposed to model and predict electricity consumption of Turkey. Multi layer perceptron with backpropagation training algorithm is used as the neural network topology. Tangent-sigmoid and pure-linear transfer functions are selected in the hidden and output layer processing elements, respectively. These input-output network models are a result of relationships that exist among electricity consumption and several other socioeconomic variables. Electricity consumption is modeled as a function of economic indicators such as population, gross national product, imports and exports. It is also modeled using export-import ratio and time input only. Performance comparison among different models is made based on absolute and percentage mean square error. Electricity consumption of Turkey is predicted until 2027 using data from 1975 to 2006 along with other economic indicators. The results show that electricity consumption can be modeled using Artificial Neural Networks, and the models can be used to predict future electricity consumption. (author)

  3. Wastewater treatment by a pilot system of artificial wetlands: removal evaluation of the organic load; Tratamiento de aguas residuales por un sistema piloto de humedales artificiales: evaluacion de la remocion de la carga organica

    Energy Technology Data Exchange (ETDEWEB)

    Romero Aguilar, Mariana [Centro de Investigacion en Biotecnologia, Universidad Autonoma del Estado de Morelos, Cuernavaca, Morelos (Mexico)]. E-mail: ortizhl@uaem.mx; Colin Cruz, Arturo [Facultad de Quimica, Universidad Autonoma del Estado de Mexico, Toluca, Estado de Mexico (Mexico); Sanchez Salinas, Enrique; Ortiz Hernandez, Ma. Laura [Centro de Investigacion en Biotecnologia, Universidad Autonoma del Estado de Morelos, Cuernavaca, Morelos (Mexico)

    2009-08-15

    Wastewater treatment is a priority at the global level, because it is important to have enough water of good quality, which will allow an improvement of environment, health and life quality. In Mexico, because of insufficient infrastructure, high costs, lack of maintenance and qualified staff, only 36 % of the generated wastewaters are treated, which generates the need for developing alternative technologies for their depuration. Artificial wetlands are an alternative due their high efficiency for removal of polluting agents and their low installation and maintenance costs. This paper evaluates the removal percentage of the organic charge of wastewaters in a treatment system of artificial wetlands of horizontal flux, with two vegetal species. The system was designed with three modules installed in a sequential way. At the first one, organisms of the species Phragmites australis (Cav.) Trin. ex Steudel were integrated; at the second, organisms of the species Typha dominguensis (Pers.) Steudel, and at the third, both species. The experimental modules were installed at the effluent of a primary treatment, which contains municipal wastewater coming from a research building. The following parameters were analyzed in the water: chemical oxygen demand (COD), ions of nitrogen (N-NO{sub 3}-, N-NO{sub 2}- y N-NH{sub 4}{sup +}) and total phosphorus. Additionally, the total count of bacteria associated to the system was evaluated. Results showed that the system is an option for the removal of organic matter and nutrients, of low operation and maintenance costs. [Spanish] El tratamiento de las aguas residuales es una cuestion prioritaria a nivel mundial, ya que es importante disponer de agua de calidad y en cantidad suficiente, lo que permitira una mejora del ambiente, la salud y la calidad de vida. En Mexico, debido a la insuficiente infraestructura, los altos costos, la falta de mantenimiento y de personal capacitado, solo 36 % de las aguas residuales generadas reciben

  4. A multiscale approach for modeling actuation response of polymeric artificial muscles.

    Science.gov (United States)

    Sharafi, Soodabeh; Li, Guoqiang

    2015-05-21

    Artificial muscles are emerging materials in the field of smart materials with applications in aerospace, robotic, and biomedical industries. Despite extensive experimental investigations in this field, there is a need for numerical modeling techniques that facilitate cutting edge research and development. This work aims at studying an artificial muscle made of twisted Nylon 6.6 fibers that are highly cold-drawn. A computationally efficient phenomenological thermo-mechanical constitutive model is developed in which several physical properties of the artificial muscles are incorporated to minimize the trial-and-error numerical curve fitting processes. Two types of molecular chains are considered at the micro-scale level that control training and actuation processes viz. (a) helically oriented chains which are structural switches that store a twisted shape in their low temperature phase and restore their random configuration during the thermal actuation process, and (b) entropic chains which are highly drawn chains that could actuate as soon as the muscle heats up, and saturates when coil contact temperature is reached. The thermal actuation response of the muscle over working temperatures has been elaborated in the Modeling section. The performance of the model is validated by available experiments in the literature. The model may provide a design platform for future artificial muscle developments.

  5. Gender Differences in Sexual Attraction and Moral Judgment: Research With Artificial Face Models.

    Science.gov (United States)

    González-Álvarez, Julio; Cervera-Crespo, Teresa

    2018-01-01

    Sexual attraction in humans is influenced by cultural or moral factors, and some gender differences can emerge in this complex interaction. A previous study found that men dissociate sexual attraction from moral judgment more than women do. Two experiments consisting of giving attractiveness ratings to photos of real opposite-sex individuals showed that men, compared to women, were significantly less influenced by the moral valence of a description about the person shown in each photo. There is evidence of some processing differences between real and artificial computer-generated faces. The present study tests the robustness of González-Álvarez's findings and extends the research to an experimental design using artificial face models as stimuli. A sample of 88 young adults (61 females and 27 males, average age 19.32, SD = 2.38) rated the attractiveness of 80 3D artificial face models generated with the FaceGen Modeller 3.5 software. Each face model was paired with a "good" and a "bad" (from a moral point of view) sentence depicting a quality or activity of the person represented in the model (e.g., she/he is an altruistic nurse in Africa vs. she/he is a prominent drug dealer). Results were in line with the previous findings and showed that, with artificial faces as well, sexual attraction is less influenced by morality in men than in women. This gender difference is consistent with an evolutionary perspective on human sexuality.

  6. Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Federico Nuñez-Piña

    2018-01-01

    Full Text Available The problem of assigning buffers in a production line to obtain an optimum production rate is a combinatorial problem of type NP-Hard and it is known as Buffer Allocation Problem. It is of great importance for designers of production systems due to the costs involved in terms of space requirements. In this work, the relationship among the number of buffer slots, the number of work stations, and the production rate is studied. Response surface methodology and artificial neural network were used to develop predictive models to find optimal throughput values. 360 production rate values for different number of buffer slots and workstations were used to obtain a fourth-order mathematical model and four hidden layers’ artificial neural network. Both models have a good performance in predicting the throughput, although the artificial neural network model shows a better fit (R=1.0000 against the response surface methodology (R=0.9996. Moreover, the artificial neural network produces better predictions for data not utilized in the models construction. Finally, this study can be used as a guide to forecast the maximum or near maximum throughput of production lines taking into account the buffer size and the number of machines in the line.

  7. Integrating Artificial Neural Networks into the VIC Model for Rainfall-Runoff Modeling

    Directory of Open Access Journals (Sweden)

    Changqing Meng

    2016-09-01

    Full Text Available A hybrid rainfall-runoff model was developed in this study by integrating the variable infiltration capacity (VIC model with artificial neural networks (ANNs. In the proposed model, the prediction interval of the ANN replaces separate, individual simulation (i.e., single simulation. The spatial heterogeneity of horizontal resolution, subgrid-scale features and their influence on the streamflow can be assessed according to the VIC model. In the routing module, instead of a simple linear superposition of the streamflow generated from each subbasin, ANNs facilitate nonlinear mappings of the streamflow produced from each subbasin into the total streamflow at the basin outlet. A total of three subbasins were delineated and calibrated independently via the VIC model; daily runoff errors were simulated for each subbasin, then corrected by an ANN bias-correction model. The initial streamflow and corrected runoff from the simulation for individual subbasins serve as inputs to the ANN routing model. The feasibility of this proposed method was confirmed according to the performance of its application to a case study on rainfall-runoff prediction in the Jinshajiang River Basin, the headwater area of the Yangtze River.

  8. Tidal-Fluvial and Estuarine Processes in the Lower Columbia River: II. Water Level Models, Floodplain Wetland Inundation, and System Zones

    Energy Technology Data Exchange (ETDEWEB)

    Jay, David A.; Borde, Amy B.; Diefenderfer, Heida L.

    2016-04-26

    Spatially varying water-level regimes are a factor controlling estuarine and tidal-fluvial wetland vegetation patterns. As described in Part I, water levels in the Lower Columbia River and estuary (LCRE) are influenced by tides, river flow, hydropower operations, and coastal processes. In Part II, regression models based on tidal theory are used to quantify the role of these processes in determining water levels in the mainstem river and floodplain wetlands, and to provide 21-year inundation hindcasts. Analyses are conducted at 19 LCRE mainstem channel stations and 23 tidally exposed floodplain wetland stations. Sum exceedance values (SEVs) are used to compare wetland hydrologic regimes at different locations on the river floodplain. A new predictive tool is introduced and validated, the potential SEV (pSEV), which can reduce the need for extensive new data collection in wetland restoration planning. Models of water levels and inundation frequency distinguish four zones encompassing eight reaches. The system zones are the wave- and current-dominated Entrance to river kilometer (rkm) 5; the Estuary (rkm-5 to 87), comprised of a lower reach with salinity, the energy minimum (where the turbidity maximum normally occurs), and an upper estuary reach without salinity; the Tidal River (rkm-87 to 229), with lower, middle, and upper reaches in which river flow becomes increasingly dominant over tides in determining water levels; and the steep and weakly tidal Cascade (rkm-229 to 234) immediately downstream from Bonneville Dam. The same zonation is seen in the water levels of floodplain stations, with considerable modification of tidal properties. The system zones and reaches defined here reflect geological features and their boundaries are congruent with five wetland vegetation zones

  9. Transport energy demand modeling of South Korea using artificial neural network

    International Nuclear Information System (INIS)

    Geem, Zong Woo

    2011-01-01

    Artificial neural network models were developed to forecast South Korea's transport energy demand. Various independent variables, such as GDP, population, oil price, number of vehicle registrations, and passenger transport amount, were considered and several good models (Model 1 with GDP, population, and passenger transport amount; Model 2 with GDP, number of vehicle registrations, and passenger transport amount; and Model 3 with oil price, number of vehicle registrations, and passenger transport amount) were selected by comparing with multiple linear regression models. Although certain regression models obtained better R-squared values than neural network models, this does not guarantee the fact that the former is better than the latter because root mean squared errors of the former were much inferior to those of the latter. Also, certain regression model had structural weakness based on P-value. Instead, neural network models produced more robust results. Forecasted results using the neural network models show that South Korea will consume around 37 MTOE of transport energy in 2025. - Highlights: → Transport energy demand of South Korea was forecasted using artificial neural network. → Various variables (GDP, population, oil price, number of registrations, etc.) were considered. → Results of artificial neural network were compared with those of multiple linear regression.

  10. Effectiveness of a model constructed wetland system containing Cyperus papyrus in degrading diesel oil

    Science.gov (United States)

    Harbowo, Danni Gathot; Choesin, Devi Nandita

    2014-03-01

    Synergism between wetland systems and the provision of degrading bacterial inoculum is now being developed for the recovery of areas polluted waters of pollutants. In connection with the frequent cases of diesel oil pollution in the waters of Indonesia, we need a way of water treatment as an efficient. In this study conducted a series of tests to develop an construcred wetland design that can effectively degrade diesel oil. Tested five systems: blanko (A), substrated, without bacterial inoculums, and vegetation (B); with the addition of inoculum (C); subsrated and vegetated (D); substrated and vegetated with the addition of inoculum (E). Vegetation used in this study is Cyperus papyrus because it has the ability to absorb pollutants. Inoculum used was Pseudomonas aeruginosa and Enterobacter aerogenes which is a bacteria degrading organic compounds commonly found in water. To measure the effectiveness of the system, use several indicators to see the degradation of pollutants, namely changes in viscosity, surface tension of pollutants, and the emergence of compound degradation. Based on the results of the study can be determined that the substrated and vegetated system with Cyperus papyrus inoculum (E) was considered the most capable of degrading diesel oil due to the large changes in all parameters. In the system E, 40.6% increase viscosity, surface tension decreased 32.7%, the appearance of degradation compounds with relatively 3614.7 points, and increased to 227.8% TDS. In addition the environmental conditions in the system E also supports the growth of vegetation and degrading microbes.

  11. Kansas Playa Wetlands

    Data.gov (United States)

    Kansas Data Access and Support Center — This digital dataset provides information about the distribution, areal extent, and morphometry of playa wetlands throughout western Kansas. Playa wetlands were...

  12. Projecting the Hydrologic Impacts of Climate Change on Montane Wetlands.

    Science.gov (United States)

    Lee, Se-Yeun; Ryan, Maureen E; Hamlet, Alan F; Palen, Wendy J; Lawler, Joshua J; Halabisky, Meghan

    2015-01-01

    Wetlands are globally important ecosystems that provide critical services for natural communities and human society. Montane wetland ecosystems are expected to be among the most sensitive to changing climate, as their persistence depends on factors directly influenced by climate (e.g. precipitation, snowpack, evaporation). Despite their importance and climate sensitivity, wetlands tend to be understudied due to a lack of tools and data relative to what is available for other ecosystem types. Here, we develop and demonstrate a new method for projecting climate-induced hydrologic changes in montane wetlands. Using observed wetland water levels and soil moisture simulated by the physically based Variable Infiltration Capacity (VIC) hydrologic model, we developed site-specific regression models relating soil moisture to observed wetland water levels to simulate the hydrologic behavior of four types of montane wetlands (ephemeral, intermediate, perennial, permanent wetlands) in the U. S. Pacific Northwest. The hybrid models captured observed wetland dynamics in many cases, though were less robust in others. We then used these models to a) hindcast historical wetland behavior in response to observed climate variability (1916-2010 or later) and classify wetland types, and b) project the impacts of climate change on montane wetlands using global climate model scenarios for the 2040s and 2080s (A1B emissions scenario). These future projections show that climate-induced changes to key driving variables (reduced snowpack, higher evapotranspiration, extended summer drought) will result in earlier and faster drawdown in Pacific Northwest montane wetlands, leading to systematic reductions in water levels, shortened wetland hydroperiods, and increased probability of drying. Intermediate hydroperiod wetlands are projected to experience the greatest changes. For the 2080s scenario, widespread conversion of intermediate wetlands to fast-drying ephemeral wetlands will likely reduce

  13. Projecting the Hydrologic Impacts of Climate Change on Montane Wetlands

    Science.gov (United States)

    Hamlet, Alan F.; Palen, Wendy J.; Lawler, Joshua J.; Halabisky, Meghan

    2015-01-01

    Wetlands are globally important ecosystems that provide critical services for natural communities and human society. Montane wetland ecosystems are expected to be among the most sensitive to changing climate, as their persistence depends on factors directly influenced by climate (e.g. precipitation, snowpack, evaporation). Despite their importance and climate sensitivity, wetlands tend to be understudied due to a lack of tools and data relative to what is available for other ecosystem types. Here, we develop and demonstrate a new method for projecting climate-induced hydrologic changes in montane wetlands. Using observed wetland water levels and soil moisture simulated by the physically based Variable Infiltration Capacity (VIC) hydrologic model, we developed site-specific regression models relating soil moisture to observed wetland water levels to simulate the hydrologic behavior of four types of montane wetlands (ephemeral, intermediate, perennial, permanent wetlands) in the U. S. Pacific Northwest. The hybrid models captured observed wetland dynamics in many cases, though were less robust in others. We then used these models to a) hindcast historical wetland behavior in response to observed climate variability (1916–2010 or later) and classify wetland types, and b) project the impacts of climate change on montane wetlands using global climate model scenarios for the 2040s and 2080s (A1B emissions scenario). These future projections show that climate-induced changes to key driving variables (reduced snowpack, higher evapotranspiration, extended summer drought) will result in earlier and faster drawdown in Pacific Northwest montane wetlands, leading to systematic reductions in water levels, shortened wetland hydroperiods, and increased probability of drying. Intermediate hydroperiod wetlands are projected to experience the greatest changes. For the 2080s scenario, widespread conversion of intermediate wetlands to fast-drying ephemeral wetlands will likely reduce

  14. COMBINING PCA ANALYSIS AND ARTIFICIAL NEURAL NETWORKS IN MODELLING ENTREPRENEURIAL INTENTIONS OF STUDENTS

    Directory of Open Access Journals (Sweden)

    Marijana Zekić-Sušac

    2013-02-01

    Full Text Available Despite increased interest in the entrepreneurial intentions and career choices of young adults, reliable prediction models are yet to be developed. Two nonparametric methods were used in this paper to model entrepreneurial intentions: principal component analysis (PCA and artificial neural networks (ANNs. PCA was used to perform feature extraction in the first stage of modelling, while artificial neural networks were used to classify students according to their entrepreneurial intentions in the second stage. Four modelling strategies were tested in order to find the most efficient model. Dataset was collected in an international survey on entrepreneurship self-efficacy and identity. Variables describe students’ demographics, education, attitudes, social and cultural norms, self-efficacy and other characteristics. The research reveals benefits from the combination of the PCA and ANNs in modeling entrepreneurial intentions, and provides some ideas for further research.

  15. A classical Master equation approach to modeling an artificial protein motor

    International Nuclear Information System (INIS)

    Kuwada, Nathan J.; Blab, Gerhard A.; Linke, Heiner

    2010-01-01

    Inspired by biomolecular motors, as well as by theoretical concepts for chemically driven nanomotors, there is significant interest in constructing artificial molecular motors. One driving force is the opportunity to create well-controlled model systems that are simple enough to be modeled in detail. A remaining challenge is the fact that such models need to take into account processes on many different time scales. Here we describe use of a classical Master equation approach, integrated with input from Langevin and molecular dynamics modeling, to stochastically model an existing artificial molecular motor concept, the Tumbleweed, across many time scales. This enables us to study how interdependencies between motor processes, such as center-of-mass diffusion and track binding/unbinding, affect motor performance. Results from our model help guide the experimental realization of the proposed motor, and potentially lead to insights that apply to a wider class of molecular motors.

  16. Artificial intelligence techniques for modeling database user behavior

    Science.gov (United States)

    Tanner, Steve; Graves, Sara J.

    1990-01-01

    The design and development of the adaptive modeling system is described. This system models how a user accesses a relational database management system in order to improve its performance by discovering use access patterns. In the current system, these patterns are used to improve the user interface and may be used to speed data retrieval, support query optimization and support a more flexible data representation. The system models both syntactic and semantic information about the user's access and employs both procedural and rule-based logic to manipulate the model.

  17. Study of Circulation in the Tillamook Bay and the Surrounding Wetland Applying Triple-Nested Models Downscaling from Global Ocean to Estuary

    Science.gov (United States)

    To study the circulation and water quality in the Tillamook Bay, Oregon, a high-resolution estuarine model that covers the shallow bay and the surrounding wetland has been developed. The estuarine circulation at Tillamook Bay is mainly driven by the tides and the river flows and ...

  18. Artificial intelligence and finite element modelling for monitoring flood defence structures

    NARCIS (Netherlands)

    Pyayt, A.L.; Mokhov, I.I.; Kozionov, A.; Kusherbaeva, V.; Melnikova, N.B.; Krzhizhanovskaya, V.V.; Meijer, R.J.

    2011-01-01

    We present a hybrid approach to monitoring the stability of flood defence structures equipped with sensors. This approach combines the finite element modelling with the artificial intelligence for real-time signal processing and anomaly detection. This combined method has been developed for the

  19. Assessment of a conceptual hydrological model and artificial neural networks for daily outflows forecasting

    NARCIS (Netherlands)

    Rezaeianzadeh, M.; Stein, A.; Tabari, H.; Abghari, H.; Jalalkamali, N.; Hosseinipour, E.Z.; Singh, V.P.

    2013-01-01

    Artificial neural networks (ANNs) are used by hydrologists and engineers to forecast flows at the outlet of a watershed. They are employed in particular where hydrological data are limited. Despite these developments, practitioners still prefer conventional hydrological models. This study applied

  20. A Neurocomputational Model of Goal-Directed Navigation in Insect-Inspired Artificial Agents

    DEFF Research Database (Denmark)

    Goldschmidt, Dennis; Manoonpong, Poramate; Dasgupta, Sakyasingha

    2017-01-01

    in artificial agents. The model consists of a path integration mechanism, reward-modulated global learning, random search, and action selection. The path integration mechanism integrates compass and odometric cues to compute a vectorial representation of the agent's current location as neural activity patterns...

  1. Leak Detection Modeling and Simulation for Oil Pipeline with Artificial Intelligence Method

    OpenAIRE

    Sukarno, Pudjo; Sidarto, Kuntjoro Adji; Trisnobudi, Amoranto; Setyoadi, Delint Ira; Rohani, Nancy; Darmadi, Darmadi

    2007-01-01

    Leak detection is always interesting research topic, where leak location and leak rate are two pipeline leaking parameters that should be determined accurately to overcome pipe leaking problems. In this research those two parameters are investigated by developing transmission pipeline model and the leak detection model which is developed using Artificial Neural Network. The mathematical approach needs actual leak data to train the leak detection model, however such data could not be obtained ...

  2. Modeling Environmental Impacts on Cognitive Performance for Artificially Intelligent Entities

    Science.gov (United States)

    2017-06-01

    Mavor, 1998). It should be noted that the authors mention the military user audience as a key constituent that must be able to trust the validity...modeling efforts in cognitive architectures. Ultimately, the Task Group 128 report finds that “modeling of post- receptive perceptual processes such

  3. Artificial Neural Network Modeling of an Inverse Fluidized Bed ...

    African Journals Online (AJOL)

    A Radial Basis Function neural network has been successfully employed for the modeling of the inverse fluidized bed reactor. In the proposed model, the trained neural network represents the kinetics of biological decomposition of pollutants in the reactor. The neural network has been trained with experimental data ...

  4. Research on Artificial Spider Web Model for Farmland Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Jun Wang

    2018-01-01

    Full Text Available Through systematic analysis of the structural characteristics and invulnerability of spider web, this paper explores the possibility of combining the advantages of spider web such as network robustness and invulnerability with farmland wireless sensor network. A universally applicable definition and mathematical model of artificial spider web structure are established. The comparison between artificial spider web and traditional networks is discussed in detail. The simulation result shows that the networking structure of artificial spider web is better than that of traditional networks in terms of improving the overall reliability and invulnerability of communication system. A comprehensive study on the advantage characteristics of spider web has important theoretical and practical significance for promoting the invulnerability research of farmland wireless sensor network.

  5. Artificial neural network modelling in heavy ion collisions

    International Nuclear Information System (INIS)

    El-dahshan, E.; Radi, A.; El-Bakry, M.Y.; El Mashad, M.

    2008-01-01

    The neural network (NN) model and parton two fireball model (PTFM) have been used to study the pseudo-rapidity distribution of the shower particles for C 12, O 16, Si 28 and S 32 on nuclear emulsion. The trained NN shows a better fitting with experimental data than the PTFM calculations. The NN is then used to predict the distributions that are not present in the training set and matched them effectively. The NN simulation results prove a strong presence modeling in heavy ion collisions

  6. Modeling the thermotaxis behavior of C.elegans based on the artificial neural network.

    Science.gov (United States)

    Li, Mingxu; Deng, Xin; Wang, Jin; Chen, Qiaosong; Tang, Yun

    2016-07-03

    ASBTRACT This research aims at modeling the thermotaxis behavior of C.elegans which is a kind of nematode with full clarified neuronal connections. Firstly, this work establishes the motion model which can perform the undulatory locomotion with turning behavior. Secondly, the thermotaxis behavior is modeled by nonlinear functions and the nonlinear functions are learned by artificial neural network. Once the artificial neural networks have been well trained, they can perform the desired thermotaxis behavior. Last, several testing simulations are carried out to verify the effectiveness of the model for thermotaxis behavior. This work also analyzes the different performances of the model under different environments. The testing results reveal the essence of the thermotaxis of C.elegans to some extent, and theoretically support the research on the navigation of the crawling robots.

  7. Coupled Model of Artificial Neural Network and Grey Model for Tendency Prediction of Labor Turnover

    Directory of Open Access Journals (Sweden)

    Yueru Ma

    2014-01-01

    Full Text Available The tendency of labor turnover in the Chinese enterprise shows the characteristics of seasonal fluctuations and irregular distribution of various factors, especially the Chinese traditional social and cultural characteristics. In this paper, we present a coupled model for the tendency prediction of labor turnover. In the model, a time series of tendency prediction of labor turnover was expressed as trend item and its random item. Trend item of tendency prediction of labor turnover is predicted using Grey theory. Random item of trend item is calculated by artificial neural network model (ANN. A case study is presented by the data of 24 months in a Chinese matured enterprise. The model uses the advantages of “accumulative generation” of a Grey prediction method, which weakens the original sequence of random disturbance factors and increases the regularity of data. It also takes full advantage of the ANN model approximation performance, which has a capacity to solve economic problems rapidly, describes the nonlinear relationship easily, and avoids the defects of Grey theory.

  8. Modelling of solar energy potential in Nigeria using an artificial neural network model

    International Nuclear Information System (INIS)

    Fadare, D.A.

    2009-01-01

    In this study, an artificial neural network (ANN) based model for prediction of solar energy potential in Nigeria (lat. 4-14 o N, log. 2-15 o E) was developed. Standard multilayered, feed-forward, back-propagation neural networks with different architecture were designed using neural toolbox for MATLAB. Geographical and meteorological data of 195 cities in Nigeria for period of 10 years (1983-1993) from the NASA geo-satellite database were used for the training and testing the network. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, mean temperature, and relative humidity) were used as inputs to the network, while the solar radiation intensity was used as the output of the network. The results show that the correlation coefficients between the ANN predictions and actual mean monthly global solar radiation intensities for training and testing datasets were higher than 90%, thus suggesting a high reliability of the model for evaluation of solar radiation in locations where solar radiation data are not available. The predicted solar radiation values from the model were given in form of monthly maps. The monthly mean solar radiation potential in northern and southern regions ranged from 7.01-5.62 to 5.43-3.54 kW h/m 2 day, respectively. A graphical user interface (GUI) was developed for the application of the model. The model can be used easily for estimation of solar radiation for preliminary design of solar applications.

  9. Wetland restoration, flood pulsing, and disturbance dynamics

    Science.gov (United States)

    Middleton, Beth A.

    1999-01-01

    While it is generally accepted that flood pulsing and disturbance dynamics are critical to wetland viability, there is as yet no consensus among those responsible for wetland restoration about how best to plan for those phenomena or even whether it is really necessary to do so at all. In this groundbreaking book, Dr. Beth Middleton draws upon the latest research from around the world to build a strong case for making flood pulsing and disturbance dynamics integral to the wetland restoration planning process.While the initial chapters of the book are devoted to laying the conceptual foundations, most of the coverage is concerned with demonstrating the practical implications for wetland restoration and management of the latest ecological theory and research. It includes a fascinating case history section in which Dr. Middleton explores the restoration models used in five major North American, European, Australian, African, and Asian wetland projects, and analyzes their relative success from the perspective of flood pulsing and disturbance dynamics planning.Wetland Restoration also features a wealth of practical information useful to all those involved in wetland restoration and management, including: * A compendium of water level tolerances, seed germination, seedling recruitment, adult survival rates, and other key traits of wetland plant species * A bibliography of 1,200 articles and monographs covering all aspects of wetland restoration * A comprehensive directory of wetland restoration ftp sites worldwide * An extensive glossary of essential terms

  10. Cognitive model of image interpretation for artificial intelligence applications

    International Nuclear Information System (INIS)

    Raju, S.

    1988-01-01

    A cognitive model of imaging diagnosis was devised to aid in the development of expert systems that assist in the interpretation of diagnostic images. In this cognitive model, a small set of observations that are strongly predictive of a particular diagnosis lead to a search for other observations that would support this diagnosis but are not necessarily specific for it. Then a set of alternative diagnoses is considered. This is followed by a search for observations that might allow differentiation of the primary diagnostic consideration from the alternatives. The production rules needed to implement this model can be classified into three major categories, each of which have certain general characteristics. Knowledge of these characteristics simplifies the development of these expert systems

  11. Developing energy forecasting model using hybrid artificial intelligence method

    Institute of Scientific and Technical Information of China (English)

    Shahram Mollaiy-Berneti

    2015-01-01

    An important problem in demand planning for energy consumption is developing an accurate energy forecasting model. In fact, it is not possible to allocate the energy resources in an optimal manner without having accurate demand value. A new energy forecasting model was proposed based on the back-propagation (BP) type neural network and imperialist competitive algorithm. The proposed method offers the advantage of local search ability of BP technique and global search ability of imperialist competitive algorithm. Two types of empirical data regarding the energy demand (gross domestic product (GDP), population, import, export and energy demand) in Turkey from 1979 to 2005 and electricity demand (population, GDP, total revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010 were investigated to demonstrate the applicability and merits of the present method. The performance of the proposed model is found to be better than that of conventional back-propagation neural network with low mean absolute error.

  12. Modelling of Surface Ships using Artificial Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Jensen, F. M.; Thoft-Christensen, Palle

    For various design and planning purposes there is at present an increasing interest and a need for numerical modelling of the process of navigating a vessel (or a floating body in general). The reasons for this is that experiments in "full mission" simulators with human navigators at the handles ...

  13. Using artificial neural network approach for modelling rainfall–runoff ...

    Indian Academy of Sciences (India)

    Department of Civil Engineering, National Pingtung University of Science and Technology, Neipu Hsiang,. Pingtung ... study, a model for estimating runoff by using rainfall data from a river basin is developed and a neural ... For example, 2009 typhoon Morakot in Pingtung ... Tokar and Markus (2000) applied ANN to predict.

  14. An artificial intelligence tool for complex age-depth models

    Science.gov (United States)

    Bradley, E.; Anderson, K. A.; de Vesine, L. R.; Lai, V.; Thomas, M.; Nelson, T. H.; Weiss, I.; White, J. W. C.

    2017-12-01

    CSciBox is an integrated software system for age modeling of paleoenvironmental records. It incorporates an array of data-processing and visualization facilities, ranging from 14C calibrations to sophisticated interpolation tools. Using CSciBox's GUI, a scientist can build custom analysis pipelines by composing these built-in components or adding new ones. Alternatively, she can employ CSciBox's automated reasoning engine, Hobbes, which uses AI techniques to perform an in-depth, autonomous exploration of the space of possible age-depth models and presents the results—both the models and the reasoning that was used in constructing and evaluating them—to the user for her inspection. Hobbes accomplishes this using a rulebase that captures the knowledge of expert geoscientists, which was collected over the course of more than 100 hours of interviews. It works by using these rules to generate arguments for and against different age-depth model choices for a given core. Given a marine-sediment record containing uncalibrated 14C dates, for instance, Hobbes tries CALIB-style calibrations using a choice of IntCal curves, with reservoir age correction values chosen from the 14CHRONO database using the lat/long information provided with the core, and finally composes the resulting age points into a full age model using different interpolation methods. It evaluates each model—e.g., looking for outliers or reversals—and uses that information to guide the next steps of its exploration, and presents the results to the user in human-readable form. The most powerful of CSciBox's built-in interpolation methods is BACON, a Bayesian sedimentation-rate algorithm—a powerful but complex tool that can be difficult to use. Hobbes adjusts BACON's many parameters autonomously to match the age model to the expectations of expert geoscientists, as captured in its rulebase. It then checks the model against the data and iteratively re-calculates until it is a good fit to the data.

  15. Prediction of paddy drying kinetics: A comparative study between mathematical and artificial neural network modelling

    Directory of Open Access Journals (Sweden)

    Beigi Mohsen

    2017-01-01

    Full Text Available The present study aimed at investigation of deep bed drying of rough rice kernels at various thin layers at different drying air temperatures and flow rates. A comparative study was performed between mathematical thin layer models and artificial neural networks to estimate the drying curves of rough rice. The suitability of nine mathematical models in simulating the drying kinetics was examined and the Midilli model was determined as the best approach for describing drying curves. Different feed forward-back propagation artificial neural networks were examined to predict the moisture content variations of the grains. The ANN with 4-18-18-1 topology, transfer function of hyperbolic tangent sigmoid and a Levenberg-Marquardt back propagation training algorithm provided the best results with the maximum correlation coefficient and the minimum mean square error values. Furthermore, it was revealed that ANN modeling had better performance in prediction of drying curves with lower root mean square error values.

  16. Response of methane emissions from wetlands to the Last Glacial Maximum and an idealized Dansgaard–Oeschger climate event: insights from two models of different complexity

    Directory of Open Access Journals (Sweden)

    B. Ringeval

    2013-01-01

    Full Text Available The role of different sources and sinks of CH4 in changes in atmospheric methane ([CH4] concentration during the last 100 000 yr is still not fully understood. In particular, the magnitude of the change in wetland CH4 emissions at the Last Glacial Maximum (LGM relative to the pre-industrial period (PI, as well as during abrupt climatic warming or Dansgaard–Oeschger (D–O events of the last glacial period, is largely unconstrained. In the present study, we aim to understand the uncertainties related to the parameterization of the wetland CH4 emission models relevant to these time periods by using two wetland models of different complexity (SDGVM and ORCHIDEE. These models have been forced by identical climate fields from low-resolution coupled atmosphere–ocean general circulation model (FAMOUS simulations of these time periods. Both emission models simulate a large decrease in emissions during LGM in comparison to PI consistent with ice core observations and previous modelling studies. The global reduction is much larger in ORCHIDEE than in SDGVM (respectively −67 and −46%, and whilst the differences can be partially explained by different model sensitivities to temperature, the major reason for spatial differences between the models is the inclusion of freezing of soil water in ORCHIDEE and the resultant impact on methanogenesis substrate availability in boreal regions. Besides, a sensitivity test performed with ORCHIDEE in which the methanogenesis substrate sensitivity to the precipitations is modified to be more realistic gives a LGM reduction of −36%. The range of the global LGM decrease is still prone to uncertainty, and here we underline its sensitivity to different process parameterizations. Over the course of an idealized D–O warming, the magnitude of the change in wetland CH4 emissions simulated by the two models at global scale is very similar at around 15 Tg yr−1, but this is only around 25% of the ice-core measured

  17. Wetland eco-engineering: Measuring and modeling feedbacks of oxidation processes between plants and clay-rich material

    NARCIS (Netherlands)

    Saaltink, R.; Dekker, S.C.; Griffioen, J.; Wassen, M.J.

    2016-01-01

    Interest is growing in using soft sediment as a foundation in eco-engineering projects. Wetland construction in the Dutch lake Markermeer is an example: here, dredging some of the clay-rich lake-bed sediment and using it to construct wetland will soon begin. Natural processes will be utilized during

  18. Environmental flows and its evaluation of restoration effect based on LEDESS model in Yellow River Delta wetlands

    NARCIS (Netherlands)

    Wang, X.G.; Lian, Y.; Huang, C.; Wang, X.J.; Wang, R.L.; Shan, K.; Pedroli, B.; Eupen, van M.; Elmahdi, A.; Ali, M.

    2012-01-01

    Due to freshwater supplement scarcity and heavy human activities, the fresh water wetland ecosystem in Yellow River Delta is facing disintegrated deterioration, and it is seriously affecting the health of the Yellow River ecosystem. This paper identifies the restoration objectives of wetland aiming

  19. Hydroregime prediction models for ephemeral groundwater-driven sinkhole wetlands: a planning tool for climate change and amphibian conservation

    Science.gov (United States)

    C. H. Greenberg; S. Goodrick; J. D. Austin; B. R. Parresol

    2015-01-01

    Hydroregimes of ephemeral wetlands affect reproductive success of many amphibian species and are sensitive to altered weather patterns associated with climate change.We used 17 years of weekly temperature, precipitation, and waterdepth measurements for eight small, ephemeral, groundwaterdriven sinkhole wetlands in Florida sandhills to develop a hydroregime predictive...

  20. A conceptual hydrologic model for a forested Carolina bay depressional wetland on the Coastal Plain of South Carolina, USA

    Science.gov (United States)

    Jennifer E. Pyzoha; Timothy J. Callahan; Ge Sun; Carl C. Trettin; Masato Miwa

    2008-01-01

    This paper describes how climate influences the hydrology of an ephemeral depressional wetland. Surface water and groundwater elevation data were collected for 7 years in a Coastal Plain watershed in South Carolina USA containing depressional wetlands, known as Carolina bays. Rainfall and temperature data were compared with water-table well and piezometer data in and...

  1. Hydrological modeling of the pipestone creek watershed using the Soil Water Assessment Tool (SWAT: Assessing impacts of wetland drainage on hydrology

    Directory of Open Access Journals (Sweden)

    Cesar Perez-Valdivia

    2017-12-01

    Full Text Available Study region: Prairie Pothole Region of North America. Study focus: The Prairie Pothole Region of North America has experienced extensive wetland drainage, potentially impacting peak flows and annual flow volumes. Some of this drainage has occurred in closed basins, possibly impacting lake water levels of these systems. In this study we investigated the potential impact of wetland drainage on peak flows and annual volumes in a 2242 km2 watershed located in southeastern Saskatchewan (Canada using the Soil Water Assessment Tool (SWAT model. New hydrological insights: The SWAT model, which had been calibrated and validated at daily and monthly time steps for the 1997–2009 period, was used to assess the impact of wetland drainage using three hypothetical scenarios that drained 15, 30, and 50% of the non-contributing drainage area. Results of these simulations suggested that drainage increased spring peak flows by about 50, 79 and 113%, respectively while annual flow volumes increased by about 43, 68, and 98% in each scenario. Years that were wetter than normal presented increased peak flows and annual flow volumes below the average of the simulated period. Alternatively, summer peak flows presented smaller increases in terms of percentages during the simulated period. Keywords: Soil Water Assessment Tool (SWAT, Wetland drainage, Peak flow, Annual volume, Prairie Pothole Region

  2. Modelling of pneumatic muscle actuator using Hill's model with different approximations of static characteristics of artificial muscle

    Directory of Open Access Journals (Sweden)

    Piteľ Ján

    2016-01-01

    Full Text Available For modelling and simulation of pneumatic muscle actuators the mathematical dependence of the muscle force on the muscle contraction at different pressures in the muscles is necessary to know. For this purpose the static characteristics of the pneumatic artificial muscle type FESTO MAS-20-250N used in the experiments were approximated. In the paper there are shown some simulation results of the pneumatic muscle actuator dynamics using modified Hill's muscle model, in which four different approximations of static characteristics of artificial muscle were used.

  3. Model for Detection and Classification of DDoS Traffic Based on Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    D. Peraković

    2017-06-01

    Full Text Available Detection of DDoS (Distributed Denial of Service traffic is of great importance for the availability protection of services and other information and communication resources. The research presented in this paper shows the application of artificial neural networks in the development of detection and classification model for three types of DDoS attacks and legitimate network traffic. Simulation results of developed model showed accuracy of 95.6% in classification of pre-defined classes of traffic.

  4. An optimization on strontium separation model for fission products (inactive trace elements) using artificial neural networks

    International Nuclear Information System (INIS)

    Moosavi, K.; Setayeshi, S.; Maragheh, M.Gh.; Ahmadi, S.J.; Kardan, M.R.; Banaem, L.M.

    2009-01-01

    In this study, an experimental design using artificial neural networks for an optimization on the strontium separation model for fission products (inactive trace elements) is investigated. The goal is to optimize the separation parameters to achieve maximum amount of strontium that is separated from the fission products. The result of the optimization method causes a proper purity of Strontium-89 that was separated from the fission products. It is also shown that ANN may be establish a method to optimize the separation model.

  5. Artificial neural network models' application for radioactive substances' migration forecasting in soil

    International Nuclear Information System (INIS)

    Kovalenko, V.I.; Khil'ko, O.S.; Kundas, S.P.

    2009-01-01

    The work is indicated to the use of artificial neural network (ANN) models in program complex SPS for radioactive substances' migration forecasting in soil. For the problem solution two ANN models are used. One of them forecasts radioactive substances' migration, another carries out forecasting of physical and chemical soil properties. Program complex SPS allows to achieve a low error of forecasting (no more than 5 %) and high training speed. (authors)

  6. A Quantum Implementation Model for Artificial Neural Networks

    OpenAIRE

    Daskin, Ammar

    2016-01-01

    The learning process for multi layered neural networks with many nodes makes heavy demands on computational resources. In some neural network models, the learning formulas, such as the Widrow-Hoff formula, do not change the eigenvectors of the weight matrix while flatting the eigenvalues. In infinity, this iterative formulas result in terms formed by the principal components of the weight matrix: i.e., the eigenvectors corresponding to the non-zero eigenvalues. In quantum computing, the phase...

  7. A Quantum Implementation Model for Artificial Neural Networks

    OpenAIRE

    Ammar Daskin

    2018-01-01

    The learning process for multilayered neural networks with many nodes makes heavy demands on computational resources. In some neural network models, the learning formulas, such as the Widrow–Hoff formula, do not change the eigenvectors of the weight matrix while flatting the eigenvalues. In infinity, these iterative formulas result in terms formed by the principal components of the weight matrix, namely, the eigenvectors corresponding to the non-zero eigenvalues. In quantum computing, the pha...

  8. Artificial muscle for reanimation of the paralyzed face: durability and biocompatibility in a gerbil model.

    Science.gov (United States)

    Ledgerwood, Levi G; Tinling, Steven; Senders, Craig; Wong-Foy, Annjoe; Prahlad, Harsha; Tollefson, Travis T

    2012-11-01

    Current management of permanent facial paralysis centers on nerve grafting and muscle transfer; however, limitations of those procedures call for other options. To determine the durability and biocompatibility of implanted artificial muscle in a gerbil model and the degree of inflammation and fibrosis at the host tissue-artificial muscle interface. Electroactive polymer artificial muscle (EPAM) devices engineered in medical-grade silicone were implanted subcutaneously in 13 gerbils. The implanted units were stimulated with 1 kV at 1 Hz, 24 h/d via a function generator. Electrical signal input/output was recorded up to 40 days after implantation. The animals were euthanized between 23 and 65 days after implantation, and the host tissue-implant interface was evaluated histologically. The animals tolerated implantation of the EPAM devices well, with no perioperative deaths. The muscle devices created motion for a mean of 30.3 days (range, 19-40 days), with a mean of 2.6 × 106 cycles (range, 1.6 × 106 to 3.5 × 106 cycles). Histologic examination of the explanted devices revealed the development of a minimal fibrous capsule surrounding the implants, with no evidence of bacterial infection or inflammatory infiltrate. No evidence of device compromise, corrosion, or silicone breakdown was noted. Artificial muscle implanted in this short-term animal model was safe and functional in this preliminary study. We believe that EPAM devices will be a safe and viable option for restoration of facial motions in patients with irreversible facial paralysis.

  9. Recent development in artificial photosynthetic model; Jinko kogosei no moderu ka kenkyu saikin no shinpo

    Energy Technology Data Exchange (ETDEWEB)

    Kaneko, M [Ibaraki Univ., Ibaraki (Japan). Faculty of Engineering

    1996-03-01

    In the conversion from solar energy into chemical energy (fuels) by photochemical conversion, an electron donor is necessary since all the fuels are reductive compounds. From the viewpoint of economic profit, water is the only one candidate as a cheap compound and existing impartially. In this paper, photosynthesis as well as the realization of its artificial model, and the relevant basic research executed recently aiming at the construction of an artificial photosynthetic system are explained. The main reaction of photosynthesis is the generation of carbohydrates by the reduction reaction of carbon dioxide with water as an electron donor and solar visual light as an energy resource. As a special example thereof, the UV photolysis of water due to the photocatalysis of a micro-particle system is introduced. The method of using a semiconductor and the method of using sensitizes are described as the photo excitation system when designing the artificial model. Additionally, as the research with respect to the construction of an artificial photosynthetic system, a photo-exciting charge transfer system is introduced. 27 refs., 1 fig.

  10. Log-linear model based behavior selection method for artificial fish swarm algorithm.

    Science.gov (United States)

    Huang, Zhehuang; Chen, Yidong

    2015-01-01

    Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.

  11. Log-Linear Model Based Behavior Selection Method for Artificial Fish Swarm Algorithm

    Directory of Open Access Journals (Sweden)

    Zhehuang Huang

    2015-01-01

    Full Text Available Artificial fish swarm algorithm (AFSA is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.

  12. Proximal caries detection using digital subtraction radiography in the artificial caries activity model

    Energy Technology Data Exchange (ETDEWEB)

    Park, Jong Hoon; Lee, Gi Ja; Choi, Sam Jin; Park, Young Ho; Kim, Kyung Soo; Jin, Hyun Seok; Hong, Kyung Won; Oh, Berm Seok; Park, Hun Kuk [Department of Biomedical Engineering, School of Medicine, Kyung Hee University, Seoul (Korea, Republic of); Choi, Yong Suk; Hwang, Eui Hwan [Department of Oral and Maxillofacial Radiology, Institute of Oral Biology, School of Dentistry, Kyung Hee University, Seoul (Korea, Republic of)

    2009-03-15

    The purpose of the experiment was to evaluating the diagnostic ability of dental caries detection using digital subtraction in the artificial caries activity model. Digital radiographs of five teeth with 8 proximal surfaces were obtained by CCD sensor (Kodak RVG 6100 using a size no.2). The digital radiographic images and subtraction images from artificial proximal caries were examined and interpreted. In this study, we proposed novel caries detection method which could diagnose the dental proximal caries from single digital radiographic image. In artificial caries activity model, the range of lesional depth was 572-1,374 {mu}m and the range of lesional area was 36.95-138.52 mm{sup 2}. The lesional depth and the area were significantly increased with demineralization time (p<0.001). Furthermore, the proximal caries detection using digital subtraction radiography showed high detection rate compared to the proximal caries examination using simple digital radiograph. The results demonstrated that the digital subtraction radiography from single radiographic image of artificial caries was highly efficient in the detection of dental caries compared to the data from simple digital radiograph.

  13. Proximal caries detection using digital subtraction radiography in the artificial caries activity model

    International Nuclear Information System (INIS)

    Park, Jong Hoon; Lee, Gi Ja; Choi, Sam Jin; Park, Young Ho; Kim, Kyung Soo; Jin, Hyun Seok; Hong, Kyung Won; Oh, Berm Seok; Park, Hun Kuk; Choi, Yong Suk; Hwang, Eui Hwan

    2009-01-01

    The purpose of the experiment was to evaluating the diagnostic ability of dental caries detection using digital subtraction in the artificial caries activity model. Digital radiographs of five teeth with 8 proximal surfaces were obtained by CCD sensor (Kodak RVG 6100 using a size no.2). The digital radiographic images and subtraction images from artificial proximal caries were examined and interpreted. In this study, we proposed novel caries detection method which could diagnose the dental proximal caries from single digital radiographic image. In artificial caries activity model, the range of lesional depth was 572-1,374 μm and the range of lesional area was 36.95-138.52 mm 2 . The lesional depth and the area were significantly increased with demineralization time (p<0.001). Furthermore, the proximal caries detection using digital subtraction radiography showed high detection rate compared to the proximal caries examination using simple digital radiograph. The results demonstrated that the digital subtraction radiography from single radiographic image of artificial caries was highly efficient in the detection of dental caries compared to the data from simple digital radiograph.

  14. Brand Choice Modeling Modeling Toothpaste Brand Choice: An Empirical Comparison of Artificial Neural Networks and Multinomial Probit Model

    Directory of Open Access Journals (Sweden)

    Tolga Kaya

    2010-11-01

    Full Text Available The purpose of this study is to compare the performances of Artificial Neural Networks (ANN and Multinomial Probit (MNP approaches in modeling the choice decision within fast moving consumer goods sector. To do this, based on 2597 toothpaste purchases of a panel sample of 404 households, choice models are built and their performances are compared on the 861 purchases of a test sample of 135 households. Results show that ANN's predictions are better while MNP is useful in providing marketing insight.

  15. Shallow Landslide Susceptibility Modeling Using the Data Mining Models Artificial Neural Network and Boosted Tree

    Directory of Open Access Journals (Sweden)

    Hyun-Joo Oh

    2017-09-01

    Full Text Available The main purpose of this paper is to present some potential applications of sophisticated data mining techniques, such as artificial neural network (ANN and boosted tree (BT, for landslide susceptibility modeling in the Yongin area, Korea. Initially, landslide inventory was detected from visual interpretation using digital aerial photographic maps with a high resolution of 50 cm taken before and after the occurrence of landslides. The debris flows were randomly divided into two groups: training and validation sets with a 50:50 proportion. Additionally, 18 environmental factors related to landslide occurrence were derived from the topography, soil, and forest maps. Subsequently, the data mining techniques were applied to identify the influence of environmental factors on landslide occurrence of the training set and assess landslide susceptibility. Finally, the landslide susceptibility indexes from ANN and BT were compared with a validation set using a receiver operating characteristics curve. The slope gradient, topographic wetness index, and timber age appear to be important factors in landslide occurrence from both models. The validation result of ANN and BT showed 82.25% and 90.79%, which had reasonably good performance. The study shows the benefit of selecting optimal data mining techniques in landslide susceptibility modeling. This approach could be used as a guideline for choosing environmental factors on landslide occurrence and add influencing factors into landslide monitoring systems. Furthermore, this method can rank landslide susceptibility in urban areas, thus providing helpful information when selecting a landslide monitoring site and planning land-use.

  16. Nitrogen Removal in a Horizontal Subsurface Flow Constructed Wetland Estimated Using the First-Order Kinetic Model

    Directory of Open Access Journals (Sweden)

    Lijuan Cui

    2016-11-01

    Full Text Available We monitored the water quality and hydrological conditions of a horizontal subsurface constructed wetland (HSSF-CW in Beijing, China, for two years. We simulated the area-based constant and the temperature coefficient with the first-order kinetic model. We examined the relationships between the nitrogen (N removal rate, N load, seasonal variations in the N removal rate, and environmental factors—such as the area-based constant, temperature, and dissolved oxygen (DO. The effluent ammonia (NH4+-N and nitrate (NO3−-N concentrations were significantly lower than the influent concentrations (p < 0.01, n = 38. The NO3−-N load was significantly correlated with the removal rate (R2 = 0.96, p < 0.01, but the NH4+-N load was not correlated with the removal rate (R2 = 0.02, p > 0.01. The area-based constants of NO3−-N and NH4+-N at 20 °C were 27 ± 26 (mean ± SD and 14 ± 10 m∙year−1, respectively. The temperature coefficients for NO3−-N and NH4+-N were estimated at 1.004 and 0.960, respectively. The area-based constants for NO3−-N and NH4+-N were not correlated with temperature (p > 0.01. The NO3−-N area-based constant was correlated with the corresponding load (R2 = 0.96, p < 0.01. The NH4+-N area rate was correlated with DO (R2 = 0.69, p < 0.01, suggesting that the factors that influenced the N removal rate in this wetland met Liebig’s law of the minimum.

  17. A new method to estimate parameters of linear compartmental models using artificial neural networks

    International Nuclear Information System (INIS)

    Gambhir, Sanjiv S.; Keppenne, Christian L.; Phelps, Michael E.; Banerjee, Pranab K.

    1998-01-01

    At present, the preferred tool for parameter estimation in compartmental analysis is an iterative procedure; weighted nonlinear regression. For a large number of applications, observed data can be fitted to sums of exponentials whose parameters are directly related to the rate constants/coefficients of the compartmental models. Since weighted nonlinear regression often has to be repeated for many different data sets, the process of fitting data from compartmental systems can be very time consuming. Furthermore the minimization routine often converges to a local (as opposed to global) minimum. In this paper, we examine the possibility of using artificial neural networks instead of weighted nonlinear regression in order to estimate model parameters. We train simple feed-forward neural networks to produce as outputs the parameter values of a given model when kinetic data are fed to the networks' input layer. The artificial neural networks produce unbiased estimates and are orders of magnitude faster than regression algorithms. At noise levels typical of many real applications, the neural networks are found to produce lower variance estimates than weighted nonlinear regression in the estimation of parameters from mono- and biexponential models. These results are primarily due to the inability of weighted nonlinear regression to converge. These results establish that artificial neural networks are powerful tools for estimating parameters for simple compartmental models. (author)

  18. Model of Cholera Forecasting Using Artificial Neural Network in Chabahar City, Iran

    Directory of Open Access Journals (Sweden)

    Zahra Pezeshki

    2016-02-01

    Full Text Available Background: Cholera as an endemic disease remains a health issue in Iran despite decrease in incidence. Since forecasting epidemic diseases provides appropriate preventive actions in disease spread, different forecasting methods including artificial neural networks have been developed to study parameters involved in incidence and spread of epidemic diseases such as cholera. Objectives: In this study, cholera in rural area of Chabahar, Iran was investigated to achieve a proper forecasting model. Materials and Methods: Data of cholera was gathered from 465 villages, of which 104 reported cholera during ten years period of study. Logistic regression modeling and correlate bivariate were used to determine risk factors and achieve possible predictive model one-hidden-layer perception neural network with backpropagation training algorithm and the sigmoid activation function was trained and tested between the two groups of infected and non-infected villages after preprocessing. For determining validity of prediction, the ROC diagram was used. The study variables included climate conditions and geographical parameters. Results: After determining significant variables of cholera incidence, the described artificial neural network model was capable of forecasting cholera event among villages of test group with accuracy up to 80%. The highest accuracy was achieved when model was trained with variables that were significant in statistical analysis describing that the two methods confirm the result of each other. Conclusions: Application of artificial neural networking assists forecasting cholera for adopting protective measures. For a more accurate prediction, comprehensive information is required including data on hygienic, social and demographic parameters.

  19. An artificial neural network ensemble model for estimating global solar radiation from Meteosat satellite images

    International Nuclear Information System (INIS)

    Linares-Rodriguez, Alvaro; Ruiz-Arias, José Antonio; Pozo-Vazquez, David; Tovar-Pescador, Joaquin

    2013-01-01

    An optimized artificial neural network ensemble model is built to estimate daily global solar radiation over large areas. The model uses clear-sky estimates and satellite images as input variables. Unlike most studies using satellite imagery based on visible channels, our model also exploits all information within infrared channels of the Meteosat 9 satellite. A genetic algorithm is used to optimize selection of model inputs, for which twelve are selected – eleven 3-km Meteosat 9 channels and one clear-sky term. The model is validated in Andalusia (Spain) from January 2008 through December 2008. Measured data from 83 stations across the region are used, 65 for training and 18 independent ones for testing the model. At the latter stations, the ensemble model yields an overall root mean square error of 6.74% and correlation coefficient of 99%; the generated estimates are relatively accurate and errors spatially uniform. The model yields reliable results even on cloudy days, improving on current models based on satellite imagery. - Highlights: • Daily solar radiation data are generated using an artificial neural network ensemble. • Eleven Meteosat channels observations and a clear sky term are used as model inputs. • Model exploits all information within infrared Meteosat channels. • Measured data for a year from 83 ground stations are used. • The proposed approach has better performance than existing models on daily basis

  20. Replacing natural wetlands with stormwater management facilities: Biophysical and perceived social values.

    Science.gov (United States)

    Rooney, R C; Foote, L; Krogman, N; Pattison, J K; Wilson, M J; Bayley, S E

    2015-04-15

    Urban expansion replaces wetlands of natural origin with artificial stormwater management facilities. The literature suggests that efforts to mimic natural wetlands in the design of stormwater facilities can expand the provision of ecosystem services. Policy developments seek to capitalize on these improvements, encouraging developers to build stormwater wetlands in place of stormwater ponds; however, few have compared the biophysical values and social perceptions of these created wetlands to those of the natural wetlands they are replacing. We compared four types of wetlands: natural references sites, natural wetlands impacted by agriculture, created stormwater wetlands, and created stormwater ponds. We anticipated that they would exhibit a gradient in biodiversity, ecological integrity, chemical and hydrologic stress. We further anticipated that perceived values would mirror measured biophysical values. We found higher biophysical values associated with wetlands of natural origin (both reference and agriculturally impacted). The biophysical values of stormwater wetlands and stormwater ponds were lower and indistinguishable from one another. The perceived wetland values assessed by the public differed from the observed biophysical values. This has important policy implications, as the public are not likely to perceive the loss of values associated with the replacement of natural wetlands with created stormwater management facilities. We conclude that 1) agriculturally impacted wetlands provide biophysical values equivalent to those of natural wetlands, meaning that land use alone is not a great predictor of wetland value; 2) stormwater wetlands are not a substantive improvement over stormwater ponds, relative to wetlands of natural origin; 3) stormwater wetlands are poor mimics of natural wetlands, likely due to fundamental distinctions in terms of basin morphology, temporal variation in hydrology, ground water connectivity, and landscape position; 4) these

  1. Modeling of mass transfer of Phospholipids in separation process with supercritical CO2 fluid by RBF artificial neural networks

    Science.gov (United States)

    An artificial Radial Basis Function (RBF) neural network model was developed for the prediction of mass transfer of the phospholipids from canola meal in supercritical CO2 fluid. The RBF kind of artificial neural networks (ANN) with orthogonal least squares (OLS) learning algorithm were used for mod...

  2. Proof of concept of an artificial muscle: theoretical model, numerical model, and hardware experiment.

    Science.gov (United States)

    Haeufle, D F B; Günther, M; Blickhan, R; Schmitt, S

    2011-01-01

    Recently, the hyperbolic Hill-type force-velocity relation was derived from basic physical components. It was shown that a contractile element CE consisting of a mechanical energy source (active element AE), a parallel damper element (PDE), and a serial element (SE) exhibits operating points with hyperbolic force-velocity dependency. In this paper, the contraction dynamics of this CE concept were analyzed in a numerical simulation of quick release experiments against different loads. A hyperbolic force-velocity relation was found. The results correspond to measurements of the contraction dynamics of a technical prototype. Deviations from the theoretical prediction could partly be explained by the low stiffness of the SE, which was modeled analog to the metal spring in the hardware prototype. The numerical model and hardware prototype together, are a proof of this CE concept and can be seen as a well-founded starting point for the development of Hill-type artificial muscles. This opens up new vistas for the technical realization of natural movements with rehabilitation devices. © 2011 IEEE

  3. Artificial intelligence and exponential technologies business models evolution and new investment opportunities

    CERN Document Server

    Corea, Francesco

    2017-01-01

    Artificial Intelligence is a huge breakthrough technology that is changing our world. It requires some degrees of technical skills to be developed and understood, so in this book we are going to first of all define AI and categorize it with a non-technical language. We will explain how we reached this phase and what historically happened to artificial intelligence in the last century. Recent advancements in machine learning, neuroscience, and artificial intelligence technology will be addressed, and new business models introduced for and by artificial intelligence research will be analyzed. Finally, we will describe the investment landscape, through the quite comprehensive study of almost 14,000 AI companies and we will discuss important features and characteristics of both AI investors as well as investments. This is the “Internet of Thinks” era. AI is revolutionizing the world we live in. It is augmenting the human experiences, and it targets to amplify human intelligence in a future not so distant from...

  4. Modeling of biodistribution of 90 Y-DOTA-hR3 by using artificial intelligence techniques

    International Nuclear Information System (INIS)

    Ondarse, Dianelys; Quiza, Ramon; Leyva, Rene; Zamora, Minely; Ducat, Luis; Hernandez, Ignacio; Alonso, Luis Michel

    2011-01-01

    In this work the biodistribution of radioimmunoconjugate 9 0Y-DOTA-hR3 was modeled by using an artificial neural network. In vivo stability of 9 0Y-DOTA-hR3 was determined in healthy male Wistar rats at 4, 24 and 48 hours, in different organs. A model describing the relationship between, by one hand, the incorporated dose and, by the other hand, organ and time was developed by using a multilayer perceptron neural network. Adjusted model was analyzed by several statistical tests. Outcomes shown that proposed neural model describes the relationship between the studied variables in a proper way. (Author)

  5. Validation of artificial neural network models for predicting biochemical markers associated with male infertility.

    Science.gov (United States)

    Vickram, A S; Kamini, A Rao; Das, Raja; Pathy, M Ramesh; Parameswari, R; Archana, K; Sridharan, T B

    2016-08-01

    Seminal fluid is the secretion from many glands comprised of several organic and inorganic compounds including free amino acids, proteins, fructose, glucosidase, zinc, and other scavenging elements like Mg(2+), Ca(2+), K(+), and Na(+). Therefore, in the view of development of novel approaches and proper diagnosis to male infertility, overall understanding of the biochemical and molecular composition and its role in regulation of sperm quality is highly desirable. Perhaps this can be achieved through artificial intelligence. This study was aimed to elucidate and predict various biochemical markers present in human seminal plasma with three different neural network models. A total of 177 semen samples were collected for this research (both fertile and infertile samples) and immediately processed to prepare a semen analysis report, based on the protocol of the World Health Organization (WHO [2010]). The semen samples were then categorized into oligoasthenospermia (n=35), asthenospermia (n=35), azoospermia (n=22), normospermia (n=34), oligospermia (n=34), and control (n=17). The major biochemical parameters like total protein content, fructose, glucosidase, and zinc content were elucidated by standard protocols. All the biochemical markers were predicted by using three different artificial neural network (ANN) models with semen parameters as inputs. Of the three models, the back propagation neural network model (BPNN) yielded the best results with mean absolute error 0.025, -0.080, 0.166, and -0.057 for protein, fructose, glucosidase, and zinc, respectively. This suggests that BPNN can be used to predict biochemical parameters for the proper diagnosis of male infertility in assisted reproductive technology (ART) centres. AAS: absorption spectroscopy; AI: artificial intelligence; ANN: artificial neural networks; ART: assisted reproductive technology; BPNN: back propagation neural network model; DT: decision tress; MLP: multilayer perceptron; PESA: percutaneous

  6. Position control of a single pneumatic artificial muscle with hysteresis compensation based on modified Prandtl-Ishlinskii model.

    Science.gov (United States)

    Zang, Xizhe; Liu, Yixiang; Heng, Shuai; Lin, Zhenkun; Zhao, Jie

    2017-01-01

    High-performance position control of pneumatic artificial muscles is limited by their inherent nonlinearity and hysteresis. This study aims to model the length/pressure hysteresis of a single pneumatic artificial muscle and to realize its accurate position tracking control with forward hysteresis compensation. The classical Prandtl-Ishlinskii model is widely used in hysteresis modelling and compensation. But it is only effective for symmetric hysteresis. Therefore, a modified Prandtl-Ishlinskii model is built to characterize the asymmetric length/pressure hysteresis of a single pneumatic artificial muscle, by replacing the classical play operators with two more flexible elementary operators to independently describe the ascending branch and descending branch of hysteresis loops. On the basis, a position tracking controller, which is composed of cascade forward hysteresis compensation and simple proportional pressure controller, is designed for the pneumatic artificial muscle. Experiment results show that the MPI model can reproduce the length/pressure hysteresis of the pneumatic artificial muscle, and the proposed controller for the pneumatic artificial muscle can track the reference position signals with high accuracy. By modelling the length/pressure hysteresis with the modified Prandtl-Ishlinskii model and using its inversion for compensation, precise position control of a single pneumatic artificial muscle is achieved.

  7. A self-taught artificial agent for multi-physics computational model personalization.

    Science.gov (United States)

    Neumann, Dominik; Mansi, Tommaso; Itu, Lucian; Georgescu, Bogdan; Kayvanpour, Elham; Sedaghat-Hamedani, Farbod; Amr, Ali; Haas, Jan; Katus, Hugo; Meder, Benjamin; Steidl, Stefan; Hornegger, Joachim; Comaniciu, Dorin

    2016-12-01

    Personalization is the process of fitting a model to patient data, a critical step towards application of multi-physics computational models in clinical practice. Designing robust personalization algorithms is often a tedious, time-consuming, model- and data-specific process. We propose to use artificial intelligence concepts to learn this task, inspired by how human experts manually perform it. The problem is reformulated in terms of reinforcement learning. In an off-line phase, Vito, our self-taught artificial agent, learns a representative decision process model through exploration of the computational model: it learns how the model behaves under change of parameters. The agent then automatically learns an optimal strategy for on-line personalization. The algorithm is model-independent; applying it to a new model requires only adjusting few hyper-parameters of the agent and defining the observations to match. The full knowledge of the model itself is not required. Vito was tested in a synthetic scenario, showing that it could learn how to optimize cost functions generically. Then Vito was applied to the inverse problem of cardiac electrophysiology and the personalization of a whole-body circulation model. The obtained results suggested that Vito could achieve equivalent, if not better goodness of fit than standard methods, while being more robust (up to 11% higher success rates) and with faster (up to seven times) convergence rate. Our artificial intelligence approach could thus make personalization algorithms generalizable and self-adaptable to any patient and any model. Copyright © 2016. Published by Elsevier B.V.

  8. Perception of Sexual Orientation from Facial Structure: A Study with Artificial Face Models.

    Science.gov (United States)

    González-Álvarez, Julio

    2017-07-01

    Research has shown that lay people can perceive sexual orientation better than chance from face stimuli. However, the relation between facial structure and sexual orientation has been scarcely examined. Recently, an extensive morphometric study on a large sample of Canadian people (Skorska, Geniole, Vrysen, McCormick, & Bogaert, 2015) identified three (in men) and four (in women) facial features as unique multivariate predictors of sexual orientation in each sex group. The present study tested the perceptual validity of these facial traits with two experiments based on realistic artificial 3D face models created by manipulating the key parameters and presented to Spanish participants. Experiment 1 included 200 White and Black face models of both sexes. The results showed an overall accuracy (0.74) clearly above chance in a binary hetero/homosexual judgment task and significant differences depending on the race and sex of the face models. Experiment 2 produced five versions of 24 artificial faces of both sexes varying the key parameters in equal steps, and participants had to rate on a 1-7 scale how likely they thought that the depicted person had a homosexual sexual orientation. Rating scores displayed an almost perfect linear regression as a function of the parameter steps. In summary, both experiments demonstrated the perceptual validity of the seven multivariate predictors identified by Skorska et al. and open up new avenues for further research on this issue with artificial face models.

  9. Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model

    Directory of Open Access Journals (Sweden)

    Hui Chen

    2014-01-01

    Full Text Available Metabolic syndrome is worldwide public health problem and is a serious threat to people's health and lives. Understanding the relationship between metabolic syndrome and the physical symptoms is a difficult and challenging task, and few studies have been performed in this field. It is important to classify adults who are at high risk of metabolic syndrome without having to use a biochemical index and, likewise, it is important to develop technology that has a high economic rate of return to simplify the complexity of this detection. In this paper, an artificial intelligence model was developed to identify adults at risk of metabolic syndrome based on physical signs; this artificial intelligence model achieved more powerful capacity for classification compared to the PCLR (principal component logistic regression model. A case study was performed based on the physical signs data, without using a biochemical index, that was collected from the staff of Lanzhou Grid Company in Gansu province of China. The results show that the developed artificial intelligence model is an effective classification system for identifying individuals at high risk of metabolic syndrome.

  10. Estimating tree bole volume using artificial neural network models for four species in Turkey.

    Science.gov (United States)

    Ozçelik, Ramazan; Diamantopoulou, Maria J; Brooks, John R; Wiant, Harry V

    2010-01-01

    Tree bole volumes of 89 Scots pine (Pinus sylvestris L.), 96 Brutian pine (Pinus brutia Ten.), 107 Cilicica fir (Abies cilicica Carr.) and 67 Cedar of Lebanon (Cedrus libani A. Rich.) trees were estimated using Artificial Neural Network (ANN) models. Neural networks offer a number of advantages including the ability to implicitly detect complex nonlinear relationships between input and output variables, which is very helpful in tree volume modeling. Two different neural network architectures were used and produced the Back propagation (BPANN) and the Cascade Correlation (CCANN) Artificial Neural Network models. In addition, tree bole volume estimates were compared to other established tree bole volume estimation techniques including the centroid method, taper equations, and existing standard volume tables. An overview of the features of ANNs and traditional methods is presented and the advantages and limitations of each one of them are discussed. For validation purposes, actual volumes were determined by aggregating the volumes of measured short sections (average 1 meter) of the tree bole using Smalian's formula. The results reported in this research suggest that the selected cascade correlation artificial neural network (CCANN) models are reliable for estimating the tree bole volume of the four examined tree species since they gave unbiased results and were superior to almost all methods in terms of error (%) expressed as the mean of the percentage errors. 2009 Elsevier Ltd. All rights reserved.

  11. Artificial neural network modeling of jatropha oil fueled diesel engine for emission predictions

    Directory of Open Access Journals (Sweden)

    Ganapathy Thirunavukkarasu

    2009-01-01

    Full Text Available This paper deals with artificial neural network modeling of diesel engine fueled with jatropha oil to predict the unburned hydrocarbons, smoke, and NOx emissions. The experimental data from the literature have been used as the data base for the proposed neural network model development. For training the networks, the injection timing, injector opening pressure, plunger diameter, and engine load are used as the input layer. The outputs are hydrocarbons, smoke, and NOx emissions. The feed forward back propagation learning algorithms with two hidden layers are used in the networks. For each output a different network is developed with required topology. The artificial neural network models for hydrocarbons, smoke, and NOx emissions gave R2 values of 0.9976, 0.9976, and 0.9984 and mean percent errors of smaller than 2.7603, 4.9524, and 3.1136, respectively, for training data sets, while the R2 values of 0.9904, 0.9904, and 0.9942, and mean percent errors of smaller than 6.5557, 6.1072, and 4.4682, respectively, for testing data sets. The best linear fit of regression to the artificial neural network models of hydrocarbons, smoke, and NOx emissions gave the correlation coefficient values of 0.98, 0.995, and 0.997, respectively.

  12. The catastrophic dieback of Typha domingensis in a drained and restored East Mediterranean wetland: re-examining proposed models

    Directory of Open Access Journals (Sweden)

    R. Simhayov

    2012-01-01

    Full Text Available We experimentally tested two geochemical models which have been proposed as potential mechanisms leading to the catastrophic dieback of Typha domingensis in constructed Lake Agmon within the drained Hula peatland, northern Israel. An elaborate lysimeter station simulating the conditions imposed by sulphide toxicity and P deficiency models was used to test them experimentally. Rhizosphere pH and redox potential were monitored in situ in real time throughout the summers of 2007 and 2008. A comparative study of redox-reduction simulation was coupled with periodic sampling and analysis of sulphide and other reducing species. N and P were determined in the plant tissues to compute the N:P nutrient limitation index and evaluate nutrient deficiencies. The sulphide toxicity model was not accepted as a viable mechanism because Typha domingensis stands did not show any signs of stress, even when growing in rhizosphere with a sulphide concentration three times that during the actual dieback in 1996. The P limitation model was not supported by the N:P index, which indicated N (16 limitation. Since Typha has now returned and is thriving in Lake Agmon and adjacent drainage canals, we suggest a self-thinning mechanism followed by normal succession of macrophytes in this relatively young constructed wetland as the most logical mechanism to explain the observed dieback.

  13. Ohio Uses Wetlands Program Development Grants to Protect Wetlands

    Science.gov (United States)

    The wetland water quality standards require the use of ORAM score to determine wetland quality. OEPA has also used these tools to evaluate wetland mitigation projects, develop performance standards for wetland mitigation banks and In Lieu Fee programs an.

  14. Weather Radar Estimations Feeding an Artificial Neural Network Model Weather Radar Estimations Feeding an Artificial Neural Network Model

    Directory of Open Access Journals (Sweden)

    Dawei Han

    2012-02-01

    Full Text Available The application of ANNs (Artifi cial Neural Networks has been studied by many researchers in modelling rainfall runoff processes. However, the work so far has been focused on the rainfall data from traditional raingauges. Weather radar is a modern technology which could provide high resolution rainfall in time and space. In this study, a comparison in rainfall runoff modelling between the raingauge and weather radar has been carried out. The data were collected from Brue catchment in Southwest of England, with 49 raingauges covering 136 km2 and two C-band weather radars. This raingauge network is extremely dense (for research purposes and does not represent the usual raingauge density in operational flood forecasting systems. The ANN models were set up with both lumped and spatial rainfall input. The results showed that raingauge data outperformed radar data in all the events tested, regardless of the lumped and spatial input. La aplicación de Redes Neuronales Artificiales (RNA en el modelado de lluvia-flujo ha sido estudiada ampliamente. Sin embargo, hasta ahora se han utilizado datos provenientes de pluviómetros tradicionales. Los radares meteorológicos son una tecnología moderna que puede proveer datos de lluvia de alta resolución en tiempo y espacio. Este es un trabajo de comparación en el modelado lluvia-flujo entre pluviómetros y radares meteorológicos. Los datos provienen de la cuenca del río Brue en el suroeste de Inglaterra, con 49 pluviómetros cubriendo 136 km2 y dos radares meteorológicos en la banda C. Esta red de pluviómetros es extremadamente densa (para investigación y no representa la densidad usual en sistemas de predicción de inundaciones. Los modelos de RNA fueron implementados con datos de entrada de lluvia tanto espaciados como no distribuidos. Los resultados muestran que los datos de los pluviómetros fueron mejores que los datos de los radares en todos los eventos probados.

  15. A review on integration of artificial intelligence into water quality modelling.

    Science.gov (United States)

    Chau, Kwok-wing

    2006-07-01

    With the development of computing technology, numerical models are often employed to simulate flow and water quality processes in coastal environments. However, the emphasis has conventionally been placed on algorithmic procedures to solve specific problems. These numerical models, being insufficiently user-friendly, lack knowledge transfers in model interpretation. This results in significant constraints on model uses and large gaps between model developers and practitioners. It is a difficult task for novice application users to select an appropriate numerical model. It is desirable to incorporate the existing heuristic knowledge about model manipulation and to furnish intelligent manipulation of calibration parameters. The advancement in artificial intelligence (AI) during the past decade rendered it possible to integrate the technologies into numerical modelling systems in order to bridge the gaps. The objective of this paper is to review the current state-of-the-art of the integration of AI into water quality modelling. Algorithms and methods studied include knowledge-based system, genetic algorithm, artificial neural network, and fuzzy inference system. These techniques can contribute to the integrated model in different aspects and may not be mutually exclusive to one another. Some future directions for further development and their potentials are explored and presented.

  16. MULTI AGENT-BASED ENVIRONMENTAL LANDSCAPE (MABEL) - AN ARTIFICIAL INTELLIGENCE SIMULATION MODEL: SOME EARLY ASSESSMENTS

    OpenAIRE

    Alexandridis, Konstantinos T.; Pijanowski, Bryan C.

    2002-01-01

    The Multi Agent-Based Environmental Landscape model (MABEL) introduces a Distributed Artificial Intelligence (DAI) systemic methodology, to simulate land use and transformation changes over time and space. Computational agents represent abstract relations among geographic, environmental, human and socio-economic variables, with respect to land transformation pattern changes. A multi-agent environment is developed providing task-nonspecific problem-solving abilities, flexibility on achieving g...

  17. Application of artificial intelligence (AI) concepts to the development of space flight parts approval model

    Science.gov (United States)

    Krishnan, G. S.

    1997-01-01

    A cost effective model which uses the artificial intelligence techniques in the selection and approval of parts is presented. The knowledge which is acquired from the specialists for different part types are represented in a knowledge base in the form of rules and objects. The parts information is stored separately in a data base and is isolated from the knowledge base. Validation, verification and performance issues are highlighted.

  18. Dynamic Analysis of a Phytoplankton-Fish Model with Biological and Artificial Control

    OpenAIRE

    Wang, Yapei; Zhao, Min; Pan, Xinhong; Dai, Chuanjun

    2014-01-01

    We investigate a nonlinear model of the interaction between phytoplankton and fish, which uses a pair of semicontinuous systems with biological and artificial control. First, the existence of an order-1 periodic solution to the system is analyzed using a Poincaré map and a geometric method. The stability conditions of the order-1 periodic solution are obtained by a theoretical mathematical analysis. Furthermore, based on previous analysis, we investigate the bifurcation in the order-1 periodi...

  19. Application of Artificial Neural Networks in the Heart Electrical Axis Position Conclusion Modeling

    Science.gov (United States)

    Bakanovskaya, L. N.

    2016-08-01

    The article touches upon building of a heart electrical axis position conclusion model using an artificial neural network. The input signals of the neural network are the values of deflections Q, R and S; and the output signal is the value of the heart electrical axis position. Training of the network is carried out by the error propagation method. The test results allow concluding that the created neural network makes a conclusion with a high degree of accuracy.

  20. Artificial Neural Network Modelling of the Energy Content of Municipal Solid Wastes in Northern Nigeria

    Directory of Open Access Journals (Sweden)

    M. B. Oumarou

    2017-12-01

    Full Text Available The study presents an application of the artificial neural network model using the back propagation learning algorithm to predict the actual calorific value of the municipal solid waste in major cities of the northern part of Nigeria, with high population densities and intense industrial activities. These cities are: Kano, Damaturu, Dutse, Bauchi, Birnin Kebbi, Gusau, Maiduguri, Katsina and Sokoto. Experimental data of the energy content and the physical characterization of the municipal solid waste serve as the input parameter in nature of wood, grass, metal, plastic, food remnants, leaves, glass and paper. Comparative studies were made by using the developed model, the experimental results and a correlation which was earlier developed by the authors to predict the energy content. While predicting the actual calorific value, the maximum error was 0.94% for the artificial neural network model and 5.20% by the statistical correlation. The network with eight neurons and an R2 = 0.96881 in the hidden layer results in a stable and optimum network. This study showed that the artificial neural network approach could successfully be used for energy content predictions from the municipal solid wastes in Northern Nigeria and other areas of similar waste stream and composition.

  1. Pesticide mitigation capacities of constructed wetlands

    Science.gov (United States)

    Matthew T. Moore; Charles M. Cooper; Sammie Smith; John H. Rodgers

    2000-01-01

    This research focused on using constructed wetlands along field perimeters to buffer receiving water against potential effects of pesticides associated with storm runoff. The current study incorporated wetland mesocosm sampling following simulated runoff events using chlorpyrifos, atrazine, and metolachlor. Through this data collection and simple model analysis,...

  2. Narrative theories as computational models: reader-oriented theory and artificial intelligence

    Energy Technology Data Exchange (ETDEWEB)

    Galloway, P.

    1983-12-01

    In view of the rapid development of reader-oriented theory and its interest in dynamic models of narrative, the author speculates in a serious way about what such models might look like in computational terms. Researchers in artificial intelligence (AI) have already begun to develop models of story understanding as the emphasis in ai research has shifted toward natural language understanding and as ai has allied itself with cognitive psychology and linguistics to become cognitive science. Research in ai and in narrative theory share many common interests and problems and both studies might benefit from an exchange of ideas. 11 references.

  3. Impacts of water development on aquatic macroinvertebrates, amphibians, and plants in wetlands of a semi-arid landscape

    Science.gov (United States)

    Euliss, Ned H.; Mushet, David M.

    2004-01-01

    We compared the macroinvertebrate and amphibian communities of 12 excavated and 12 natural wetlands in western North Dakota, USA, to assess the effects of artificially lengthened hydroperiods on the biotic communities of wetlands in this semi-arid region. Excavated wetlands were much deeper and captured greater volumes of water than natural wetlands. Most excavated wetlands maintained water throughout the study period (May to October 1999), whereas most of the natural wetlands were dry by June. Excavated wetlands were largely unvegetated or contained submergent and deep-marsh plant species. The natural wetlands had two well-defined vegetative zones populated by plant species typical of wet meadows and shallow marshes. Excavated wetlands had a richer aquatic macroinvertebrate community that included several predatory taxa not found in natural wetlands. Taxa adapted to the short hydroperiods of seasonal wetlands were largely absent from excavated wetlands. The amphibian community of natural and excavated wetlands included the boreal chorus frog (Pseudacris maculata), northern leopard frog (Rana pipiens), plains spadefoot (Scaphiopus bombifrons), Woodhouse's toad (Bufo woodhousii woodhousii), and tiger salamander (Ambystoma tigrinum). The plains spadefoot occurred only in natural wetlands while tiger salamanders occurred in all 12 excavated wetlands and only one natural wetland. Boreal chorus frogs and northern leopard frogs were present in both wetland types; however, they successfully reproduced only in wetlands lacking tiger salamanders. Artificially extending the hydroperiod of wetlands by excavation has greatly influenced the composition of native biotic communities adapted to the naturally short hydroperiods of wetlands in this semi-arid region. The compositional change of the biotic communities can be related to hydrological changes and biotic interactions, especially predation related to excavation.

  4. Wonderful Wetlands: An Environmental Education Curriculum Guide for Wetlands.

    Science.gov (United States)

    King County Parks Div., Redmond, WA.

    This curriculum guide was designed to give teachers, students, and society a better understanding of wetlands in the hope that they learn why wetlands should be valued and preserved. It explores what is meant by wetlands, functions and values of wetlands, wetland activities, and wetland offerings which benefit animal and plant life, recreation,…

  5. An artificial pancreas provided a novel model of blood glucose level variability in beagles.

    Science.gov (United States)

    Munekage, Masaya; Yatabe, Tomoaki; Kitagawa, Hiroyuki; Takezaki, Yuka; Tamura, Takahiko; Namikawa, Tsutomu; Hanazaki, Kazuhiro

    2015-12-01

    Although the effects on prognosis of blood glucose level variability have gained increasing attention, it is unclear whether blood glucose level variability itself or the manifestation of pathological conditions that worsen prognosis. Then, previous reports have not been published on variability models of perioperative blood glucose levels. The aim of this study is to establish a novel variability model of blood glucose concentration using an artificial pancreas. We maintained six healthy, male beagles. After anesthesia induction, a 20-G venous catheter was inserted in the right femoral vein and an artificial pancreas (STG-22, Nikkiso Co. Ltd., Tokyo, Japan) was connected for continuous blood glucose monitoring and glucose management. After achieving muscle relaxation, total pancreatectomy was performed. After 1 h of stabilization, automatic blood glucose control was initiated using the artificial pancreas. Blood glucose level varied for 8 h, alternating between the target blood glucose values of 170 and 70 mg/dL. Eight hours later, the experiment was concluded. Total pancreatectomy was performed for 62 ± 13 min. Blood glucose swings were achieved 9.8 ± 2.3 times. The average blood glucose level was 128.1 ± 5.1 mg/dL with an SD of 44.6 ± 3.9 mg/dL. The potassium levels after stabilization and at the end of the experiment were 3.5 ± 0.3 and 3.1 ± 0.5 mmol/L, respectively. In conclusion, the results of the present study demonstrated that an artificial pancreas contributed to the establishment of a novel variability model of blood glucose levels in beagles.

  6. Development of an indicator to monitor mediterranean wetlands.

    Science.gov (United States)

    Sanchez, Antonio; Abdul Malak, Dania; Guelmami, Anis; Perennou, Christian

    2015-01-01

    Wetlands are sensitive ecosystems that are increasingly subjected to threats from anthropogenic factors. In the last decades, coastal Mediterranean wetlands have been suffering considerable pressures from land use change, intensification of urban growth, increasing tourism infrastructure and intensification of agricultural practices. Remote sensing (RS) and Geographic Information Systems (GIS) techniques are efficient tools that can support monitoring Mediterranean coastal wetlands on large scales and over long periods of time. The study aims at developing a wetland indicator to support monitoring Mediterranean coastal wetlands using these techniques. The indicator makes use of multi-temporal Landsat images, land use reference layers, a 50m numerical model of the territory (NMT) and Corine Land Cover (CLC) for the identification and mapping of wetlands. The approach combines supervised image classification techniques making use of vegetation indices and decision tree analysis to identify the surface covered by wetlands at a given date. A validation process is put in place to compare outcomes with existing local wetland inventories to check the results reliability. The indicator´s results demonstrate an improvement in the level of precision of change detection methods achieved by traditional tools providing reliability up to 95% in main wetland areas. The results confirm that the use of RS techniques improves the precision of wetland detection compared to the use of CLC for wetland monitoring and stress the strong relation between the level of wetland detection and the nature of the wetland areas and the monitoring scale considered.

  7. Emergence of nutrient-cycling feedbacks related to plant size and invasion success in a wetland community-ecosystem model

    Czech Academy of Sciences Publication Activity Database

    Currie, W. S.; Goldberg, D. E.; Martina, J.; Wildová, Radka; Farrer, E.; Elgersma, K. J.

    2014-01-01

    Roč. 282, 24 JUNE (2014), s. 69-82 ISSN 0304-3800 Institutional support: RVO:67985939 Keywords : wetland * invasive species * biodiversity Subject RIV: EH - Ecology, Behaviour OBOR OECD: Ecology Impact factor: 2.321, year: 2014

  8. Use of geomorphic, hydrologic, and nitrogen mass balance data to model ecosystem nitrate retention in tidal freshwater wetlands

    OpenAIRE

    E. D. Seldomridge; K. L. Prestegaard

    2012-01-01

    Geomorphic characteristics have been used as scaling parameters to predict water and other fluxes in many systems. In this study, we combined geomorphic analysis with in-situ mass balance studies of nitrate retention (NR) to evaluate which geomorphic scaling parameters best predicted NR in a tidal freshwater wetland ecosystem. Geomorphic characteristics were measured for 267 individual marshes that constitute the freshwater tidal wetland ecosystem of the Patuxent River...

  9. Envisat/ASAR Images for the Calibration of Wind Drag Action in the Doñana Wetlands 2D Hydrodynamic Model

    Directory of Open Access Journals (Sweden)

    Anaïs Ramos-Fuertes

    2013-12-01

    Full Text Available Doñana National Park wetlands are located in southwest Spain, on the right bank of the Guadalquivir River, near the Atlantic Ocean coast. The wetlands dry out completely every summer and progressively flood again throughout the fall and winter seasons. Given the flatness of Doñana’s topography, the wind drag action can induce the flooding or emergence of extensive areas, detectable in remote sensing images. Envisat/ASAR scenes acquired before and during strong and persistent wind episodes enabled the spatial delineation of the wind-induced water displacement. A two-dimensional hydrodynamic model of Doñana wetlands was built in 2006 with the aim to predict the effect of proposed hydrologic restoration actions within Doñana’s basin. In this work, on-site wind records and concurrent ASAR scenes are used for the calibration of the wind-drag modeling by assessing different formulations. Results show a good adjustment between the modeled and observed wind drag effect. Displacements of up to 2 km in the wind direction are satisfactorily reproduced by the hydrodynamic model, while including an atmospheric stability parameter led to no significant improvement of the results. Such evidence will contribute to a more accurate simulation of hypothetic or design scenarios, when no information is available for the atmospheric stability assessment.

  10. Modeling of Malachite Green Removal from Aqueous Solutions by Nanoscale Zerovalent Zinc Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Wenqian Ruan

    2017-12-01

    Full Text Available The commercially available nanoscale zerovalent zinc (nZVZ was used as an adsorbent for the removal of malachite green (MG from aqueous solutions. This material was characterized by X-ray diffraction and X-ray photoelectron spectroscopy. The advanced experimental design tools were adopted to study the effect of process parameters (viz. initial pH, temperature, contact time and initial concentration and to reduce number of trials and cost. Response surface methodology and rapidly developing artificial intelligence technologies, i.e., artificial neural network coupled with particle swarm optimization (ANN-PSO and artificial neural network coupled with genetic algorithm (ANN-GA were employed for predicting the optimum process variables and obtaining the maximum removal efficiency of MG. The results showed that the removal efficiency predicted by ANN-GA (94.12% was compatible with the experimental value (90.72%. Furthermore, the Langmuir isotherm was found to be the best model to describe the adsorption of MG onto nZVZ, while the maximum adsorption capacity was calculated to be 1000.00 mg/g. The kinetics for adsorption of MG onto nZVZ was found to follow the pseudo-second-order kinetic model. Thermodynamic parameters (ΔG0, ΔH0 and ΔS0 were calculated from the Van’t Hoff plot of lnKc vs. 1/T in order to discuss the removal mechanism of MG.

  11. Modelling of pneumatic muscle actuator using Hill's model with different approximations of static characteristics of artificial muscle

    OpenAIRE

    Piteľ Ján; Tóthová Mária

    2016-01-01

    For modelling and simulation of pneumatic muscle actuators the mathematical dependence of the muscle force on the muscle contraction at different pressures in the muscles is necessary to know. For this purpose the static characteristics of the pneumatic artificial muscle type FESTO MAS-20-250N used in the experiments were approximated. In the paper there are shown some simulation results of the pneumatic muscle actuator dynamics using modified Hill's muscle model, in which four different appr...

  12. Subpixel Inundation Mapping Using Landsat-8 OLI and UAV Data for a Wetland Region on the Zoige Plateau, China

    Directory of Open Access Journals (Sweden)

    Haoming Xia

    2017-01-01

    Full Text Available Wetland inundation is crucial to the survival and prosperity of fauna and flora communities in wetland ecosystems. Even small changes in surface inundation may result in a substantial impact on the wetland ecosystem characteristics and function. This study presented a novel method for wetland inundation mapping at a subpixel scale in a typical wetland region on the Zoige Plateau, northeast Tibetan Plateau, China, by combining use of an unmanned aerial vehicle (UAV and Landsat-8 Operational Land Imager (OLI data. A reference subpixel inundation percentage (SIP map at a Landsat-8 OLI 30 m pixel scale was first generated using high resolution UAV data (0.16 m. The reference SIP map and Landsat-8 OLI imagery were then used to develop SIP estimation models using three different retrieval methods (Linear spectral unmixing (LSU, Artificial neural networks (ANN, and Regression tree (RT. Based on observations from 2014, the estimation results indicated that the estimation model developed with RT method could provide the best fitting results for the mapping wetland SIP (R2 = 0.933, RMSE = 8.73% compared to the other two methods. The proposed model with RT method was validated with observations from 2013, and the estimated SIP was highly correlated with the reference SIP, with an R2 of 0.986 and an RMSE of 9.84%. This study highlighted the value of high resolution UAV data and globally and freely available Landsat data in combination with the developed approach for monitoring finely gradual inundation change patterns in wetland ecosystems.

  13. Artificial neural network modelling of a large-scale wastewater treatment plant operation.

    Science.gov (United States)

    Güçlü, Dünyamin; Dursun, Sükrü

    2010-11-01

    Artificial Neural Networks (ANNs), a method of artificial intelligence method, provide effective predictive models for complex processes. Three independent ANN models trained with back-propagation algorithm were developed to predict effluent chemical oxygen demand (COD), suspended solids (SS) and aeration tank mixed liquor suspended solids (MLSS) concentrations of the Ankara central wastewater treatment plant. The appropriate architecture of ANN models was determined through several steps of training and testing of the models. ANN models yielded satisfactory predictions. Results of the root mean square error, mean absolute error and mean absolute percentage error were 3.23, 2.41 mg/L and 5.03% for COD; 1.59, 1.21 mg/L and 17.10% for SS; 52.51, 44.91 mg/L and 3.77% for MLSS, respectively, indicating that the developed model could be efficiently used. The results overall also confirm that ANN modelling approach may have a great implementation potential for simulation, precise performance prediction and process control of wastewater treatment plants.

  14. Evaluation of a combined modelling-remote sensing method for estimating net radiation in a wetland: a case study in the Nebraska Sand Hills, USA

    International Nuclear Information System (INIS)

    Goodin, D.G.

    1995-01-01

    Close-range measurement combined with modelling of incoming radiation is used to evaluate the prospect of remotely-measuring net radiation of a wetland environment located in the Sand Hills of Nebraska. Results indicate that net radiation can be measured with an accuracy comparable to that of conventional instruments. Sources of error are identified and discussed. Possible application of the methodology to satellite remote sensing is considered. (author)

  15. Estimating temporal trend in the presence of spatial complexity: a Bayesian hierarchical model for a wetland plant population undergoing restoration.

    Directory of Open Access Journals (Sweden)

    Thomas J Rodhouse

    Full Text Available Monitoring programs that evaluate restoration and inform adaptive management are important for addressing environmental degradation. These efforts may be well served by spatially explicit hierarchical approaches to modeling because of unavoidable spatial structure inherited from past land use patterns and other factors. We developed bayesian hierarchical models to estimate trends from annual density counts observed in a spatially structured wetland forb (Camassia quamash [camas] population following the cessation of grazing and mowing on the study area, and in a separate reference population of camas. The restoration site was bisected by roads and drainage ditches, resulting in distinct subpopulations ("zones" with different land use histories. We modeled this spatial structure by fitting zone-specific intercepts and slopes. We allowed spatial covariance parameters in the model to vary by zone, as in stratified kriging, accommodating anisotropy and improving computation and biological interpretation. Trend estimates provided evidence of a positive effect of passive restoration, and the strength of evidence was influenced by the amount of spatial structure in the model. Allowing trends to vary among zones and accounting for topographic heterogeneity increased precision of trend estimates. Accounting for spatial autocorrelation shifted parameter coefficients in ways that varied among zones depending on strength of statistical shrinkage, autocorrelation and topographic heterogeneity--a phenomenon not widely described. Spatially explicit estimates of trend from hierarchical models will generally be more useful to land managers than pooled regional estimates and provide more realistic assessments of uncertainty. The ability to grapple with historical contingency is an appealing benefit of this approach.

  16. Modeling river total bed material load discharge using artificial intelligence approaches (based on conceptual inputs)

    Science.gov (United States)

    Roushangar, Kiyoumars; Mehrabani, Fatemeh Vojoudi; Shiri, Jalal

    2014-06-01

    This study presents Artificial Intelligence (AI)-based modeling of total bed material load through developing the accuracy level of the predictions of traditional models. Gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS)-based models were developed and validated for estimations. Sediment data from Qotur River (Northwestern Iran) were used for developing and validation of the applied techniques. In order to assess the applied techniques in relation to traditional models, stream power-based and shear stress-based physical models were also applied in the studied case. The obtained results reveal that developed AI-based models using minimum number of dominant factors, give more accurate results than the other applied models. Nonetheless, it was revealed that k-fold test is a practical but high-cost technique for complete scanning of applied data and avoiding the over-fitting.

  17. Past, present and prospect of an Artificial Intelligence (AI) based model for sediment transport prediction

    Science.gov (United States)

    Afan, Haitham Abdulmohsin; El-shafie, Ahmed; Mohtar, Wan Hanna Melini Wan; Yaseen, Zaher Mundher

    2016-10-01

    An accurate model for sediment prediction is a priority for all hydrological researchers. Many conventional methods have shown an inability to achieve an accurate prediction of suspended sediment. These methods are unable to understand the behaviour of sediment transport in rivers due to the complexity, noise, non-stationarity, and dynamism of the sediment pattern. In the past two decades, Artificial Intelligence (AI) and computational approaches have become a remarkable tool for developing an accurate model. These approaches are considered a powerful tool for solving any non-linear model, as they can deal easily with a large number of data and sophisticated models. This paper is a review of all AI approaches that have been applied in sediment modelling. The current research focuses on the development of AI application in sediment transport. In addition, the review identifies major challenges and opportunities for prospective research. Throughout the literature, complementary models superior to classical modelling.

  18. Landscape unit based digital elevation model development for the freshwater wetlands within the Arthur C. Marshall Loxahatchee National Wildlife Refuge, Southeastern Florida

    Science.gov (United States)

    Xie, Zhixiao; Liu, Zhongwei; Jones, John W.; Higer, Aaron L.; Telis, Pamela A.

    2011-01-01

    The hydrologic regime is a critical limiting factor in the delicate ecosystem of the greater Everglades freshwater wetlands in south Florida that has been severely altered by management activities in the past several decades. "Getting the water right" is regarded as the key to successful restoration of this unique wetland ecosystem. An essential component to represent and model its hydrologic regime, specifically water depth, is an accurate ground Digital Elevation Model (DEM). The Everglades Depth Estimation Network (EDEN) supplies important hydrologic data, and its products (including a ground DEM) have been well received by scientists and resource managers involved in Everglades restoration. This study improves the EDEN DEMs of the Loxahatchee National Wildlife Refuge, also known as Water Conservation Area 1 (WCA1), by adopting a landscape unit (LU) based interpolation approach. The study first filtered the input elevation data based on newly available vegetation data, and then created a separate geostatistical model (universal kriging) for each LU. The resultant DEMs have encouraging cross-validation and validation results, especially since the validation is based on an independent elevation dataset (derived by subtracting water depth measurements from EDEN water surface elevations). The DEM product of this study will directly benefit hydrologic and ecological studies as well as restoration efforts. The study will also be valuable for a broad range of wetland studies.

  19. Design and Modeling of RF Power Amplifiers with Radial Basis Function Artificial Neural Networks

    OpenAIRE

    Ali Reza Zirak; Sobhan Roshani

    2016-01-01

    A radial basis function (RBF) artificial neural network model for a designed high efficiency radio frequency class-F power amplifier (PA) is presented in this paper. The presented amplifier is designed at 1.8 GHz operating frequency with 12 dB of gain and 36 dBm of 1dB output compression point. The obtained power added efficiency (PAE) for the presented PA is 76% under 26 dBm input power. The proposed RBF model uses input and DC power of the PA as inputs variables and considers output power a...

  20. Hybrid Modeling and Optimization of Manufacturing Combining Artificial Intelligence and Finite Element Method

    CERN Document Server

    Quiza, Ramón; Davim, J Paulo

    2012-01-01

    Artificial intelligence (AI) techniques and the finite element method (FEM) are both powerful computing tools, which are extensively used for modeling and optimizing manufacturing processes. The combination of these tools has resulted in a new flexible and robust approach as several recent studies have shown. This book aims to review the work already done in this field as well as to expose the new possibilities and foreseen trends. The book is expected to be useful for postgraduate students and researchers, working in the area of modeling and optimization of manufacturing processes.

  1. Modeling of steam distillation mechanism during steam injection process using artificial intelligence.

    Science.gov (United States)

    Daryasafar, Amin; Ahadi, Arash; Kharrat, Riyaz

    2014-01-01

    Steam distillation as one of the important mechanisms has a great role in oil recovery in thermal methods and so it is important to simulate this process experimentally and theoretically. In this work, the simulation of steam distillation is performed on sixteen sets of crude oil data found in the literature. Artificial intelligence (AI) tools such as artificial neural network (ANN) and also adaptive neurofuzzy interference system (ANFIS) are used in this study as effective methods to simulate the distillate recoveries of these sets of data. Thirteen sets of data were used to train the models and three sets were used to test the models. The developed models are highly compatible with respect to input oil properties and can predict the distillate yield with minimum entry. For showing the performance of the proposed models, simulation of steam distillation is also done using modified Peng-Robinson equation of state. Comparison between the calculated distillates by ANFIS and neural network models and also equation of state-based method indicates that the errors of the ANFIS model for training data and test data sets are lower than those of other methods.

  2. A review on the integration of artificial intelligence into coastal modeling.

    Science.gov (United States)

    Chau, Kwokwing

    2006-07-01

    With the development of computing technology, mechanistic models are often employed to simulate processes in coastal environments. However, these predictive tools are inevitably highly specialized, involving certain assumptions and/or limitations, and can be manipulated only by experienced engineers who have a thorough understanding of the underlying theories. This results in significant constraints on their manipulation as well as large gaps in understanding and expectations between the developers and practitioners of a model. The recent advancements in artificial intelligence (AI) technologies are making it possible to integrate machine learning capabilities into numerical modeling systems in order to bridge the gaps and lessen the demands on human experts. The objective of this paper is to review the state-of-the-art in the integration of different AI technologies into coastal modeling. The algorithms and methods studied include knowledge-based systems, genetic algorithms, artificial neural networks, and fuzzy inference systems. More focus is given to knowledge-based systems, which have apparent advantages over the others in allowing more transparent transfers of knowledge in the use of models and in furnishing the intelligent manipulation of calibration parameters. Of course, the other AI methods also have their individual contributions towards accurate and reliable predictions of coastal processes. The integrated model might be very powerful, since the advantages of each technique can be combined.

  3. Modeling of Steam Distillation Mechanism during Steam Injection Process Using Artificial Intelligence

    Science.gov (United States)

    Ahadi, Arash; Kharrat, Riyaz

    2014-01-01

    Steam distillation as one of the important mechanisms has a great role in oil recovery in thermal methods and so it is important to simulate this process experimentally and theoretically. In this work, the simulation of steam distillation is performed on sixteen sets of crude oil data found in the literature. Artificial intelligence (AI) tools such as artificial neural network (ANN) and also adaptive neurofuzzy interference system (ANFIS) are used in this study as effective methods to simulate the distillate recoveries of these sets of data. Thirteen sets of data were used to train the models and three sets were used to test the models. The developed models are highly compatible with respect to input oil properties and can predict the distillate yield with minimum entry. For showing the performance of the proposed models, simulation of steam distillation is also done using modified Peng-Robinson equation of state. Comparison between the calculated distillates by ANFIS and neural network models and also equation of state-based method indicates that the errors of the ANFIS model for training data and test data sets are lower than those of other methods. PMID:24883365

  4. Identification of mathematical model of human breathing in system “Artificial lungs – self-contained breathing apparatus”

    Science.gov (United States)

    Onevsky, P. M.; Onevsky, M. P.; Pogonin, V. A.

    2018-03-01

    The structure and mathematical models of the main subsystems of the control system of the “Artificial Lungs” are presented. This structure implements the process of imitation of human external respiration in the system “Artificial lungs - self-contained breathing apparatus”. A presented algorithm for parametric identification of the model is based on spectral operators, which allows using it in real time.

  5. Modelling Hydrological Processes in Created Freshwater Wetlands:an Integrated System Approach%人工淡水湿地的水文过程模拟:综合系统法(摘要)

    Institute of Scientific and Technical Information of China (English)

    张立; 威廉·杰·米奇

    2006-01-01

    This study investigates hydrologic processes of four different flow-through created freshwater wetlands in Ohio, USA, by use of several versions of a simple daily mass-balance water budget model.The model includes surface inflows and outflows, precipitation, evapotranspiration, and groundwater seepage. We calibrated the daily water budget for two experimental wetlands that had pumped inflow during 1999 and validated it during 2000 - 2002 on the same basins. The coefficient of prediction efficiency is 0.70 and the modelled hydroperiod followed observed water depths during the calibration period well. The average retention time in the calibration year 1999 was 4.4 - 4.6 days. The model was applied to a 3-ha created riparian wetland that receives river flooding. Results illustrated that this wetland has developed a hydroperiod with more than sufficient flooding to ensure that it will meet the hydrologic criteria of a formal jurisdictional wetland definition in the USA. Water budget predictions for a stormwater wetland provided useful design information for hydroperiod and hydrologic dynamics prior to the construction of that system. The model was simulated for average, dry, and wet years. An integrated systems approach was developed using a STELLA 7.0 with its capabilities of dynamicinterface level control (e. g. buttons and switches) features.

  6. Use of artificial intelligence to identify cardiovascular compromise in a model of hemorrhagic shock.

    Science.gov (United States)

    Glass, Todd F; Knapp, Jason; Amburn, Philip; Clay, Bruce A; Kabrisky, Matt; Rogers, Steven K; Garcia, Victor F

    2004-02-01

    To determine whether a prototype artificial intelligence system can identify volume of hemorrhage in a porcine model of controlled hemorrhagic shock. Prospective in vivo animal model of hemorrhagic shock. Research foundation animal surgical suite; computer laboratories of collaborating industry partner. Nineteen, juvenile, 25- to 35-kg, male and female swine. Anesthetized animals were instrumented for arterial and systemic venous pressure monitoring and blood sampling, and a splenectomy was performed. Following a 1-hr stabilization period, animals were hemorrhaged in aliquots to 10, 20, 30, 35, 40, 45, and 50% of total blood volume with a 10-min recovery between each aliquot. Data were downloaded directly from a commercial monitoring system into a proprietary PC-based software package for analysis. Arterial and venous blood gas values, glucose, and cardiac output were collected at specified intervals. Electrocardiogram, electroencephalogram, mixed venous oxygen saturation, temperature (core and blood), mean arterial pressure, pulmonary artery pressure, central venous pressure, pulse oximetry, and end-tidal CO(2) were continuously monitored and downloaded. Seventeen of 19 animals (89%) died as a direct result of hemorrhage. Stored data streams were analyzed by the prototype artificial intelligence system. For this project, the artificial intelligence system identified and compared three electrocardiographic features (R-R interval, QRS amplitude, and R-S interval) from each of nine unknown samples of the QRS complex. We found that the artificial intelligence system, trained on only three electrocardiographic features, identified hemorrhage volume with an average accuracy of 91% (95% confidence interval, 84-96%). These experiments demonstrate that an artificial intelligence system, based solely on the analysis of QRS amplitude, R-R interval, and R-S interval of an electrocardiogram, is able to accurately identify hemorrhage volume in a porcine model of lethal

  7. Daily Crude Oil Price Forecasting Using Hybridizing Wavelet and Artificial Neural Network Model

    Directory of Open Access Journals (Sweden)

    Ani Shabri

    2014-01-01

    Full Text Available A new method based on integrating discrete wavelet transform and artificial neural networks (WANN model for daily crude oil price forecasting is proposed. The discrete Mallat wavelet transform is used to decompose the crude price series into one approximation series and some details series (DS. The new series obtained by adding the effective one approximation series and DS component is then used as input into the ANN model to forecast crude oil price. The relative performance of WANN model was compared to regular ANN model for crude oil forecasting at lead times of 1 day for two main crude oil price series, West Texas Intermediate (WTI and Brent crude oil spot prices. In both cases, WANN model was found to provide more accurate crude oil prices forecasts than individual ANN model.

  8. Analysis of Artificial Neural Network in Erosion Modeling: A Case Study of Serang Watershed

    Science.gov (United States)

    Arif, N.; Danoedoro, P.; Hartono

    2017-12-01

    Erosion modeling is an important measuring tool for both land users and decision makers to evaluate land cultivation and thus it is necessary to have a model to represent the actual reality. Erosion models are a complex model because of uncertainty data with different sources and processing procedures. Artificial neural networks can be relied on for complex and non-linear data processing such as erosion data. The main difficulty in artificial neural network training is the determination of the value of each network input parameters, i.e. hidden layer, momentum, learning rate, momentum, and RMS. This study tested the capability of artificial neural network application in the prediction of erosion risk with some input parameters through multiple simulations to get good classification results. The model was implemented in Serang Watershed, Kulonprogo, Yogyakarta which is one of the critical potential watersheds in Indonesia. The simulation results showed the number of iterations that gave a significant effect on the accuracy compared to other parameters. A small number of iterations can produce good accuracy if the combination of other parameters was right. In this case, one hidden layer was sufficient to produce good accuracy. The highest training accuracy achieved in this study was 99.32%, occurred in ANN 14 simulation with combination of network input parameters of 1 HL; LR 0.01; M 0.5; RMS 0.0001, and the number of iterations of 15000. The ANN training accuracy was not influenced by the number of channels, namely input dataset (erosion factors) as well as data dimensions, rather it was determined by changes in network parameters.

  9. Why are wetlands important?

    Science.gov (United States)

    Wetlands are among the most productive ecosystems in the world, comparable to rain forests and coral reefs. An immense variety of species of microbes, plants, insects, amphibians, reptiles, birds, fish, and mammals can be part of a wetland ecosystem.

  10. Percent Wetland Cover

    Data.gov (United States)

    U.S. Environmental Protection Agency — Wetlands act as filters, removing or diminishing the amount of pollutants that enter surface water. Higher values for percent of wetland cover (WETLNDSPCT) may be...

  11. Percent Wetland Cover (Future)

    Data.gov (United States)

    U.S. Environmental Protection Agency — Wetlands act as filters, removing or diminishing the amount of pollutants that enter surface water. Higher values for percent of wetland cover (WETLNDSPCT) may be...

  12. VSWI Wetlands Advisory Layer

    Data.gov (United States)

    Vermont Center for Geographic Information — This dataset represents the DEC Wetlands Program's Advisory layer. This layer makes the most up-to-date, non-jurisdictional, wetlands mapping avaiable to the public...

  13. Modeling and experiments on the drive characteristics of high-strength water hydraulic artificial muscles

    Science.gov (United States)

    Zhang, Zengmeng; Hou, Jiaoyi; Ning, Dayong; Gong, Xiaofeng; Gong, Yongjun

    2017-05-01

    Fluidic artificial muscles are popular in robotics and function as biomimetic actuators. Their pneumatic version has been widely investigated. A novel water hydraulic artificial muscle (WHAM) with high strength is developed in this study. WHAMs can be applied to underwater manipulators widely used in ocean development because of their environment-friendly characteristics, high force-to-weight ratio, and good bio-imitability. Therefore, the strength of WHAMs has been improved to fit the requirements of underwater environments and the work pressure of water hydraulic components. However, understanding the mechanical behaviors of WHAMs is necessary because WHAMs use work media and pressure control that are different from those used by pneumatic artificial muscles. This paper presents the static and dynamic characteristics of the WHAM system, including the water hydraulic pressure control circuit. A test system is designed and built to analyze the drive characteristics of the developed WHAM. The theoretical relationships among the amount of contraction, pressure, and output drawing force of the WHAM are tested and verified. A linearized transfer function is proposed, and the dynamic characteristics of the WHAM are investigated through simulation and inertia load experiments. Simulation results agree with the experimental results and show that the proposed model can be applied to the control of WHAM actuators.

  14. Artificial Intelligence in Numerical Modeling of Silver Nanoparticles Prepared in Montmorillonite Interlayer Space

    Directory of Open Access Journals (Sweden)

    Parvaneh Shabanzadeh

    2013-01-01

    Full Text Available Artificial neural network (ANN models have the capacity to eliminate the need for expensive experimental investigation in various areas of manufacturing processes, including the casting methods. An understanding of the interrelationships between input variables is essential for interpreting the sensitivity data and optimizing the design parameters. Silver nanoparticles (Ag-NPs have attracted considerable attention for chemical, physical, and medical applications due to their exceptional properties. The nanocrystal silver was synthesized into an interlamellar space of montmorillonite by using the chemical reduction technique. The method has an advantage of size control which is essential in nanometals synthesis. Silver nanoparticles with nanosize and devoid of aggregation are favorable for several properties. In this investigation, the accuracy of artificial neural network training algorithm was applied in studying the effects of different parameters on the particles, including the AgNO3 concentration, reaction temperature, UV-visible wavelength, and montmorillonite (MMT d-spacing on the prediction of size of silver nanoparticles. Analysis of the variance showed that the AgNO3 concentration and temperature were the most significant factors affecting the size of silver nanoparticles. Using the best performing artificial neural network, the optimum conditions predicted were a concentration of AgNO3 of 1.0 (M, MMT d-spacing of 1.27 nm, reaction temperature of 27°C, and wavelength of 397.50 nm.

  15. Towards artificial tissue models: past, present, and future of 3D bioprinting.

    Science.gov (United States)

    Arslan-Yildiz, Ahu; El Assal, Rami; Chen, Pu; Guven, Sinan; Inci, Fatih; Demirci, Utkan

    2016-03-01

    Regenerative medicine and tissue engineering have seen unprecedented growth in the past decade, driving the field of artificial tissue models towards a revolution in future medicine. Major progress has been achieved through the development of innovative biomanufacturing strategies to pattern and assemble cells and extracellular matrix (ECM) in three-dimensions (3D) to create functional tissue constructs. Bioprinting has emerged as a promising 3D biomanufacturing technology, enabling precise control over spatial and temporal distribution of cells and ECM. Bioprinting technology can be used to engineer artificial tissues and organs by producing scaffolds with controlled spatial heterogeneity of physical properties, cellular composition, and ECM organization. This innovative approach is increasingly utilized in biomedicine, and has potential to create artificial functional constructs for drug screening and toxicology research, as well as tissue and organ transplantation. Herein, we review the recent advances in bioprinting technologies and discuss current markets, approaches, and biomedical applications. We also present current challenges and provide future directions for bioprinting research.

  16. Using LIDAR and Quickbird Data to Model Plant Production and Quantify Uncertainties Associated with Wetland Detection and Land Cover Generalizations

    Science.gov (United States)

    Cook, Bruce D.; Bolstad, Paul V.; Naesset, Erik; Anderson, Ryan S.; Garrigues, Sebastian; Morisette, Jeffrey T.; Nickeson, Jaime; Davis, Kenneth J.

    2009-01-01

    Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the MOderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30 m to 1 km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600 ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400 m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.

  17. A model of pathways to artificial superintelligence catastrophe for risk and decision analysis

    Science.gov (United States)

    Barrett, Anthony M.; Baum, Seth D.

    2017-03-01

    An artificial superintelligence (ASI) is an artificial intelligence that is significantly more intelligent than humans in all respects. Whilst ASI does not currently exist, some scholars propose that it could be created sometime in the future, and furthermore that its creation could cause a severe global catastrophe, possibly even resulting in human extinction. Given the high stakes, it is important to analyze ASI risk and factor the risk into decisions related to ASI research and development. This paper presents a graphical model of major pathways to ASI catastrophe, focusing on ASI created via recursive self-improvement. The model uses the established risk and decision analysis modelling paradigms of fault trees and influence diagrams in order to depict combinations of events and conditions that could lead to AI catastrophe, as well as intervention options that could decrease risks. The events and conditions include select aspects of the ASI itself as well as the human process of ASI research, development and management. Model structure is derived from published literature on ASI risk. The model offers a foundation for rigorous quantitative evaluation and decision-making on the long-term risk of ASI catastrophe.

  18. Mechanical Characterization and Numerical Modelling of Rubber Shockpads in 3G Artificial Turf

    Directory of Open Access Journals (Sweden)

    David Cole

    2018-02-01

    Full Text Available Third generation (3G artificial turf systems use in sporting applications is increasingly prolific. These multi-component systems are comprised of a range of polymeric and elastomeric materials that exhibit non-linear and strain rate dependent behaviours under the complex loads applied from players and equipment. To further study and better understand the behaviours of these systems, the development of a numerical model to accurately predict individual layers’ behaviour as well as the overall system response under different loading conditions is necessary. The purpose of this study was to characterise and model the mechanical behaviour of a rubber shockpad found in 3G artificial surfaces for vertical shock absorption using finite element analysis. A series of uniaxial compression tests were performed to characterise the mechanical behaviour of the shockpad. Compression loading was performed at 0.9 Hz to match human walking speeds. A Microfoam material model was selected from the PolyUMod library and optimised using MCalibration software before being imported into ABAQUS for analysis. A finite element model was created for the shockpad using ABAQUS and a compressive load applied to match that of the experimental data. Friction coefficients were altered to view the effect on the loading response. The accuracy of the model was compared using a series of comparative measures including the energy loss and root mean square error.

  19. Unsteady flamelet modelling of spray flames using deep artificial neural networks

    Science.gov (United States)

    Owoyele, Opeoluwa; Kundu, Prithwish; Ameen, Muhsin; Echekki, Tarek; Som, Sibendu

    2017-11-01

    We investigate the applicability of the tabulated, multidimensional unsteady flamelet model and artificial neural networks (TFM-ANN) to lifted diesel spray flame simulations. The tabulated flamelet model (TFM), based on the widely known flamelet assumption, eliminates the use of a progress variable and has been shown to successfully model global diesel spray flame characteristics in previous studies. While the TFM has shown speed-up compared to other models and predictive capabilities across a range of ambient conditions, it involves the storage of multidimensional tables, requiring large memory and multidimensional interpolation schemes. This work discusses the implementation of deep artificial neural networks (ANN) to replace the use of large tables and multidimensional interpolation. The proposed framework is validated by applying it to an n-dodecane spray flame (ECN Spray A) at different conditions using a 4 dimensional flamelet library. The validations are then extended for the simulations using a 5-dimensional flamelet table applied to the combustion of methyl decanoate in a compression ignition engine. Different ANN topologies, optimization algorithms and speed-up techniques are explored and details of computational resources required for TFM-ANN and the TFM are also presented. The overall tools and algorithms used in this study can be directly extended to other multidimensional tabulated models.

  20. The potential of artificial aging for modelling of natural aging processes of ballpoint ink.

    Science.gov (United States)

    Weyermann, Céline; Spengler, Bernhard

    2008-08-25

    Artificial aging has been used to reproduce natural aging processes in an accelerated pace. Questioned documents were exposed to light or high temperature in a well-defined manner in order to simulate an increased age. This may be used to study the aging processes or to date documents by reproducing their aging curve. Ink was studied especially because it is deposited on the paper when a document, such as a contract, is produced. Once on the paper, aging processes start through degradation of dyes, solvents drying and resins polymerisation. Modelling of dye's and solvent's aging was attempted. These processes, however, follow complex pathways, influenced by many factors which can be classified as three major groups: ink composition, paper type and storage conditions. The influence of these factors is such that different aging states can be obtained for an identical point in time. Storage conditions in particular are difficult to simulate, as they are dependent on environmental conditions (e.g. intensity and dose of light, temperature, air flow, humidity) and cannot be controlled in the natural aging of questioned documents. The problem therefore lies more in the variety of different conditions a questioned document might be exposed to during its natural aging, rather than in the simulation of such conditions in the laboratory. Nevertheless, a precise modelling of natural aging curves based on artificial aging curves is obtained when performed on the same paper and ink. A standard model for aging processes of ink on paper is therefore presented that is based on a fit of aging curves to a power law of solvent concentrations as a function of time. A mathematical transformation of artificial aging curves into modelled natural aging curves results in excellent overlap with data from real natural aging processes.

  1. Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp.

    Science.gov (United States)

    Pathak, Lakshmi; Singh, Vineeta; Niwas, Ram; Osama, Khwaja; Khan, Saif; Haque, Shafiul; Tripathi, C K M; Mishra, B N

    2015-01-01

    Cholesterol oxidase (COD) is a bi-functional FAD-containing oxidoreductase which catalyzes the oxidation of cholesterol into 4-cholesten-3-one. The wider biological functions and clinical applications of COD have urged the screening, isolation and characterization of newer microbes from diverse habitats as a source of COD and optimization and over-production of COD for various uses. The practicability of statistical/ artificial intelligence techniques, such as response surface methodology (RSM), artificial neural network (ANN) and genetic algorithm (GA) have been tested to optimize the medium composition for the production of COD from novel strain Streptomyces sp. NCIM 5500. All experiments were performed according to the five factor central composite design (CCD) and the generated data was analysed using RSM and ANN. GA was employed to optimize the models generated by RSM and ANN. Based upon the predicted COD concentration, the model developed with ANN was found to be superior to the model developed with RSM. The RSM-GA approach predicted maximum of 6.283 U/mL COD production, whereas the ANN-GA approach predicted a maximum of 9.93 U/mL COD concentration. The optimum concentrations of the medium variables predicted through ANN-GA approach were: 1.431 g/50 mL soybean, 1.389 g/50 mL maltose, 0.029 g/50 mL MgSO4, 0.45 g/50 mL NaCl and 2.235 ml/50 mL glycerol. The experimental COD concentration was concurrent with the GA predicted yield and led to 9.75 U/mL COD production, which was nearly two times higher than the yield (4.2 U/mL) obtained with the un-optimized medium. This is the very first time we are reporting the statistical versus artificial intelligence based modeling and optimization of COD production by Streptomyces sp. NCIM 5500.

  2. Investigation of Methane and Soil Carbon Dynamics Using Near Surface Geophysical Methods at the Tanoma Educational Wetland Site, Tanoma, Pennsylvania

    Science.gov (United States)

    Seidel, A. D.; Mount, G.

    2017-12-01

    Studies to constrain methane budgets of Pennsylvania have sought to quantify the amount and rate of fugitive methane released during industrial natural gas development. However, contributions from other environmental systems such as artificial wetlands used to treat part of the 300 million gallons per day of acid mine drainage (AMD) are often not understated or not considered. The artificial wetlands are sources of both biogenic and thermogenic methane and are used to treat AMD which would otherwise flow untreated into Pennsylvania surface waters. Our research utilizes a combination of indirect non-invasive geophysical methods (ground penetrating radar, GPR) and the complex refractive index model, aerial imagery, and direct measurements (coring and gas traps) to estimate the contribution of biogenic methane from wetlands and legacy thermogenic methane from acid mine drainage from a flooded coal mine at an artificial wetland designed to treat these polluted waters at Tanoma, Pennsylvania. Our approach uses (3D) GPR surveys to define the thickness of the soil from the surface to the regolith-bedrock interface to create a volume model of potential biogenic gas stores. Velocity data derived from the GPR is then used to calculate the dielectric permittivity of the soil and then modeled for gas content when considering the saturation, porosity and amount of soil present. Depth-profile cores are extracted to confirm soil column interfaces and determine changes in soil carbon content. Comparisons of gas content are made with gas traps placed across the wetlands that measure the variability of gaseous methane released. In addition, methane dissolved in the waters from biogenic processes in the wetland and thermogenic processes underground are analyzed by a gas chromatograph to quantify those additions. In sum, these values can then be extrapolated to estimate carbon stocks in AMD areas such as those with similar water quality and vegetation types in the Appalachian region

  3. Model of wetland development of the Amapá coast during the late Holocene

    Directory of Open Access Journals (Sweden)

    José T.F. Guimarães

    2010-06-01

    Full Text Available The modern vegetation types, sedimentary sequences, pollen records and radiocarbon dating obtained from three sediment cores from Calçoene Coastal Plain were used to provide a palaeoecological history during the late Holocene of Amapá coastal wetland according to flood regime, sea-level and climatic changes. Based on these records, four phases of vegetation development are presented and they probably reflect the interaction between the flow energy to the sediment accumulation and the brackish/freshwater influence in the vegetation. This work suggests interchanges among time periods characterized by marine and fluvial influence. The longitudinal profile did not reveal the occurrence of mangrove in the sediment deposited around 2100 yr B.P. During the second phase, the mud progressively filled the depressions and tidal channels. The mangrove probably started its development on the channel edge, and the herbaceous field on the elevated sectors. The third phase is characterized by the interruption of mangrove development and the increase of "várzea" vegetation that may be due to the decrease in porewater salinity related to a decrease in marine water influence. The last phase is represented by the mangrove and "várzea" increase. The correlation between current patterns of geobotanical unit distribution and palaeovegetation indicates that mangrove and "várzea" forests are migrating over the herbaceous field on the topographically highest part of the studied coast, which can be related to a relative sea-level rise.Os tipos de vegetação atual, sequências sedimentares, dados de pólen e datações por radiocarbono obtidas em três testemunhos de sedimento da planície costeira de Calçoene foram utilizados para estabelecer uma história paleoecológica durante o Holoceno superior das zonas úmidas costeiras do Amapá conforme as mudanças no regime de inundação, nível do mar e clima. Baseado nestes três registros, quatro fases de

  4. APPLYING TEACHING-LEARNING TO ARTIFICIAL BEE COLONY FOR PARAMETER OPTIMIZATION OF SOFTWARE EFFORT ESTIMATION MODEL

    Directory of Open Access Journals (Sweden)

    THANH TUNG KHUAT

    2017-05-01

    Full Text Available Artificial Bee Colony inspired by the foraging behaviour of honey bees is a novel meta-heuristic optimization algorithm in the community of swarm intelligence algorithms. Nevertheless, it is still insufficient in the speed of convergence and the quality of solutions. This paper proposes an approach in order to tackle these downsides by combining the positive aspects of TeachingLearning based optimization and Artificial Bee Colony. The performance of the proposed method is assessed on the software effort estimation problem, which is the complex and important issue in the project management. Software developers often carry out the software estimation in the early stages of the software development life cycle to derive the required cost and schedule for a project. There are a large number of methods for effort estimation in which COCOMO II is one of the most widely used models. However, this model has some restricts because its parameters have not been optimized yet. In this work, therefore, we will present the approach to overcome this limitation of COCOMO II model. The experiments have been conducted on NASA software project dataset and the obtained results indicated that the improvement of parameters provided better estimation capabilities compared to the original COCOMO II model.

  5. Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural Network

    Science.gov (United States)

    Yao, Weigang; Liou, Meng-Sing

    2012-01-01

    The present study demonstrates the efficacy of a recurrent artificial neural network to provide a high fidelity time-dependent nonlinear reduced-order model (ROM) for flutter/limit-cycle oscillation (LCO) modeling. An artificial neural network is a relatively straightforward nonlinear method for modeling an input-output relationship from a set of known data, for which we use the radial basis function (RBF) with its parameters determined through a training process. The resulting RBF neural network, however, is only static and is not yet adequate for an application to problems of dynamic nature. The recurrent neural network method [1] is applied to construct a reduced order model resulting from a series of high-fidelity time-dependent data of aero-elastic simulations. Once the RBF neural network ROM is constructed properly, an accurate approximate solution can be obtained at a fraction of the cost of a full-order computation. The method derived during the study has been validated for predicting nonlinear aerodynamic forces in transonic flow and is capable of accurate flutter/LCO simulations. The obtained results indicate that the present recurrent RBF neural network is accurate and efficient for nonlinear aero-elastic system analysis

  6. Investigation and modeling on protective textiles using artificial neural networks for defense applications

    International Nuclear Information System (INIS)

    Ramaiah, Gurumurthy B.; Chennaiah, Radhalakshmi Y.; Satyanarayanarao, Gurumurthy K.

    2010-01-01

    Kevlar 29 is a class of Kevlar fiber used for protective applications primarily by the military and law enforcement agencies for bullet resistant vests, hence for these reasons military has found that armors reinforced with Kevlar 29 multilayer fabrics which offer 25-40% better fragmentation resistance and provide better fit with greater comfort. The objective of this study is to investigate and develop an artificial neural network model for analyzing the performance of ballistic fabrics made from Kevlar 29 single layer fabrics using their material properties as inputs. Data from fragment simulation projectile (FSP) ballistic penetration measurements at 244 m/s has been used to demonstrate the modeling aspects of artificial neural networks. The neural network models demonstrated in this paper is based on back propagation (BP) algorithm which is inbuilt in MATLAB 7.1 software and is used for studies in science, technology and engineering. In the present research, comparisons are also made between the measured values of samples selected for building the neural network model and network predicted results. The analysis of the results for network predicted and experimental samples used in this study showed similarity.

  7. The road to higher permanence and biodiversity in exurban wetlands.

    Science.gov (United States)

    Urban, Mark C; Roehm, Robert

    2018-01-01

    Exurban areas are expanding throughout the world, yet their effects on local biodiversity remain poorly understood. Wetlands, in particular, face ongoing and substantial threats from exurban development. We predicted that exurbanization would reduce the diversity of wetland amphibian and invertebrate communities and that more spatially aggregated residential development would leave more undisturbed natural land, thereby promoting greater local diversity. Using structural equation models, we tested a series of predictions about the direct and indirect pathways by which exurbanization extent, spatial pattern, and wetland characteristics might affect diversity patterns in 38 wetlands recorded during a growing season. We used redundancy, indicator species, and nested community analyses to evaluate how exurbanization affected species composition. In contrast to expectations, we found higher diversity in exurban wetlands. We also found that housing aggregation did not significantly affect diversity. Exurbanization affected biodiversity indirectly by increasing roads and development, which promoted permanent wetlands with less canopy cover and more aquatic vegetation. These pond characteristics supported greater diversity. However, exurbanization was associated with fewer temporary wetlands and fewer of the species that depend on these habitats. Moreover, the best indicator species for an exurban wetland was the ram's head snail, a common disease vector in disturbed ponds. Overall, results suggest that exurbanization is homogenizing wetlands into more permanent water bodies. These more permanent, exurban ponds support higher overall animal diversity, but exclude temporary wetland specialists. Conserving the full assemblage of wetland species in expanding exurban regions throughout the world will require protecting and creating temporary wetlands.

  8. Artificial neural network modeling and optimization of ultrahigh pressure extraction of green tea polyphenols.

    Science.gov (United States)

    Xi, Jun; Xue, Yujing; Xu, Yinxiang; Shen, Yuhong

    2013-11-01

    In this study, the ultrahigh pressure extraction of green tea polyphenols was modeled and optimized by a three-layer artificial neural network. A feed-forward neural network trained with an error back-propagation algorithm was used to evaluate the effects of pressure, liquid/solid ratio and ethanol concentration on the total phenolic content of green tea extracts. The neural network coupled with genetic algorithms was also used to optimize the conditions needed to obtain the highest yield of tea polyphenols. The obtained optimal architecture of artificial neural network model involved a feed-forward neural network with three input neurons, one hidden layer with eight neurons and one output layer including single neuron. The trained network gave the minimum value in the MSE of 0.03 and the maximum value in the R(2) of 0.9571, which implied a good agreement between the predicted value and the actual value, and confirmed a good generalization of the network. Based on the combination of neural network and genetic algorithms, the optimum extraction conditions for the highest yield of green tea polyphenols were determined as follows: 498.8 MPa for pressure, 20.8 mL/g for liquid/solid ratio and 53.6% for ethanol concentration. The total phenolic content of the actual measurement under the optimum predicated extraction conditions was 582.4 ± 0.63 mg/g DW, which was well matched with the predicted value (597.2mg/g DW). This suggests that the artificial neural network model described in this work is an efficient quantitative tool to predict the extraction efficiency of green tea polyphenols. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

  9. An articulated predictive model for fluid-free artificial basilar membrane as broadband frequency sensor

    Science.gov (United States)

    Ahmed, Riaz; Banerjee, Sourav

    2018-02-01

    In this article, an extremely versatile predictive model for a newly developed Basilar meta-Membrane (BM2) sensors is reported with variable engineering parameters that contribute to it's frequency selection capabilities. The predictive model reported herein is for advancement over existing method by incorporating versatile and nonhomogeneous (e.g. functionally graded) model parameters that could not only exploit the possibilities of creating complex combinations of broadband frequency sensors but also explain the unique unexplained physical phenomenon that prevails in BM2, e.g. tailgating waves. In recent years, few notable attempts were made to fabricate the artificial basilar membrane, mimicking the mechanics of the human cochlea within a very short range of frequencies. To explain the operation of these sensors a few models were proposed. But, we fundamentally argue the "fabrication to explanation" approach and proposed the model driven predictive design process for the design any (BM2) as broadband sensors. Inspired by the physics of basilar membrane, frequency domain predictive model is proposed where both the material and geometrical parameters can be arbitrarily varied. Broadband frequency is applicable in many fields of science, engineering and technology, such as, sensors for chemical, biological and acoustic applications. With the proposed model, which is three times faster than its FEM counterpart, it is possible to alter the attributes of the selected length of the designed sensor using complex combinations of model parameters, based on target frequency applications. Finally, the tailgating wave peaks in the artificial basilar membranes that prevails in the previously reported experimental studies are also explained using the proposed model.

  10. The Evolution of a Malignancy Risk Prediction Model for Thyroid Nodules Using the Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Shahram Paydar

    2016-01-01

    Full Text Available Background: Clinically frank thyroid nodules are common and believed to be present in 4% to 10% of the adult population in the United States. In the current literature, fine needle aspiration biopsies are considered to be the milestone of a model which helps the physician decide whether a certain thyroid nodule needs a surgical approach or not. A considerable fact is that sensitivity and specificity of the fine needle aspiration varies significantly as it remains highly dependent on the operator as well as the cytologist’s skills. Practically, in the above group of patients, thyroid lobectomy/isthmusectomy becomes mandatory for attaining a definitive diagnosis where the majority (70%-80% have a benign surgical pathology. The scattered nature of clinically gathered data and analysis of their relevant variables need a compliant statistical method. The artificial neural network is a branch of artificial intelligence. We have hypothesized that conduction of an artificial neural network applied to certain clinical attributes could develop a malignancy risk assessment tool to help physicians interpret the fine needle aspiration biopsy results of thyroid nodules in a context composed of patient’s clinical variables, known as malignancy related risk factors. Methods: We designed and trained an artificial neural network on a prospectively formed cohort gathered over a four year period (2007-2011. The study population comprised 345 subjects who underwent thyroid resection at Nemazee and Rajaee hospitals, tertiary care centers of Shiraz University of Medical Sciences, and Rajaee Hospital as a level I trauma center in Shiraz, Iran after having undergone thyroid fine needle aspiration. Histopathological results of the fine needle aspirations and surgical specimens were analyzed and compared by experienced, board-certified pathologists who lacked knowledge of the fine needle aspiration results for thyroid malignancy. Results: We compared the preoperative

  11. Delineating wetland catchments and modeling hydrologic connectivity using lidar data and aerial imagery

    Science.gov (United States)

    In traditional watershed delineation and topographic modeling, surface depressions are generally treated as spurious features and simply removed from a digital elevation model (DEM) to enforce flow continuity of water across the topographic surface to the watershed outlets. In re...

  12. Designing of plant artificial chromosome (PAC) by using the Chlorella smallest chromosome as a model system.

    Science.gov (United States)

    Noutoshi, Y; Arai, R; Fujie, M; Yamada, T

    1997-01-01

    As a model for plant-type chromosomes, we have been characterizing molecular organization of the Chlorella vulgaris C-169 chromosome I. To identify chromosome structural elements including the centromeric region and replication origins, we constructed a chromosome I specific cosmid library and aligned each cosmid clones to generate contigs. So far, more than 80% of the entire chromosome I has been covered. A complete clonal physical reconstitution of chromosome I provides information on the structure and genomic organization of plant genome. We propose our strategy to construct an artificial chromosome by assembling the functional chromosome structural elements identified on Chrorella chromosome I.

  13. Artificial Neural Networks for Reducing Computational Effort in Active Truncated Model Testing of Mooring Lines

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Voie, Per Erlend Torbergsen; Høgsberg, Jan Becker

    2015-01-01

    simultaneously, this method is very demanding in terms of numerical efficiency and computational power. Therefore, this method has not yet proved to be feasible. It has recently been shown how a hybrid method combining classical numerical models and artificial neural networks (ANN) can provide a dramatic...... prior to the experiment and with a properly trained ANN it is no problem to obtain accurate simulations much faster than real time-without any need for large computational capacity. The present study demonstrates how this hybrid method can be applied to the active truncated experiments yielding a system...

  14. Artificial neural network (ANN) approach for modeling Zn(II) adsorption in batch process

    Energy Technology Data Exchange (ETDEWEB)

    Yildiz, Sayiter [Engineering Faculty, Cumhuriyet University, Sivas (Turkmenistan)

    2017-09-15

    Artificial neural networks (ANN) were applied to predict adsorption efficiency of peanut shells for the removal of Zn(II) ions from aqueous solutions. Effects of initial pH, Zn(II) concentrations, temperature, contact duration and adsorbent dosage were determined in batch experiments. The sorption capacities of the sorbents were predicted with the aid of equilibrium and kinetic models. The Zn(II) ions adsorption onto peanut shell was better defined by the pseudo-second-order kinetic model, for both initial pH, and temperature. The highest R{sup 2} value in isotherm studies was obtained from Freundlich isotherm for the inlet concentration and from Temkin isotherm for the sorbent amount. The high R{sup 2} values prove that modeling the adsorption process with ANN is a satisfactory approach. The experimental results and the predicted results by the model with the ANN were found to be highly compatible with each other.

  15. Modeling mechanical properties of cast aluminum alloy using artificial neural network

    International Nuclear Information System (INIS)

    Jokhio, M.H.; Panhwar, M.I.

    2009-01-01

    Modeling is widely used to investigate the mechanical properties of engineering materials due to increasing demand of low cost and high strength to weight ratio for many engineering applications. The aluminum casting alloys are cost competitive material and possess the desired properties. The mechanical properties largely depend upon composition of alloys and their processing method. Alloy design involves controlling mechanical properties via optimization of the composition and processing parameters. For optimization the possible root is empirical modeling and its more refined version is the analysis of the wide range of data using ANN (Artificial Neural Networks) modeling. The modeling of mechanical properties of the aluminum alloys are the main objective of present work. For this purpose, some data were collected and experimentally prepared using conventional casting method. A MLP (Multilayer Perceptron) network was developed, which is trained by using the error back propagation algorithm. (author)

  16. Modeling Microstructural Evolution During Dynamic Recrystallization of Alloy D9 Using Artificial Neural Network

    Science.gov (United States)

    Mandal, Sumantra; Sivaprasad, P. V.; Dube, R. K.

    2007-12-01

    An artificial neural network (ANN) model was developed to predict the microstructural evolution of a 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless steel (Alloy D9) during dynamic recrystallization (DRX). The input parameters were strain, strain rate, and temperature whereas microstructural features namely, %DRX and average grain size were the output parameters. The ANN was trained with the database obtained from various industrial scale metal-forming operations like forge hammer, hydraulic press, and rolling carried out in the temperature range 1173-1473 K to various strain levels. The performance of the model was evaluated using a wide variety of statistical indices and the predictability of the model was found to be good. The combined influence of temperature and strain on microstructural features has been simulated employing the developed model. The results were found to be consistent with the relevant fundamental metallurgical phenomena.

  17. An empirical model of the Earth's bow shock based on an artificial neural network

    Science.gov (United States)

    Pallocchia, Giuseppe; Ambrosino, Danila; Trenchi, Lorenzo

    2014-05-01

    All of the past empirical models of the Earth's bow shock shape were obtained by best-fitting some given surfaces to sets of observed crossings. However, the issue of bow shock modeling can be addressed by means of artificial neural networks (ANN) as well. In this regard, here it is presented a perceptron, a simple feedforward network, which computes the bow shock distance along a given direction using the two angular coordinates of that direction, the bow shock predicted distance RF79 (provided by Formisano's model (F79)) and the upstream alfvénic Mach number Ma. After a brief description of the ANN architecture and training method, we discuss the results of the statistical comparison, performed over a test set of 1140 IMP8 crossings, between the prediction accuracies of ANN and F79 models.

  18. Development of Artificial Neural Network Model of Crude Oil Distillation Column

    Directory of Open Access Journals (Sweden)

    Ali Hussein Khalaf

    2016-02-01

    Full Text Available Artificial neural network in MATLAB simulator is used to model Baiji crude oil distillation unit based on data generated from aspen-HYSYS simulator. Thirteen inputs, six outputs and over 1487 data set are used to model the actual unit. Nonlinear autoregressive network with exogenous inputs (NARXand back propagation algorithm are used for training. Seventy percent of data are used for training the network while the remaining  thirty percent are used for testing  and validating the network to determine its prediction accuracy. One hidden layer and 34 hidden neurons are used for the proposed network with MSE of 0.25 is obtained. The number of neuron are selected based on less MSE for the network. The model founded to predict the optimal operating conditions for different objective functions within the training limit since ANN models are poor extrapolators. They are usually only reliable within the range of data that they had been trained for.

  19. Development of Artificial Neural Network Model of Crude Oil Distillation Column

    Directory of Open Access Journals (Sweden)

    Duraid F. Ahmed

    2016-02-01

    Full Text Available Artificial neural network in MATLAB simulator is used to model Baiji crude oil distillation unit based on data generated from aspen-HYSYS simulator. Thirteen inputs, six outputs and over 1487 data set are used to model the actual unit. Nonlinear autoregressive network with exogenous inputs (NARX and back propagation algorithm are used for training. Seventy percent of data are used for training the network while the remaining thirty percent are used for testing and validating the network to determine its prediction accuracy. One hidden layer and 34 hidden neurons are used for the proposed network with MSE of 0.25 is obtained. The number of neuron are selected based on less MSE for the network. The model founded to predict the optimal operating conditions for different objective functions within the training limit since ANN models are poor extrapolators. They are usually only reliable within the range of data that they had been trained for.

  20. DECISION WITH ARTIFICIAL NEURAL NETWORKS IN DISCRETE EVENT SIMULATION MODELS ON A TRAFFIC SYSTEM

    Directory of Open Access Journals (Sweden)

    Marília Gonçalves Dutra da Silva

    2016-04-01

    Full Text Available ABSTRACT This work aims to demonstrate the use of a mechanism to be applied in the development of the discrete-event simulation models that perform decision operations through the implementation of an artificial neural network. Actions that involve complex operations performed by a human agent in a process, for example, are often modeled in simplified form with the usual mechanisms of simulation software. Therefore, it was chosen a traffic system controlled by a traffic officer with a flow of vehicles and pedestrians to demonstrate the proposed solution. From a module built in simulation software itself, it was possible to connect the algorithm for intelligent decision to the simulation model. The results showed that the model elaborated responded as expected when it was submitted to actions, which required different decisions to maintain the operation of the system with changes in the flow of people and vehicles.

  1. Rolling force prediction for strip casting using theoretical model and artificial intelligence

    Institute of Scientific and Technical Information of China (English)

    CAO Guang-ming; LI Cheng-gang; ZHOU Guo-ping; LIU Zhen-yu; WU Di; WANG Guo-dong; LIU Xiang-hua

    2010-01-01

    Rolling force for strip casting of 1Cr17 ferritic stainless steel was predicted using theoretical model and artificial intelligence.Solution zone was classified into two parts by kiss point position during casting strip.Navier-Stokes equation in fluid mechanics and stream function were introduced to analyze the rheological property of liquid zone and mushy zone,and deduce the analytic equation of unit compression stress distribution.The traditional hot rolling model was still used in the solid zone.Neural networks based on feedforward training algorithm in Bayesian regularization were introduced to build model for kiss point position.The results show that calculation accuracy for verification data of 94.67% is in the range of+7.0%,which indicates that the predicting accuracy of this model is very high.

  2. Artificial neural network (ANN) approach for modeling Zn(II) adsorption in batch process

    International Nuclear Information System (INIS)

    Yildiz, Sayiter

    2017-01-01

    Artificial neural networks (ANN) were applied to predict adsorption efficiency of peanut shells for the removal of Zn(II) ions from aqueous solutions. Effects of initial pH, Zn(II) concentrations, temperature, contact duration and adsorbent dosage were determined in batch experiments. The sorption capacities of the sorbents were predicted with the aid of equilibrium and kinetic models. The Zn(II) ions adsorption onto peanut shell was better defined by the pseudo-second-order kinetic model, for both initial pH, and temperature. The highest R"2 value in isotherm studies was obtained from Freundlich isotherm for the inlet concentration and from Temkin isotherm for the sorbent amount. The high R"2 values prove that modeling the adsorption process with ANN is a satisfactory approach. The experimental results and the predicted results by the model with the ANN were found to be highly compatible with each other.

  3. FLASH-FLOOD MODELLING WITH ARTIFICIAL NEURAL NETWORKS USING RADAR RAINFALL ESTIMATES

    Directory of Open Access Journals (Sweden)

    Dinu Cristian

    2017-09-01

    Full Text Available The use of artificial neural networks (ANNs in modelling the hydrological processes has become a common approach in the last two decades, among side the traditional methods. In regard to the rainfall-runoff modelling, in both traditional and ANN models the use of ground rainfall measurements is prevalent, which can be challenging in areas with low rain gauging station density, especially in catchments where strong focused rainfall can generate flash-floods. The weather radar technology can prove to be a solution for such areas by providing rain estimates with good time and space resolution. This paper presents a comparison between different ANN setups using as input both ground and radar observations for modelling the rainfall-runoff process for Bahluet catchment, with focus on a flash-flood observed in the catchment.

  4. Reduction of neonicotinoid insecticide residues in Prairie wetlands by common wetland plants.

    Science.gov (United States)

    Main, Anson R; Fehr, Jessica; Liber, Karsten; Headley, John V; Peru, Kerry M; Morrissey, Christy A

    2017-02-01

    Neonicotinoid insecticides are frequently detected in wetlands during the early to mid-growing period of the Canadian Prairie cropping season. These detections also overlap with the growth of macrophytes that commonly surround agricultural wetlands which we hypothesized may reduce neonicotinoid transport and retention in wetlands. We sampled 20 agricultural wetlands and 11 macrophyte species in central Saskatchewan, Canada, over eight weeks to investigate whether macrophytes were capable of reducing movement of neonicotinoids from cultivated fields and/or reducing concentrations in surface water by accumulating insecticide residues into their tissues. Study wetlands were surrounded by clothianidin-treated canola and selected based on the presence (n=10) or absence (n=10) of a zonal plant community. Neonicotinoids were positively detected in 43% of wetland plants, and quantified in 8% of all plant tissues sampled. Three plant species showed high rates of detection: 78% Equisetum arvense (clothianidin, range: wetlands had higher detection frequency and water concentrations of clothianidin (β±S.E.: -0.77±0.26, P=0.003) and thiamethoxam (β±S.E.: -0.69±0.35, P=0.049) than vegetated wetlands. We assessed the importance of wetland characteristics (e.g. vegetative zone width, emergent plant height, water depth) on neonicotinoid concentrations in Prairie wetlands over time using linear mixed-effects models. Clothianidin concentrations were significantly lower in wetlands surrounded by taller plants (β±S.E.: -0.57±0.12, P≤0.001). The results of this study suggest that macrophytes can play an important role in mitigating water contamination by accumulating neonicotinoids and possibly slowing transport to wetlands during the growing season. Copyright © 2016 Elsevier B.V. All rights reserved.

  5. Artificial neural network models for prediction of intestinal permeability of oligopeptides

    Directory of Open Access Journals (Sweden)

    Kim Min-Kook

    2007-07-01

    Full Text Available Abstract Background Oral delivery is a highly desirable property for candidate drugs under development. Computational modeling could provide a quick and inexpensive way to assess the intestinal permeability of a molecule. Although there have been several studies aimed at predicting the intestinal absorption of chemical compounds, there have been no attempts to predict intestinal permeability on the basis of peptide sequence information. To develop models for predicting the intestinal permeability of peptides, we adopted an artificial neural network as a machine-learning algorithm. The positive control data consisted of intestinal barrier-permeable peptides obtained by the peroral phage display technique, and the negative control data were prepared from random sequences. Results The capacity of our models to make appropriate predictions was validated by statistical indicators including sensitivity, specificity, enrichment curve, and the area under the receiver operating characteristic (ROC curve (the ROC score. The training and test set statistics indicated that our models were of strikingly good quality and could discriminate between permeable and random sequences with a high level of confidence. Conclusion We developed artificial neural network models to predict the intestinal permeabilities of oligopeptides on the basis of peptide sequence information. Both binary and VHSE (principal components score Vectors of Hydrophobic, Steric and Electronic properties descriptors produced statistically significant training models; the models with simple neural network architectures showed slightly greater predictive power than those with complex ones. We anticipate that our models will be applicable to the selection of intestinal barrier-permeable peptides for generating peptide drugs or peptidomimetics.

  6. Time-lapse gravity data for monitoring and modeling artificial recharge through a thick unsaturated zone

    Science.gov (United States)

    Kennedy, Jeffrey R.; Ferre, Ty P.A.; Creutzfeldt, Benjamin

    2016-01-01

    Groundwater-level measurements in monitoring wells or piezometers are the most common, and often the only, hydrologic measurements made at artificial recharge facilities. Measurements of gravity change over time provide an additional source of information about changes in groundwater storage, infiltration, and for model calibration. We demonstrate that for an artificial recharge facility with a deep groundwater table, gravity data are more sensitive to movement of water through the unsaturated zone than are groundwater levels. Groundwater levels have a delayed response to infiltration, change in a similar manner at many potential monitoring locations, and are heavily influenced by high-frequency noise induced by pumping; in contrast, gravity changes start immediately at the onset of infiltration and are sensitive to water in the unsaturated zone. Continuous gravity data can determine infiltration rate, and the estimate is only minimally affected by uncertainty in water-content change. Gravity data are also useful for constraining parameters in a coupled groundwater-unsaturated zone model (Modflow-NWT model with the Unsaturated Zone Flow (UZF) package).

  7. NOVEL APPROACH TO IMPROVE GEOCENTRIC TRANSLATION MODEL PERFORMANCE USING ARTIFICIAL NEURAL NETWORK TECHNOLOGY

    Directory of Open Access Journals (Sweden)

    Yao Yevenyo Ziggah

    Full Text Available Abstract: Geocentric translation model (GTM in recent times has not gained much popularity in coordinate transformation research due to its attainable accuracy. Accurate transformation of coordinate is a major goal and essential procedure for the solution of a number of important geodetic problems. Therefore, motivated by the successful application of Artificial Intelligence techniques in geodesy, this study developed, tested and compared a novel technique capable of improving the accuracy of GTM. First, GTM based on official parameters (OP and new parameters determined using the arithmetic mean (AM were applied to transform coordinate from global WGS84 datum to local Accra datum. On the basis of the results, the new parameters (AM attained a maximum horizontal position error of 1.99 m compared to the 2.75 m attained by OP. In line with this, artificial neural network technology of backpropagation neural network (BPNN, radial basis function neural network (RBFNN and generalized regression neural network (GRNN were then used to compensate for the GTM generated errors based on AM parameters to obtain a new coordinate transformation model. The new implemented models offered significant improvement in the horizontal position error from 1.99 m to 0.93 m.

  8. Prediction of pork loin quality using online computer vision system and artificial intelligence model.

    Science.gov (United States)

    Sun, Xin; Young, Jennifer; Liu, Jeng-Hung; Newman, David

    2018-06-01

    The objective of this project was to develop a computer vision system (CVS) for objective measurement of pork loin under industry speed requirement. Color images of pork loin samples were acquired using a CVS. Subjective color and marbling scores were determined according to the National Pork Board standards by a trained evaluator. Instrument color measurement and crude fat percentage were used as control measurements. Image features (18 color features; 1 marbling feature; 88 texture features) were extracted from whole pork loin color images. Artificial intelligence prediction model (support vector machine) was established for pork color and marbling quality grades. The results showed that CVS with support vector machine modeling reached the highest prediction accuracy of 92.5% for measured pork color score and 75.0% for measured pork marbling score. This research shows that the proposed artificial intelligence prediction model with CVS can provide an effective tool for predicting color and marbling in the pork industry at online speeds. Copyright © 2018 Elsevier Ltd. All rights reserved.

  9. How ``Natural'' are inland wetlands? an example from the trail wood audubon sanctuary in Connecticut, USA

    Science.gov (United States)

    Thorson, Robert M.; Harris, Sandra L.

    1991-09-01

    We examined the geology of a small inland wetland in Hampton, Connecticut to determine its postglacial history and to assess the severity of human impact at this remote wooded site. Using stratigraphic evidence, we dernonstrate that the present wetland was created when sediment pollution from a 19th-century railroad filled a preexisting artificial reservoir, and that the prehistoric wetland was a narrow drainage swale along Hampton Brook. This same, severely impacted wetland was interpreted by the Pulitzer Prize-winning naturalist Edwin Way Teale as a beautiful wilderness area of particular interest. These conflicting perceptions indicate that artificial wetlands can be naturally mitigated in less than a century of healing, even in the absence of deliberate management. We also point out that the “wilderness” value of the Teale wetland was in the eye of the beholder and that unseen human impacts may have improved the aesthetic experience.

  10. Analysis on evaluation ability of nonlinear safety assessment model of coal mines based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    SHI Shi-liang; LIU Hai-bo; LIU Ai-hua

    2004-01-01

    Based on the integration analysis of goods and shortcomings of various methods used in safety assessment of coal mines, combining nonlinear feature of mine safety sub-system, this paper establishes the neural network assessment model of mine safety, analyzes the ability of artificial neural network to evaluate mine safety state, and lays the theoretical foundation of artificial neural network using in the systematic optimization of mine safety assessment and getting reasonable accurate safety assessment result.

  11. Artificial cell mimics as simplified models for the study of cell biology.

    Science.gov (United States)

    Salehi-Reyhani, Ali; Ces, Oscar; Elani, Yuval

    2017-07-01

    Living cells are hugely complex chemical systems composed of a milieu of distinct chemical species (including DNA, proteins, lipids, and metabolites) interconnected with one another through a vast web of interactions: this complexity renders the study of cell biology in a quantitative and systematic manner a difficult task. There has been an increasing drive towards the utilization of artificial cells as cell mimics to alleviate this, a development that has been aided by recent advances in artificial cell construction. Cell mimics are simplified cell-like structures, composed from the bottom-up with precisely defined and tunable compositions. They allow specific facets of cell biology to be studied in isolation, in a simplified environment where control of variables can be achieved without interference from a living and responsive cell. This mini-review outlines the core principles of this approach and surveys recent key investigations that use cell mimics to address a wide range of biological questions. It will also place the field in the context of emerging trends, discuss the associated limitations, and outline future directions of the field. Impact statement Recent years have seen an increasing drive to construct cell mimics and use them as simplified experimental models to replicate and understand biological phenomena in a well-defined and controlled system. By summarizing the advances in this burgeoning field, and using case studies as a basis for discussion on the limitations and future directions of this approach, it is hoped that this minireview will spur others in the experimental biology community to use artificial cells as simplified models with which to probe biological systems.

  12. Ecosystem Model Performance at Wetlands: Results from the North American Carbon Program Site Synthesis

    Science.gov (United States)

    Sulman, B. N.; Desai, A. R.; Schroeder, N. M.; NACP Site Synthesis Participants

    2011-12-01

    Northern peatlands contain a significant fraction of the global carbon pool, and their responses to hydrological change are likely to be important factors in future carbon cycle-climate feedbacks. Global-scale carbon cycle modeling studies typically use general ecosystem models with coarse spatial resolution, often without peatland-specific processes. Here, seven ecosystem models were used to simulate CO2 fluxes at three field sites in Canada and the northern United States, including two nutrient-rich fens and one nutrient-poor, sphagnum-dominated bog, from 2002-2006. Flux residuals (simulated - observed) were positively correlated with measured water table for both gross ecosystem productivity (GEP) and ecosystem respiration (ER) at the two fen sites for all models, and were positively correlated with water table at the bog site for the majority of models. Modeled diurnal cycles at fen sites agreed well with eddy covariance measurements overall. Eddy covariance GEP and ER were higher during dry periods than during wet periods, while model results predicted either the opposite relationship or no significant difference. At the bog site, eddy covariance GEP had no significant dependence on water table, while models predicted higher GEP during wet periods. All models significantly over-estimated GEP at the bog site, and all but one over-estimated ER at the bog site. Carbon cycle models in peatland-rich regions could be improved by incorporating better models or measurements of hydrology and by inhibiting GEP and ER rates under saturated conditions. Bogs and fens likely require distinct treatments in ecosystem models due to differences in nutrients, peat properties, and plant communities.

  13. Modeling of Aircraft Deicing Fluid Induced Biochemical Oxygen Demand in Subsurface-Flow Constructed Treatment Wetlands

    Science.gov (United States)

    2009-03-01

    Jukka A. Rintala, Christof Holliger, and Alla N. Nozhevnikova. “Evaluation of Kinetic Coefficients Using Intergrated Monod and Haldane Models for...Rousseau, Diederik P. L., Peter A Vanrolleghem, and Niels De Pauw. “Model-Based Design of Horizontal Subsurface Flow constructed Treatment

  14. Temperature-based modeling of reference evapotranspiration using several artificial intelligence models: application of different modeling scenarios

    Science.gov (United States)

    Sanikhani, Hadi; Kisi, Ozgur; Maroufpoor, Eisa; Yaseen, Zaher Mundher

    2018-02-01

    The establishment of an accurate computational model for predicting reference evapotranspiration (ET0) process is highly essential for several agricultural and hydrological applications, especially for the rural water resource systems, water use allocations, utilization and demand assessments, and the management of irrigation systems. In this research, six artificial intelligence (AI) models were investigated for modeling ET0 using a small number of climatic data generated from the minimum and maximum temperatures of the air and extraterrestrial radiation. The investigated models were multilayer perceptron (MLP), generalized regression neural networks (GRNN), radial basis neural networks (RBNN), integrated adaptive neuro-fuzzy inference systems with grid partitioning and subtractive clustering (ANFIS-GP and ANFIS-SC), and gene expression programming (GEP). The implemented monthly time scale data set was collected at the Antalya and Isparta stations which are located in the Mediterranean Region of Turkey. The Hargreaves-Samani (HS) equation and its calibrated version (CHS) were used to perform a verification analysis of the established AI models. The accuracy of validation was focused on multiple quantitative metrics, including root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R 2), coefficient of residual mass (CRM), and Nash-Sutcliffe efficiency coefficient (NS). The results of the conducted models were highly practical and reliable for the investigated case studies. At the Antalya station, the performance of the GEP and GRNN models was better than the other investigated models, while the performance of the RBNN and ANFIS-SC models was best compared to the other models at the Isparta station. Except for the MLP model, all the other investigated models presented a better performance accuracy compared to the HS and CHS empirical models when applied in a cross-station scenario. A cross-station scenario examination implies the

  15. Artificial intelligence-based computer modeling tools for controlling slag foaming in electric arc furnaces

    Science.gov (United States)

    Wilson, Eric Lee

    Due to increased competition in a world economy, steel companies are currently interested in developing techniques that will allow for the improvement of the steelmaking process, either by increasing output efficiency or by improving the quality of their product, or both. Slag foaming is one practice that has been shown to contribute to both these goals. However, slag foaming is highly dynamic and difficult to model or control. This dissertation describes an effort to use artificial intelligence-based tools (genetic algorithms, fuzzy logic, and neural networks) to both model and control the slag foaming process. Specifically, a neural network is trained and tested on slag foaming data provided by a steel plant. This neural network model is then controlled by a fuzzy logic controller, which in turn is optimized by a genetic algorithm. This tuned controller is then installed at a steel plant and given control be a more efficient slag foaming controller than what was previously used by the steel plant.

  16. Artificial neural network for modeling the extraction of aromatic hydrocarbons from lube oil cuts

    Energy Technology Data Exchange (ETDEWEB)

    Mehrkesh, A.H.; Hajimirzaee, S. [Islamic Azad University, Majlesi Branch, Isfahan (Iran, Islamic Republic of); Hatamipour, M.S.; Tavakoli, T. [Department of Chemical Engineering, University of Isfahan, Isfahan (Iran, Islamic Republic of)

    2011-03-15

    An artificial neural network (ANN) approach was used to obtain a simulation model to predict the rotating disc contactor (RDC) performance during the extraction of aromatic hydrocarbons from lube oil cuts, to produce a lubricating base oil using furfural as solvent. The field data used for training the ANN model was obtained from a lubricating oil production company. The input parameters of the ANN model were the volumetric flow rates of feed and solvent, the temperatures of feed and solvent, and the disc rotation rate. The output parameters were the volumetric flow rate of the raffinate phase and the extraction yield. In this study, a feed-forward multi-layer perceptron neural network was successfully used to demonstrate the complex relationship between the mentioned input and output parameters. (Copyright copyright 2011 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

  17. Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS.

    Science.gov (United States)

    Djurovic, Nevenka; Domazet, Milka; Stricevic, Ruzica; Pocuca, Vesna; Spalevic, Velibor; Pivic, Radmila; Gregoric, Enika; Domazet, Uros

    2015-01-01

    Water table forecasting plays an important role in the management of groundwater resources in agricultural regions where there are drainage systems in river valleys. The results presented in this paper pertain to an area along the left bank of the Danube River, in the Province of Vojvodina, which is the northern part of Serbia. Two soft computing techniques were used in this research: an adaptive neurofuzzy inference system (ANFIS) and an artificial neural network (ANN) model for one-month water table forecasts at several wells located at different distances from the river. The results suggest that both these techniques represent useful tools for modeling hydrological processes in agriculture, with similar computing and memory capabilities, such that they constitute an exceptionally good numerical framework for generating high-quality models.

  18. Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Aminmohammad Saberian

    2014-01-01

    Full Text Available This paper presents a solar power modelling method using artificial neural networks (ANNs. Two neural network structures, namely, general regression neural network (GRNN feedforward back propagation (FFBP, have been used to model a photovoltaic panel output power and approximate the generated power. Both neural networks have four inputs and one output. The inputs are maximum temperature, minimum temperature, mean temperature, and irradiance; the output is the power. The data used in this paper started from January 1, 2006, until December 31, 2010. The five years of data were split into two parts: 2006–2008 and 2009-2010; the first part was used for training and the second part was used for testing the neural networks. A mathematical equation is used to estimate the generated power. At the end, both of these networks have shown good modelling performance; however, FFBP has shown a better performance comparing with GRNN.

  19. Analysis of multicriteria models application for selection of an optimal artificial lift method in oil production

    Directory of Open Access Journals (Sweden)

    Crnogorac Miroslav P.

    2016-01-01

    Full Text Available In the world today for the exploitation of oil reservoirs by artificial lift methods are applied different types of deep pumps (piston, centrifugal, screw, hydraulic, water jet pumps and gas lift (continuous, intermittent and plunger. Maximum values of oil production achieved by these exploitation methods are significantly different. In order to select the optimal exploitation method of oil well, the multicriteria analysis models are used. In this paper is presented an analysis of the multicriteria model's application known as VIKOR, TOPSIS, ELECTRE, AHP and PROMETHEE for selection of optimal exploitation method for typical oil well at Serbian exploration area. Ranking results of applicability of the deep piston pumps, hydraulic pumps, screw pumps, gas lift method and electric submersible centrifugal pumps, indicated that in the all above multicriteria models except in PROMETHEE, the optimal method of exploitation are deep piston pumps and gas lift.

  20. ARTIFICIAL NEURAL NETWORK AND FUZZY LOGIC CONTROLLER FOR GTAW MODELING AND CONTROL

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    An artificial neural network(ANN) and a self-adjusting fuzzy logic controller(FLC) for modeling and control of gas tungsten arc welding(GTAW) process are presented. The discussion is mainly focused on the modeling and control of the weld pool depth with ANN and the intelligent control for weld seam tracking with FLC. The proposed neural network can produce highly complex nonlinear multi-variable model of the GTAW process that offers the accurate prediction of welding penetration depth. A self-adjusting fuzzy controller used for seam tracking adjusts the control parameters on-line automatically according to the tracking errors so that the torch position can be controlled accurately.

  1. Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study.

    Science.gov (United States)

    Li, Qiongge; Chan, Maria F

    2017-01-01

    Over half of cancer patients receive radiotherapy (RT) as partial or full cancer treatment. Daily quality assurance (QA) of RT in cancer treatment closely monitors the performance of the medical linear accelerator (Linac) and is critical for continuous improvement of patient safety and quality of care. Cumulative longitudinal QA measurements are valuable for understanding the behavior of the Linac and allow physicists to identify trends in the output and take preventive actions. In this study, artificial neural networks (ANNs) and autoregressive moving average (ARMA) time-series prediction modeling techniques were both applied to 5-year daily Linac QA data. Verification tests and other evaluations were then performed for all models. Preliminary results showed that ANN time-series predictive modeling has more advantages over ARMA techniques for accurate and effective applicability in the dosimetry and QA field. © 2016 New York Academy of Sciences.

  2. Application of artificial neural networks for response surface modelling in HPLC method development

    Directory of Open Access Journals (Sweden)

    Mohamed A. Korany

    2012-01-01

    Full Text Available This paper discusses the usefulness of artificial neural networks (ANNs for response surface modelling in HPLC method development. In this study, the combined effect of pH and mobile phase composition on the reversed-phase liquid chromatographic behaviour of a mixture of salbutamol (SAL and guaiphenesin (GUA, combination I, and a mixture of ascorbic acid (ASC, paracetamol (PAR and guaiphenesin (GUA, combination II, was investigated. The results were compared with those produced using multiple regression (REG analysis. To examine the respective predictive power of the regression model and the neural network model, experimental and predicted response factor values, mean of squares error (MSE, average error percentage (Er%, and coefficients of correlation (r were compared. It was clear that the best networks were able to predict the experimental responses more accurately than the multiple regression analysis.

  3. Parameter identification of piezoelectric hysteresis model based on improved artificial bee colony algorithm

    Science.gov (United States)

    Wang, Geng; Zhou, Kexin; Zhang, Yeming

    2018-04-01

    The widely used Bouc-Wen hysteresis model can be utilized to accurately simulate the voltage-displacement curves of piezoelectric actuators. In order to identify the unknown parameters of the Bouc-Wen model, an improved artificial bee colony (IABC) algorithm is proposed in this paper. A guiding strategy for searching the current optimal position of the food source is proposed in the method, which can help balance the local search ability and global exploitation capability. And the formula for the scout bees to search for the food source is modified to increase the convergence speed. Some experiments were conducted to verify the effectiveness of the IABC algorithm. The results show that the identified hysteresis model agreed well with the actual actuator response. Moreover, the identification results were compared with the standard particle swarm optimization (PSO) method, and it can be seen that the search performance in convergence rate of the IABC algorithm is better than that of the standard PSO method.

  4. Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS

    Directory of Open Access Journals (Sweden)

    Nevenka Djurovic

    2015-01-01

    Full Text Available Water table forecasting plays an important role in the management of groundwater resources in agricultural regions where there are drainage systems in river valleys. The results presented in this paper pertain to an area along the left bank of the Danube River, in the Province of Vojvodina, which is the northern part of Serbia. Two soft computing techniques were used in this research: an adaptive neurofuzzy inference system (ANFIS and an artificial neural network (ANN model for one-month water table forecasts at several wells located at different distances from the river. The results suggest that both these techniques represent useful tools for modeling hydrological processes in agriculture, with similar computing and memory capabilities, such that they constitute an exceptionally good numerical framework for generating high-quality models.

  5. artificial neural network model for low strength rc beam shear capacity

    African Journals Online (AJOL)

    User

    RESEARCH PAPER. Keywords: Shear strength, reinforced concrete, Artificial Neural Network, design equations ... searchers using artificial intelligence to im- prove on theoretical ...... benefit to humanity or a waste of time?” The. Structural ...

  6. Setup of a Parameterized FE Model for the Die Roll Prediction in Fine Blanking using Artificial Neural Networks

    Science.gov (United States)

    Stanke, J.; Trauth, D.; Feuerhack, A.; Klocke, F.

    2017-09-01

    Die roll is a morphological feature of fine blanked sheared edges. The die roll reduces the functional part of the sheared edge. To compensate for the die roll thicker sheet metal strips and secondary machining must be used. However, in order to avoid this, the influence of various fine blanking process parameters on the die roll has been experimentally and numerically studied, but there is still a lack of knowledge on the effects of some factors and especially factor interactions on the die roll. Recent changes in the field of artificial intelligence motivate the hybrid use of the finite element method and artificial neural networks to account for these non-considered parameters. Therefore, a set of simulations using a validated finite element model of fine blanking is firstly used to train an artificial neural network. Then the artificial neural network is trained with thousands of experimental trials. Thus, the objective of this contribution is to develop an artificial neural network that reliably predicts the die roll. Therefore, in this contribution, the setup of a fully parameterized 2D FE model is presented that will be used for batch training of an artificial neural network. The FE model enables an automatic variation of the edge radii of blank punch and die plate, the counter and blank holder force, the sheet metal thickness and part diameter, V-ring height and position, cutting velocity as well as material parameters covered by the Hensel-Spittel model for 16MnCr5 (1.7131, AISI/SAE 5115). The FE model is validated using experimental trails. The results of this contribution is a FE model suitable to perform 9.623 simulations and to pass the simulated die roll width and height automatically to an artificial neural network.

  7. Artificial snowmaking possibilities and climate change based on regional climate modeling in the Southern Black Forest

    Energy Technology Data Exchange (ETDEWEB)

    Schmidt, Philipp; Matzarakis, Andreas [Freiburg Univ. (Germany). Meteorological Inst.; Steiger, Robert [alpS - Centre for Climate Change Adaptation Technologies, Innsbruck (Austria)

    2012-04-15

    Winter sport, especially ski tourism - is one of those sectors of tourism that will be affected by climate change. Ski resorts across the Alps and in the adjacent low mountain ranges react to warm winter seasons by investing in artificial snowmaking. But snowmaking in warm winter seasons is fraught with risk, because sufficiently low air temperature will become less frequent in the future. The present study deals with the ski resort Feldberg, which has 14 ski lifts and 16 ski slopes which is the biggest ski resort in the German Federal state Baden-Wuerttemberg. The impact of climate change in this region is extraordinary important because winter tourism is the main source of revenue for the whole area around the ski resort. The study area is in altitudinal range of 850 to 1450 meters above sea level. At the moment, it is possible to supply one third of the whole area with artificial snow, but there is plan for artificial snowmaking of the whole Feldberg area by the year 2020. Based on this, more detailed investigations of season length and the needed volume of produced snow are necessary. A ski season simulation model (SkiSim 2.0) was applied in order to assess potential impacts of climate change on the Feldberg ski area for the A1B and B1 emission scenarios based on the ECHAM5 GCM downscaled by the REMO RCM. SkiSim 2.0 calculates daily snow depth (natural and technically produced snow) and the required amount of artificial snow for 100 m altitudinal bands. Analysing the development of the number of potential skiing days, it can be assessed whether ski operation is cost covering or not. Model results of the study show a more pronounced and rapid shortening of the ski season in the lower ranges until the year 2100 in each climate scenario. In both the A1B and B1 scenario runs of REMO, a cost-covering ski season of 100 days cannot be guaranteed in every altitudinal range even if snowmaking is considered. In this context, the obtained high-resolution snow data can

  8. Artificial neural network model for prediction of safety performance indicators goals in nuclear plants

    Energy Technology Data Exchange (ETDEWEB)

    Souto, Kelling C.; Nunes, Wallace W. [Instituto Federal de Educacao, Ciencia e Tecnologia do Rio de Janeiro, Nilopolis, RJ (Brazil). Lab. de Aplicacoes Computacionais; Machado, Marcelo D., E-mail: dornemd@eletronuclear.gov.b [ELETROBRAS Termonuclear S.A. (ELETRONUCLEAR), Rio de Janeiro, RJ (Brazil). Gerencia de Combustivel Nuclear - GCN.T

    2011-07-01

    Safety performance indicators have been developed to provide a quantitative indication of the performance and safety in various industry sectors. These indexes can provide assess to aspects ranging from production, design, and human performance up to management issues in accordance with policy, objectives and goals of the company. The use of safety performance indicators in nuclear power plants around the world is a reality. However, it is necessary to periodically set goal values. Such goals are targets relating to each of the indicators to be achieved by the plant over a predetermined period of operation. The current process of defining these goals is carried out by experts in a subjective way, based on actual data from the plant, and comparison with global indices. Artificial neural networks are computational techniques that present a mathematical model inspired by the neural structure of intelligent organisms that acquire knowledge through experience. This paper proposes an artificial neural network model aimed at predicting values of goals to be used in the evaluation of safety performance indicators for nuclear power plants. (author)

  9. Development of Artificial Neural Network Model for Diesel Fuel Properties Prediction using Vibrational Spectroscopy.

    Science.gov (United States)

    Bolanča, Tomislav; Marinović, Slavica; Ukić, Sime; Jukić, Ante; Rukavina, Vinko

    2012-06-01

    This paper describes development of artificial neural network models which can be used to correlate and predict diesel fuel properties from several FTIR-ATR absorbances and Raman intensities as input variables. Multilayer feed forward and radial basis function neural networks have been used to rapid and simultaneous prediction of cetane number, cetane index, density, viscosity, distillation temperatures at 10% (T10), 50% (T50) and 90% (T90) recovery, contents of total aromatics and polycyclic aromatic hydrocarbons of commercial diesel fuels. In this study two-phase training procedures for multilayer feed forward networks were applied. While first phase training algorithm was constantly the back propagation one, two second phase training algorithms were varied and compared, namely: conjugate gradient and quasi Newton. In case of radial basis function network, radial layer was trained using K-means radial assignment algorithm and three different radial spread algorithms: explicit, isotropic and K-nearest neighbour. The number of hidden layer neurons and experimental data points used for the training set have been optimized for both neural networks in order to insure good predictive ability by reducing unnecessary experimental work. This work shows that developed artificial neural network models can determine main properties of diesel fuels simultaneously based on a single and fast IR or Raman measurement.

  10. Artificial neural network model for prediction of safety performance indicators goals in nuclear plants

    International Nuclear Information System (INIS)

    Souto, Kelling C.; Nunes, Wallace W.; Machado, Marcelo D.

    2011-01-01

    Safety performance indicators have been developed to provide a quantitative indication of the performance and safety in various industry sectors. These indexes can provide assess to aspects ranging from production, design, and human performance up to management issues in accordance with policy, objectives and goals of the company. The use of safety performance indicators in nuclear power plants around the world is a reality. However, it is necessary to periodically set goal values. Such goals are targets relating to each of the indicators to be achieved by the plant over a predetermined period of operation. The current process of defining these goals is carried out by experts in a subjective way, based on actual data from the plant, and comparison with global indices. Artificial neural networks are computational techniques that present a mathematical model inspired by the neural structure of intelligent organisms that acquire knowledge through experience. This paper proposes an artificial neural network model aimed at predicting values of goals to be used in the evaluation of safety performance indicators for nuclear power plants. (author)

  11. Study on Fault Diagnostics of a Turboprop Engine Using Inverse Performance Model and Artificial Intelligent Methods

    Science.gov (United States)

    Kong, Changduk; Lim, Semyeong

    2011-12-01

    Recently, the health monitoring system of major gas path components of gas turbine uses mostly the model based method like the Gas Path Analysis (GPA). This method is to find quantity changes of component performance characteristic parameters such as isentropic efficiency and mass flow parameter by comparing between measured engine performance parameters such as temperatures, pressures, rotational speeds, fuel consumption, etc. and clean engine performance parameters without any engine faults which are calculated by the base engine performance model. Currently, the expert engine diagnostic systems using the artificial intelligent methods such as Neural Networks (NNs), Fuzzy Logic and Genetic Algorithms (GAs) have been studied to improve the model based method. Among them the NNs are mostly used to the engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base if there are large amount of learning data. In addition, it has a very complex structure for finding effectively single type faults or multiple type faults of gas path components. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measured performance data, and proposes a fault diagnostic system using the base engine performance model and the artificial intelligent methods such as Fuzzy logic and Neural Network. The proposed diagnostic system isolates firstly the faulted components using Fuzzy Logic, then quantifies faults of the identified components using the NN leaned by fault learning data base, which are obtained from the developed base performance model. In leaning the NN, the Feed Forward Back Propagation (FFBP) method is used. Finally, it is verified through several test examples that the component faults implanted arbitrarily in the engine are well isolated and quantified by the proposed diagnostic system.

  12. Assessing the Ecological Relevance of Organic Discharge Limits for Constructed Wetlands by Means of a Model-Based Analysis

    Directory of Open Access Journals (Sweden)

    Natalia Donoso

    2018-01-01

    Full Text Available Polder watercourses within agricultural areas are affected by high chemical oxygen demand (COD and biological oxygen demand (BOD5 concentrations, due to intensive farming activities and runoff. Practical cases have shown that constructed wetlands (CWs are eco-friendly and cost-effective treatment systems which can reduce high levels of organic and nutrient pollution from agricultural discharges. However, accumulated recalcitrant organic matter, originated by in-situ sources or elements of CWs (i.e., plants or microbial detritus, limits the fulfilment of current COD discharge threshold. Thus, to evaluate its relevance regarding rivers ecosystem health preservation, we analysed the response of bio-indicators, the Multimetric Macroinvertebrate Index Flanders (MMIF and the occurrence of organic pollution sensitive taxa towards organic pollutants. For this purpose, statistical models were developed based on collected data in polder watercourses and CWs located in Flanders (Belgium. Results showed that, given the correlation between COD and BOD5, both parameters can be used to indicate the ecological and water quality conditions. However, the variability of the MMIF and the occurrence of sensitive species are explained better by BOD5, which captures a major part of their common effect. Whereas, recalcitrant COD and the interaction among other physico-chemical variables indicate a minor variability on the bio-indicators. Based on these outcomes we suggest a critical re-evaluation of current COD thresholds and moreover, consider other emerging technologies determining organic pollution levels, since this could support the feasibility of the implementation of CWs to tackle agricultural pollution.

  13. Exploring drivers of wetland hydrologic fluxes across parameters and space

    Science.gov (United States)

    Jones, C. N.; Cheng, F. Y.; Mclaughlin, D. L.; Basu, N. B.; Lang, M.; Alexander, L. C.

    2017-12-01

    Depressional wetlands provide diverse ecosystem services, ranging from critical habitat to the regulation of landscape hydrology. The latter is of particular interest, because while hydrologic connectivity between depressional wetlands and downstream waters has been a focus of both scientific research and policy, it remains difficult to quantify the mode, magnitude, and timing of this connectivity at varying spatial and temporary scales. To do so requires robust empirical and modeling tools that accurately represent surface and subsurface flowpaths between depressional wetlands and other landscape elements. Here, we utilize a parsimonious wetland hydrology model to explore drivers of wetland water fluxes in different archetypal wetland-rich landscapes. We validated the model using instrumented sites from regions that span North America: Prairie Pothole Region (south-central Canada), Delmarva Peninsula (Mid-Atlantic Coastal Plain), and Big Cypress Swamp (southern Florida). Then, using several national scale datasets (e.g., National Wetlands Inventory, USFWS; National Hydrography Dataset, USGS; Soil Survey Geographic Database, NRCS), we conducted a global sensitivity analysis to elucidate dominant drivers of simulated fluxes. Finally, we simulated and compared wetland hydrology in five contrasting landscapes dominated by depressional wetlands: prairie potholes, Carolina and Delmarva bays, pocosins, western vernal pools, and Texas coastal prairie wetlands. Results highlight specific drivers that vary across these regions. Largely, hydroclimatic variables (e.g., PET/P ratios) controlled the timing and magnitude of wetland connectivity, whereas both wetland morphology (e.g., storage capacity and watershed size) and soil characteristics (e.g., ksat and confining layer depth) controlled the duration and mode (surface vs. subsurface) of wetland connectivity. Improved understanding of the drivers of wetland hydrologic connectivity supports enhanced, region

  14. A Preliminary System Dynamics Model of a Constructed Wetland for the Mitigation of Metals in USAF Storm Water

    Science.gov (United States)

    1994-09-01

    Cultivated Land Grassland 0 500 1000 1500 2000 (Technology Review, 1994:23) The Scientific American article continues with the benefits provided by...34 Ecotoxicology and Wetland Ecosystems: Current Understanding and Future Needs," Environmental Toxicology and Chemistry. Volume 12, pp 2209-2224, 1993

  15. 3D artificial bones for bone repair prepared by computed tomography-guided fused deposition modeling for bone repair.

    Science.gov (United States)

    Xu, Ning; Ye, Xiaojian; Wei, Daixu; Zhong, Jian; Chen, Yuyun; Xu, Guohua; He, Dannong

    2014-09-10

    The medical community has expressed significant interest in the development of new types of artificial bones that mimic natural bones. In this study, computed tomography (CT)-guided fused deposition modeling (FDM) was employed to fabricate polycaprolactone (PCL)/hydroxyapatite (HA) and PCL 3D artificial bones to mimic natural goat femurs. The in vitro mechanical properties, in vitro cell biocompatibility, and in vivo performance of the artificial bones in a long load-bearing goat femur bone segmental defect model were studied. All of the results indicate that CT-guided FDM is a simple, convenient, relatively low-cost method that is suitable for fabricating natural bonelike artificial bones. Moreover, PCL/HA 3D artificial bones prepared by CT-guided FDM have more close mechanics to natural bone, good in vitro cell biocompatibility, biodegradation ability, and appropriate in vivo new bone formation ability. Therefore, PCL/HA 3D artificial bones could be potentially be of use in the treatment of patients with clinical bone defects.

  16. Improving Gastric Cancer Outcome Prediction Using Single Time-Point Artificial Neural Network Models

    Science.gov (United States)

    Nilsaz-Dezfouli, Hamid; Abu-Bakar, Mohd Rizam; Arasan, Jayanthi; Adam, Mohd Bakri; Pourhoseingholi, Mohamad Amin

    2017-01-01

    In cancer studies, the prediction of cancer outcome based on a set of prognostic variables has been a long-standing topic of interest. Current statistical methods for survival analysis offer the possibility of modelling cancer survivability but require unrealistic assumptions about the survival time distribution or proportionality of hazard. Therefore, attention must be paid in developing nonlinear models with less restrictive assumptions. Artificial neural network (ANN) models are primarily useful in prediction when nonlinear approaches are required to sift through the plethora of available information. The applications of ANN models for prognostic and diagnostic classification in medicine have attracted a lot of interest. The applications of ANN models in modelling the survival of patients with gastric cancer have been discussed in some studies without completely considering the censored data. This study proposes an ANN model for predicting gastric cancer survivability, considering the censored data. Five separate single time-point ANN models were developed to predict the outcome of patients after 1, 2, 3, 4, and 5 years. The performance of ANN model in predicting the probabilities of death is consistently high for all time points according to the accuracy and the area under the receiver operating characteristic curve. PMID:28469384

  17. Spatial Disaggregation of Areal Rainfall Using Two Different Artificial Neural Networks Models

    Directory of Open Access Journals (Sweden)

    Sungwon Kim

    2015-06-01

    Full Text Available The objective of this study is to develop artificial neural network (ANN models, including multilayer perceptron (MLP and Kohonen self-organizing feature map (KSOFM, for spatial disaggregation of areal rainfall in the Wi-stream catchment, an International Hydrological Program (IHP representative catchment, in South Korea. A three-layer MLP model, using three training algorithms, was used to estimate areal rainfall. The Levenberg–Marquardt training algorithm was found to be more sensitive to the number of hidden nodes than were the conjugate gradient and quickprop training algorithms using the MLP model. Results showed that the networks structures of 11-5-1 (conjugate gradient and quickprop and 11-3-1 (Levenberg-Marquardt were the best for estimating areal rainfall using the MLP model. The networks structures of 1-5-11 (conjugate gradient and quickprop and 1-3-11 (Levenberg–Marquardt, which are the inverse networks for estimating areal rainfall using the best MLP model, were identified for spatial disaggregation of areal rainfall using the MLP model. The KSOFM model was compared with the MLP model for spatial disaggregation of areal rainfall. The MLP and KSOFM models could disaggregate areal rainfall into individual point rainfall with spatial concepts.

  18. Freshwater Wetlands: A Citizen's Primer.

    Science.gov (United States)

    Catskill Center for Conservation and Development, Inc., Hobart, NY.

    The purpose of this "primer" for the general public is to describe the general characteristics of wetlands and how wetland alteration adversely affects the well-being of humans. Particular emphasis is placed on wetlands in New York State and the northeast. Topics discussed include wetland values, destruction of wetlands, the costs of…

  19. Application of artificial neural networks in hydrological modeling: A case study of runoff simulation of a Himalayan glacier basin

    Science.gov (United States)

    Buch, A. M.; Narain, A.; Pandey, P. C.

    1994-01-01

    The simulation of runoff from a Himalayan Glacier basin using an Artificial Neural Network (ANN) is presented. The performance of the ANN model is found to be superior to the Energy Balance Model and the Multiple Regression model. The RMS Error is used as the figure of merit for judging the performance of the three models, and the RMS Error for the ANN model is the latest of the three models. The ANN is faster in learning and exhibits excellent system generalization characteristics.

  20. An experimental artificial-neural-network-based modeling of magneto-rheological fluid dampers

    International Nuclear Information System (INIS)

    Tudón-Martínez, J C; Lozoya-Santos, J J; Morales-Menendez, R; Ramirez-Mendoza, R A

    2012-01-01

    A static model for a magneto-rheological (MR) damper based on artificial neural networks (ANNs) is proposed, and an intensive and experimental study is presented for designing the ANN structure. The ANN model does not require time delays in the input vector. Besides the electric current signal, only one additional sensor is used to achieve a reliable MR damper structure. The model is experimentally validated with two commercial MR dampers of different characteristics: MR 1 damper with continuous actuation and MR 2 damper with two levels of actuation. The error to signal ratio (ESR) index is used to measure the model accuracy; for both MR dampers, an average value of 6.03% of total error is obtained from different experiments, which are designed to explore the nonlinearities of the MR phenomenon at different frequencies by including the impact of the electric current fluctuations. The proposed ANN model is compared with other well known parametric models; the qualitative and quantitative comparison among the models highlights the advantages of the ANN for representing a commercial MR damper. The ESR index was reduced by the ANN-based model by up to 29% with respect to the parametric models for the MR 1 damper and up to 40% for the MR 2 damper. The force–velocity diagram is used to compare the modeling properties of each approach: (1) the Bingham model cannot describe the hysteresis of both MR dampers and the distribution function of the modeled force varies from the experimental data, (2) the algebraic models have complications in representing the nonlinear behavior of the asymmetric damper (MR 2 ) and, (3) the ANN-based MR damper can model the nonlinearities of both MR dampers and presents good scalability; the accuracy of the results supports the use of this model for the validation of semi-active suspension control systems for a vehicle, by using nonlinear simulations. (paper)

  1. Changes of hydrological environment and their influences on coastal wetlands in the southern Laizhou Bay, China.

    Science.gov (United States)

    Zhang, Xuliang; Zhang, Yuanzhi; Sun, Hongxia; Xia, Dongxing

    2006-08-01

    The structure and function of the coastal wetland ecosystem in the southern Laizhou Bay have been changed greatly and influenced by regional hydrological changes. The coastal wetlands have degraded significantly during the latest 30 years due to successive drought, decreasing of runoff, pollution, underground saline water intrusion, and aggravating marine disasters such as storm tides and sea level rising. Most archaic lakes have vanished, while artificial wetlands have been extending since natural coastal wetlands replaced by salt areas and ponds of shrimps and crabs. The pollution of sediments in inter-tidal wetlands and the pollution of water quality in sub-tidal wetlands are getting worse and therefore "red tides" happen more often than before. The biodiversity in the study area has been decreased. Further studies are still needed to protect the degraded coastal wetlands in the area.

  2. Urban Growth Modelling with Artificial Neural Network and Logistic Regression. Case Study: Sanandaj City, Iran

    Directory of Open Access Journals (Sweden)

    SASSAN MOHAMMADY

    2013-01-01

    Full Text Available Cities have shown remarkable growth due to attraction, economic, social and facilities centralization in the past few decades. Population and urban expansion especially in developing countries, led to lack of resources, land use change from appropriate agricultural land to urban land use and marginalization. Under these circumstances, land use activity is a major issue and challenge for town and country planners. Different approaches have been attempted in urban expansion modelling. Artificial Neural network (ANN models are among knowledge-based models which have been used for urban growth modelling. ANNs are powerful tools that use a machine learning approach to quantify and model complex behaviour and patterns. In this research, ANN and logistic regression have been employed for interpreting urban growth modelling. Our case study is Sanandaj city and we used Landsat TM and ETM+ imageries acquired at 2000 and 2006. The dataset used includes distance to main roads, distance to the residence region, elevation, slope, and distance to green space. Percent Area Match (PAM obtained from modelling of these changes with ANN is equal to 90.47% and the accuracy achieved for urban growth modelling with Logistic Regression (LR is equal to 88.91%. Percent Correct Match (PCM and Figure of Merit for ANN method were 91.33% and 59.07% and then for LR were 90.84% and 57.07%, respectively.

  3. Predicting adsorptive removal of chlorophenol from aqueous solution using artificial intelligence based modeling approaches.

    Science.gov (United States)

    Singh, Kunwar P; Gupta, Shikha; Ojha, Priyanka; Rai, Premanjali

    2013-04-01

    The research aims to develop artificial intelligence (AI)-based model to predict the adsorptive removal of 2-chlorophenol (CP) in aqueous solution by coconut shell carbon (CSC) using four operational variables (pH of solution, adsorbate concentration, temperature, and contact time), and to investigate their effects on the adsorption process. Accordingly, based on a factorial design, 640 batch experiments were conducted. Nonlinearities in experimental data were checked using Brock-Dechert-Scheimkman (BDS) statistics. Five nonlinear models were constructed to predict the adsorptive removal of CP in aqueous solution by CSC using four variables as input. Performances of the constructed models were evaluated and compared using statistical criteria. BDS statistics revealed strong nonlinearity in experimental data. Performance of all the models constructed here was satisfactory. Radial basis function network (RBFN) and multilayer perceptron network (MLPN) models performed better than generalized regression neural network, support vector machines, and gene expression programming models. Sensitivity analysis revealed that the contact time had highest effect on adsorption followed by the solution pH, temperature, and CP concentration. The study concluded that all the models constructed here were capable of capturing the nonlinearity in data. A better generalization and predictive performance of RBFN and MLPN models suggested that these can be used to predict the adsorption of CP in aqueous solution using CSC.

  4. Application of artificial intelligence (AI) concepts to the development of space flight parts approval model

    Science.gov (United States)

    Krishnan, Govindarajapuram Subramaniam

    1997-12-01

    The National Aeronautics & Space Administration (NASA), the European Space Agency (ESA), and the Canadian Space Agency (CSA) missions involve the performance of scientific experiments in Space. Instruments used in such experiments are fabricated using electronic parts such as microcircuits, inductors, capacitors, diodes, transistors, etc. For instruments to perform reliably the selection of commercial parts must be monitored and strictly controlled. The process used to achieve this goal is by a manual review and approval of every part used to build the instrument. The present system to select and approve parts for space applications is manual, inefficient, inconsistent, slow and tedious, and very costly. In this dissertation a computer based decision support model is developed for implementing this process using artificial intelligence concepts based on the current information (expert sources). Such a model would result in a greater consistency, accuracy, and timeliness of evaluation. This study presents the methodology of development and features of the model, and the analysis of the data pertaining to the performance of the model in the field. The model was evaluated for three different part types by experts from three different space agencies. The results show that the model was more consistent than the manual evaluation for all part types considered. The study concludes with the cost and benefits analysis of implementing the models and shows that implementation of the model will result in significant cost savings. Other implementation details are highlighted.

  5. Artificial neural Network-Based modeling and monitoring of photovoltaic generator

    Directory of Open Access Journals (Sweden)

    H. MEKKI

    2015-03-01

    Full Text Available In this paper, an artificial neural network based-model (ANNBM is introduced for partial shading detection losses in photovoltaic (PV panel. A Multilayer Perceptron (MLP is used to estimate the electrical outputs (current and voltage of the photovoltaic module using the external meteorological data: solar irradiation G (W/m2 and the module temperature T (°C. Firstly, a database of the BP150SX photovoltaic module operating without any defect has been used to train the considered MLP. Subsequently, in the first case of this study, the developed model is used to estimate the output current and voltage of the PV module considering the partial shading effect. Results confirm the good ability of the ANNBM to detect the partial shading effect in the photovoltaic module with logical accuracy. The propos