WorldWideScience

Sample records for artificial wetland modelling

  1. Ecosystem Health and Comprehensive Ecological Benefit Assessment of an Artificial Wetland in Western Jilin Province

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    [Objective] This research aimed to assess the state of ecosystem health and comprehensive ecological benefit of an artificial wetland in western Jilin Province. [Method] To investigate the effects of reclaimed water from Yingtai Oil Production Plant on the wetland ecosystem, a comprehensive ecological assessment index of an artificial wetland in the west of Jilin Province was established to measure the ecological economic and social benefits. The quantitative evaluation on the ecosystem health and comprehen...

  2. 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.

  3. Simulation of the Effect of Artificial Water Transfer on Carbon Stock of Phragmites australis in the Baiyangdian Wetland, China

    Science.gov (United States)

    Chen, Xinyong; Wang, Fengyi; Li, Hongbo; Zhu, Jing; Lv, Xiaotong

    2017-01-01

    How to explain the effect of seasonal water transfer on the carbon stocks of Baiyangdian wetland is studied. The ecological model of the relationship between the carbon stocks and water depth fluctuation of the reed was established by using STELLA software. For the first time the Michaelis-Menten equation (1) introduced the relation function between the water depth and reed environmental carrying capacity, (2) introduced the concept of suitable growth water depth, and (3) simulated the variation rules of water and reed carbon stocks of artificial adjustment. The model could be used to carry out the research on the optimization design of the ecological service function of the damaged wetland.

  4. 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

  5. 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. .

  6. Research on Phosphorus Removal in Artificial Wetlands by Plants and Their Photosynthesis

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    Quan Quan

    Full Text Available ABSTRACT Urban rainfall runoff pollution has become a major reason for water eutrophication problem in the process of urbanization in China, while phosphorus is a significant restrictive factor that influences primary productivity of freshwater system. It's rather significant to conduct phosphorus control in waste water with engineering measures. This research, based on material balance research of phosphorus in artificial wetlands, HRT (hydraulic retention time and analysis of wetland plant photosynthesis and removal rate of phosphorus, simulates purification of phosphorus in urban runoff sewage by artificial wetland system. Experiment shows that removal rate of total phosphorus in urban runoff sewage by artificial wetland system reaches 42.23%-60.89%, and contribution rate in removal of phosphorus which is assimilated and absorbed by plants is 14.74%; contribution rate in removal of phosphorus which is accumulated and absorbed by substrates is 43.22%; contribution rate in removal of phosphorus which is absorbed by means like microorganisms is 2.93%. Pollutant absorption by substrates is a process of dynamic equilibrium. With extension of HRT, phosphorus removing effect of wetlands present an increasing and then decreasing tendency; Net photosynthetic rate and TP removal rate of canna and reed have significant positive correlation, and correlation coefficients are respectively 0.941(P<0.001 and 0.915(P<0.05. Substrates and plants are main pathways for phosphorus removal of artificial wetlands, covering 95% of the total removing effect.

  7. A STUDY ON WETLAND CLASSIFICATION MODEL OFREMOTE SENSING IN THE SANGJIANG PLAIN

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    The Sanjiang Plain, where nearly 20 kinds of wetlands exist now, is one of the largest wetlands distributed area of wetlands in China. To identify each of them and pick up them separately by means of automatic interpretation of remote sensing from TM Landsat images is extremely important. However, most of the types of wetlands can not be divided each other due to the similarity and the illegibility of the wetland spectrum shown in TM images. Special disposals to remote sensing images include the spectrurn enhancement of wetland information, the pseudo color composite of TM im ages of different bands and the algebra enhancement of TM images. By this way some kinds of wetlands such as Sparganium stoloniferum and Bolboschoenus maritimus can be identified. But in many cases, these methods are still insufficient because of the noise brought from the atmosphere transportation and so on. The physical features of wetlands reflecting the diversification of spectrum information of wetlands, which include the spatial-temporal characteristics of the wetlands distribution, the landscape differences of wetlands from season to season, the growing environment and the vertical structure of wetlands vegetation and so on, must be taken into consideration. Besides these, the artificial alteration to spatial structure of wetlands such as the exploitation of some types of them can be also used as important symbols of wetlands identification from remote sensing images. On the basis of the above geographics analysis, a set of wetlands classification models of remote sensing could be established, and many types of wetlands such as paddy-field, reed swamp, peat mire,meadow, CAREX marsh and paludification meadow and so on, will be distinguished consequently. All the ways of geographical analysis and model establishment will be given in detail in this article.

  8. Treating coal mine drainage with an artificial wetland. [USA - Ohio

    Energy Technology Data Exchange (ETDEWEB)

    Fennessy, M.S.; Mitsch, W.J. (Ohio State University Columbus, OH (USA). School of Natural Resources)

    A 0.22-ha constructed wetland dominated by Typha latofolia was evaluated for its ability to treat approximately 340 L/min of coal mine drainage from an underground seep in eastern Ohio. Loading of mine drainage to the wetland ranged from 15 to 35 cm/d. Conductivity, pH, manganese, and sulfate were little changed by the wetland. Iron decreased by 50 to 60%, with slightly higher decreases during the growing season. Comparisons are made to a volunteer Typha marsh receiving mine drainage where iron was found to decrease by approximately 89%. Design considerations of loading rates of created wetlands suggest that improved treatment of mine drainage is correlated with longer retention times and lower iron loading rates. Preliminary design criteria for construction of these types of Typha wetlands for removal of iron are suggested as 5 cm/d hydrologic loading and 2 to 40 g Fe/m{sup 2}.d for iron loading, depending on the treatment desired. 34 refs., 8 figs., 5 tabs.

  9. Degradation of benzotriazole and benzothiazole in treatment wetlands and by artificial sunlight.

    Science.gov (United States)

    Felis, Ewa; Sochacki, Adam; Magiera, Sylwia

    2016-11-01

    Laboratory-scale experiments were performed using unsaturated subsurface-flow treatment wetlands and artificial sunlight (with and without TiO2) to study the efficiency of benzotriazole and benzothiazole removal and possible integration of these treatment methods. Transformation products in the effluent from the treatment wetlands and the artificial sunlight reactor were identified by high performance liquid chromatography coupled with tandem mass spectrometry. The removal of benzothiazole in the vegetated treatment wetlands was 99.7%, whereas the removal of benzotriazole was 82.8%. The vegetation positively affected only the removal of benzothiazole. The major transformation products in the effluents from the treatment wetlands were methylated and hydroxylated derivatives of benzotriazole, and hydroxylated derivatives of benzothiazole. Hydroxylation was found to be the main process governing the transformation pathway for both compounds in the artificial sunlight experiment (with and without TiO2). Benzotriazole was not found to be susceptible to photodegradation in the absence of TiO2. The integration of the sunlight-induced processes (with TiO2) with subsurface-flow treatment wetlands caused further elimination of the compounds (42% for benzotriazole and 58% for benzothiazole). This was especially significant for the elimination of benzotriazole, because the removal of this compound was 96% in the coupled processes. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. Investigating the efficiency and kinetic coefficients of nutrient removal in the subsurface artificial wetland of Yazd wastewater treatment plant

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    Mahdi Farzadkia

    2015-01-01

    Full Text Available Background: Investigating the performance of naturally operated treatment plants may be due to the fact that they cannot be operated as desired, or that they should be modified to achieve good performance e.g. for nutrients removal. The advantage of kinetic coefficient determination is that the model can be adjusted to fit data and then used for analyzing alternatives to improve the process. This study investigates the efficiency of subsurface artificial wetland and determines its kinetic coefficients for nutrient removal. Methods: The present study investigated the kinetics of biological reactions that occurred in subsurface wetland to remove wastewater nutrient. Samples were taken from 3 locations of wetlands for 6 months. The nutrient content was determined through measuring Total Kjehldahl Nitrogen (TKN, ammonium, nitrate, and phosphate values. Results: Average levels for TKN, ammonium, nitrate, and phosphate in effluent of control wetland were 41.15, 23.59, 1.735, and 6.43 mg/L, and in wetland with reeds were 28.91, 19.99, 1.49 and 5.63 mg/L, respectively. First-order, second-order, and Stover-Kincannon models were applied and analyzed using statistical parameters obtained from the models (Umax, KB. Conclusion: The nutrients removal at Yazd wastewater treatment plant was remarkable, and the presence of reeds in wetland beds was not very efficient in improving system performance. Other more efficient plants are suggested to be evaluated in the system. Stover-Kincannon kinetic model provided predictions having the closest relationship with actual data obtained from the field.

  11. 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.

  12. A SIMPLIFIED WATER QUALITY MODEL FOR WETLANDS

    Institute of Scientific and Technical Information of China (English)

    Jan-Tai KUO; Jihn-Sung LAI; Wu-Seng LUNG; Chou-Ping YANG

    2004-01-01

    The purpose of this study is to develop a simplified mathematical model to simulate suspended solids and total phosphorus concentrations in a wetland or detention pond.Field data collected from a wet detention pond during storms were used to demonstrate the application of this model.Favorable agreements between the model results and data were achieved.The ratio of average outlet method and summary of loads method were used to quantify the removal efficiency of pollutants,reflecting the efficiencies are very close.The results of this study can be used for nonpoint source pollution control,wastewater treatment or best management practices (BMPs) through the wetland.

  13. 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.

  14. 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.

  15. 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.

  16. 九龙山红树林国家湿地公园人工养殖与社区管理模式%Study on Artificial Breeding and Community Management Mode of Jiulongshan Mangrove National Wetland Park

    Institute of Scientific and Technical Information of China (English)

    梁曾飞

    2014-01-01

    阐述当前九龙山国家湿地公园区域内人工养殖的现状,指出人工养殖存在的问题,分析人工养殖对湿地公园生态系统的影响。根据湿地公园的实际情况,分析九龙山红树林国家湿地公园开展社区管理的必要性,提出了全新的社区管理模式。%This paper analyzed the current status of artificial breeding in Jiulongshan mangrove national wetland park, pointed out the problems of artificial culture, and analyzed the impact of artificial culture of wetland ecosystems. According to the situation of wetland park, this paper analyzed the necessity to carry out community wetland management, and presented a community management model.

  17. Seasonal Variation of Nutrient Removal in a Full-Scale Artificial Aerated Hybrid Constructed Wetland

    Directory of Open Access Journals (Sweden)

    Jun Zhai

    2016-11-01

    Full Text Available To improve nutrient removal, a full-scale hybrid constructed wetland (CW consisting of pre-treatment units, vertical-baffled flow wetlands (VBFWs, and horizontal subsurface flow wetlands (HSFWs was installed in August 2014 to treat sewage wastewater. Artificial aeration (AA was applied continuously in the VBFW stage to improve the aerobic condition in the hybrid CW. Water samples were collected and analyzed twice a month between the period of August 2015 and July 2016. The results suggest that this new hybrid CW can achieve a satisfactory reduction of chemical oxygen demand (COD, ammonium nitrogen (NH4+-N, total nitrogen (TN, and total phosphorus (TP with average removal rates of 85% ± 10% (35% ± 19 g/m2 per day, 76% ± 18% (7% ± 2 g/m2 per day, 65% ± 13% (8% ± 2 g/m2 per day, and 65% ± 21% (1 g/m2 per day, respectively. AA significantly improved the aerobic condition throughout the experimental period, and the positive influence of AA on nitrogen removal was found to be higher during summer that during winter. A significant positive correlation between water temperature and nutrient removal (p < 0.01 was observed in the system. Overall, this study demonstrates the application of AA in a full-scale hybrid CW with satisfactory nutrient removal rates. The hybrid CW system with artificial aeration can serve as a reference for future applications areas where land availability is limited.

  18. 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.

  19. 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

  20. 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

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

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    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

  2. 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.

  3. Spatial modeling on the nutrient retention of an estuary wetland

    NARCIS (Netherlands)

    Li, X.; Xiao, D.; Jongman, R.H.G.; Harms, W.B.; Bregt, A.K.

    2003-01-01

    There is a great potential to use the estuary wetland as a final filter for nutrient enriched river water, and reduce the possibility of coastal water eutrophication. Based upon field data, spatial models were designed on a stepwise basis to simulate the nutrient reduction function of the wetland in

  4. Effects of stereoscopic artificial floating wetlands on nekton abundance and biomass in the Yangtze Estuary.

    Science.gov (United States)

    Huang, Xiaofeng; Zhao, Feng; Song, Chao; Gao, Yu; Geng, Zhi; Zhuang, Ping

    2017-09-01

    Habitat degradation is one of the greatest existing threats to nekton biodiversity in estuarine and coastal habitats. Stereoscopic artificial floating wetlands (SAFWs) can provide new nekton habitats and have been widely used as conservation and management tools in freshwater and marine environments. In the current study, we constructed Phragmites australis SAFWs: the P. australis rhizomes were planted on the artificial floating beds, and palm slices were hung under the floating platforms to act as submerged plants. These SAFWs were anchored in the north channel of the Yangtze Estuary. To determine if SAFWs can serve as fish aggregation devices, fishes and crustaceans were sampled monthly using a bottom lift net during high-tide from July to October 2014. Our assessment was based on environmental parameters, nekton density, nekton species composition and the total length of the three most abundant fishes at the experimental and control sites. Nekton abundance was approximately three times greater in the SAFWs than that in the control habitats (108.2 ± 27.56 ind./m(2) vs. 28.37 ± 15.88 ind./m(2), respectively). There were no significant habitat-specific differences in the size distribution of the three most abundant fish species (Acanthogobius ommaturus, Odontamblyopus rubicundus and Eleutheronema tetradactylum) because most of the individuals sampled were juveniles. This study demonstrates that SAFWs can form stable environments for nekton and increase habitat available to juvenile fish. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. Present state of global wetland extent and wetland methane modelling: conclusions from a model intercomparison project (WETCHIMP

    Directory of Open Access Journals (Sweden)

    J. R. Melton

    2012-08-01

    Full Text Available Global wetlands are believed to be climate sensitive, and are the largest natural emitters of methane (CH4. Increased wetland CH4 emissions could act as a positive feedback to future warming. The Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP investigated our present ability to simulate large scale wetland characteristics and corresponding CH4 emissions. To ensure inter-comparability, we used a common experimental protocol driving all models with the same climate and carbon dioxide (CO2 forcing datasets. The WETCHIMP experiments were conducted for model equilibrium states as well as transient simulations covering the last century. Sensitivity experiments investigated model response to changes in selected forcing inputs (precipitation, temperature, and atmospheric CO2 concentration. Ten models participated, covering the spectrum from simple to relatively complex, including models tailored either for regional or global simulations. The models also varied in methods to calculate wetland size and location with some models simulating wetland area prognostically, while other models relied on remotely-sensed inundation datasets, or an approach intermediate between the two.

    Four major conclusions emerged from the project. First, the suite of models demonstrate extensive disagreement in their simulations of wetland areal extent and CH4 emissions, in both space and time. Simple metrics of wetland area, such as the latitudinal gradient, show large variability, principally between models that use inundation dataset information and those that independently determine wetland area. Agreement between the models improves for zonally summed CH4 emissions, but large variation between the models remains. For annual global CH4 emissions, the models vary by ±40 % of the all model mean (190 Tg CH4 yr−1. Second, all

  6. Present state of global wetland extent and wetland methane modelling: conclusions from a model inter-comparison project (WETCHIMP

    Directory of Open Access Journals (Sweden)

    J. R. Melton

    2013-02-01

    Full Text Available Global wetlands are believed to be climate sensitive, and are the largest natural emitters of methane (CH4. Increased wetland CH4 emissions could act as a positive feedback to future warming. The Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP investigated our present ability to simulate large-scale wetland characteristics and corresponding CH4 emissions. To ensure inter-comparability, we used a common experimental protocol driving all models with the same climate and carbon dioxide (CO2 forcing datasets. The WETCHIMP experiments were conducted for model equilibrium states as well as transient simulations covering the last century. Sensitivity experiments investigated model response to changes in selected forcing inputs (precipitation, temperature, and atmospheric CO2 concentration. Ten models participated, covering the spectrum from simple to relatively complex, including models tailored either for regional or global simulations. The models also varied in methods to calculate wetland size and location, with some models simulating wetland area prognostically, while other models relied on remotely sensed inundation datasets, or an approach intermediate between the two.

    Four major conclusions emerged from the project. First, the suite of models demonstrate extensive disagreement in their simulations of wetland areal extent and CH4 emissions, in both space and time. Simple metrics of wetland area, such as the latitudinal gradient, show large variability, principally between models that use inundation dataset information and those that independently determine wetland area. Agreement between the models improves for zonally summed CH4 emissions, but large variation between the models remains. For annual global CH4 emissions, the models vary by ±40% of the all-model mean (190 Tg CH4 yr−1. Second, all

  7. 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.

  8. 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.

  9. Present state of global wetland extent and wetland methane modelling: methodology of a model intercomparison project (WETCHIMP

    Directory of Open Access Journals (Sweden)

    R. Wania

    2012-12-01

    Full Text Available The Wetland and Wetland CH4 Intercomparison of Models Project (WETCHIMP was created to evaluate our present ability to simulate large-scale wetland characteristics and corresponding methane (CH4 emissions. A multi-model comparison is essential to evaluate the key uncertainties in the mechanisms and parameters leading to methane emissions. Ten modelling groups joined WETCHIMP to run eight global and two regional models with a common experimental protocol using the same climate and atmospheric carbon dioxide (CO2 forcing datasets. We reported the main conclusions from the intercomparison effort in a companion paper (Melton et al., 2012. Here we provide technical details for the six experiments, which included an equilibrium, a transient, and an optimized run plus three sensitivity experiments (temperature, precipitation, and atmospheric CO2 concentration. The diversity of approaches used by the models is summarized through a series of conceptual figures, and is used to evaluate the wide range of wetland extents and CH4 fluxes predicted by the models in the equilibrium run. We discuss relationships among the various approaches and patterns in consistencies of these model predictions. Within this group of models, there are three broad classes of methods used to estimate wetland extent: prescribed based on wetland distribution maps, prognostic relationships between hydrological states based on satellite observations, and explicit hydrological mass balances. A larger variety of approaches was used to estimate the net CH4 fluxes from wetland systems. Even though modelling of wetland extents and CH4 emissions has progressed significantly over recent decades, large uncertainties still exist when estimating CH4 emissions: there is little consensus on model structure or complexity due to knowledge gaps, different aims of the models, and the range of temporal and spatial

  10. Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP

    Directory of Open Access Journals (Sweden)

    R. Wania

    2013-05-01

    Full Text Available The Wetland and Wetland CH4 Intercomparison of Models Project (WETCHIMP was created to evaluate our present ability to simulate large-scale wetland characteristics and corresponding methane (CH4 emissions. A multi-model comparison is essential to evaluate the key uncertainties in the mechanisms and parameters leading to methane emissions. Ten modelling groups joined WETCHIMP to run eight global and two regional models with a common experimental protocol using the same climate and atmospheric carbon dioxide (CO2 forcing datasets. We reported the main conclusions from the intercomparison effort in a companion paper (Melton et al., 2013. Here we provide technical details for the six experiments, which included an equilibrium, a transient, and an optimized run plus three sensitivity experiments (temperature, precipitation, and atmospheric CO2 concentration. The diversity of approaches used by the models is summarized through a series of conceptual figures, and is used to evaluate the wide range of wetland extent and CH4 fluxes predicted by the models in the equilibrium run. We discuss relationships among the various approaches and patterns in consistencies of these model predictions. Within this group of models, there are three broad classes of methods used to estimate wetland extent: prescribed based on wetland distribution maps, prognostic relationships between hydrological states based on satellite observations, and explicit hydrological mass balances. A larger variety of approaches was used to estimate the net CH4 fluxes from wetland systems. Even though modelling of wetland extent and CH4 emissions has progressed significantly over recent decades, large uncertainties still exist when estimating CH4 emissions: there is little consensus on model structure or complexity due to knowledge gaps, different aims of the models, and the range of temporal and spatial resolutions of the models.

  11. Nitrogen transformations and retention in planted and artificially aerated constructed wetlands.

    Science.gov (United States)

    Maltais-Landry, Gabriel; Maranger, Roxane; Brisson, Jacques; Chazarenc, Florent

    2009-02-01

    Nitrogen (N) processing in constructed wetlands (CWs) is often variable, and the contribution to N loss and retention by various pathways (nitrification/denitrification, plant uptake and sediment storage) remains unclear. We studied the seasonal variation of the effects of artificial aeration and three different macrophyte species (Phragmites australis, Typha angustifolia and Phalaris arundinacea) on N processing (removal rates, transformations and export) using experimental CW mesocosms. Removal of total nitrogen (TN) was higher in summer and in planted and aerated units, with the highest mean removal in units planted with T. angustifolia. Export of ammonium (NH(4)(+)), a proxy for nitrification limitation, was higher in winter, and in unplanted and non-aerated units. Planted and aerated units had the highest export of oxidized nitrogen (NO(y)), a proxy for reduced denitrification. Redox potential, evapotranspiration (ETP) rates and hydraulic retention times (HRT) were all predictors of TN, NH(4)(+) and NO(y) export, and significantly affected by plants. Denitrification was the main N sink in most treatments accounting for 47-62% of TN removal, while sediment storage was dominant in unplanted non-aerated units and units planted with P. arundinacea. Plant uptake accounted for less than 20% of the removal. Uncertainties about the long-term fate of the N stored in sediments suggest that the fraction attributed to denitrification losses could be underestimated in this study.

  12. Wetland State-and-Transition Model _Units

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — A geodatabase containing the boundaries of semipermanently flooded wetlands sampled on 8 National Wildlife Refuges in 2014 and 2015. These stations are located in...

  13. Plant Growth Models Using Artificial Neural Networks

    Science.gov (United States)

    Bubenheim, David

    1997-01-01

    In this paper, we descrive our motivation and approach to devloping models and the neural network architecture. Initial use of the artificial neural network for modeling the single plant process of transpiration is presented.

  14. Economic modeling using artificial intelligence methods

    CERN Document Server

    Marwala, Tshilidzi

    2013-01-01

    This book examines the application of artificial intelligence methods to model economic data. It addresses causality and proposes new frameworks for dealing with this issue. It also applies evolutionary computing to model evolving economic environments.

  15. Comparison of the diversity of root-associated bacteria in Phragmites australis and Typha angustifolia L. in artificial wetlands.

    Science.gov (United States)

    Li, Yan Hong; Zhu, Jing Nan; Liu, Qun Fang; Liu, Yin; Liu, Min; Liu, Lei; Zhang, Qiang

    2013-08-01

    Common reed (Phragmites australis) and narrow-leaved cattail (Typha angustifolia L.) are two plant species used widely in artificial wetlands constructed to treat wastewater. In this study, the community structure and diversity of root-associated bacteria of common reed and narrow-leaved cattail growing in the Beijing Cuihu Wetland, China, were investigated using 16S rDNA library and PCR-denaturing gradient gel electrophoresis methods. Root-associated bacterial diversity was higher in common reed than in narrow-leaved cattail. In both plant species, the dominant root-associated bacterial species were Alpha, Beta and Gamma Proteobacteria, including the genera Aeromonas, Hydrogenophaga, Ideonella, Uliginosibacterium and Vogesella. Acidobacteria, Actinobacteria, Nitrospirae and Spirochaetes were only found in the roots of common reed. Comparing the root-associated bacterial communities of reed and cattail in our system, many more species of bacteria related involved in the total nitrogen cycle were observed in reed versus cattail, while species involved in total phosphorus and organic matter removal were mainly found in cattail. Although we cannot determine their nutrient removal capacity separately, differences in the root-associated bacterial communities may be an important factor contributing to the differing water purification effects mediated by T. angustifolia and P. australis wetlands. Thus, further work describing the ecosystem functions of these bacterial species is needed, in order to fully understand how effective common reed- and narrow-leaved cattail-dominated wetlands are for phytoremediation.

  16. 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

  17. Successional changes and water quality in artificial wetlands at the Rocky Mountain Arsenal progress report

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — The U.S. Army and U.S. Fish and Wildlife Service created five wetlands on Rocky Mountain Arsenal (RMA) during the summer and fall of 1991. These wetlands were...

  18. Effect of artificial aeration on the performance of vertical-flow constructed wetland treating heavily polluted river water

    Institute of Scientific and Technical Information of China (English)

    Huiyu Dong; Zhimin Qiang; Tinggang Li; Hui Jin; Weidong Chen

    2012-01-01

    Three lab-scale vertical-flow constructed wetlands (VFCWs),including the non-aerated (NA),intermittently aerated (IA) and continuously aerated (CA) ones,were operated at different hydraulic loading rates (HLRs) to evaluate the effect of artificial aeration on the treatment efficiency of heavily polluted river water.Results indicated that artificial aeration increased the dissolved oxygen (DO) concentrations in IA and CA,which significantly favored the removal of organic matter and NH4+-N.The DO grads caused by intermittent aeration formed aerobic and anoxic regions in IA and thus promoted the removal of total nitrogen (TN).Although the removal efficiencies of CODcr,NH4+-N and TN in the three VFCWs all decreased with an increase in HLR,artificial aeration enhanced the reactor resistance to the fluctuation of pollutant loadings.The maximal removal efficiencies of CODcr,NH4+-N and total phosphorus (TP) (i.e.,81%,87% and 37%,respectively) were observed in CA at 19 cm/day HLR,while the maximal TN removal (i.e.,57%) was achieved in IA.Although the improvement of artificial aeration on TP removal was limited,this study has demonstrated the feasibility of applying artificial aeration to VFCWs treating polluted eiver water,particularly at a high HLR.

  19. 关于人工湿地水质净化技术分析%Analysis on Artificial Wetland Water Purification Technology

    Institute of Scientific and Technical Information of China (English)

    刘继凯; 陈玉涛

    2016-01-01

    The wetland is the humid area of land and water, artificial wetland sewage purification function, with its unique increasingly attention from all walks of life� Papers on the related concepts of artificial wetland and characteristics are analyzed, and water quality purification of artificial wetland system was analyzed, and the artificial wetland water purification technology in sewage treatment has a very broad application prospects.%湿地是陆地的潮湿地带和水体,人工湿地以其独有的污水净化功能,日益受到各界的关注。本文对人工湿地的相关概念和特点进行了分析,并对人工湿地系统水质净化技术进行了分析,人工湿地水质净化技术在污水深度处理中具有非常广阔的应用前景。

  20. Identification and characterization of sulfur-oxidizing bacteria in an artificial wetland that treats wastewater from a tannery.

    Science.gov (United States)

    Pacheco Aguilar, Juan Ramiro; Peña Cabriales, Juan José; Maldonado Vega, María

    2008-01-01

    Wastewater from tanneries contains high concentrations of organic matter, chromium, nitrogen, and sulfur compounds. In this study, an artificial wetland is is used as the tertiary treatment in a tannery in León Gto., México. It consists of three subplots with an area of about 450 m2. Two subplots were planted with Typha sp. and the third with Scirpus americanus. Geochemical analyses along the flowpath of the wetland show that contaminants were effectively attenuated. The most probable number technique was used to determine rhizospheric microbial populations involved in the sulfur cycle and suggested that there were 104-10(6) cells g(-1) sediment of sulfate-reducing bacteria and 10(2)-10(5) of sulfur-oxidizing bacteria (SOB). Representatives of SOB were isolated on media containing thiosulfate. Phylogenetic analysis of 16S rRNA of SOB isolates shows that they belong to the genera Acinetobacter, Alcaligenes, Ochrobactrum, and Pseudomonas. Most of the isolates are organotrophic and can oxidize reduced sulfur compounds such as elemental sulfur or thiosulfate, accumulating thiosulfate, or tetrathionate during growth. All isolates can use reduced-sulfur compounds as their sole sulfur source and some can use nitrate as an electron acceptor to grow anaerobically. Our results illustrate the relevance of SOB in the functioning of the wetland constructed for tannery wastewater remediation.

  1. Performance of Four Full-Scale Artificially Aerated Horizontal Flow Constructed Wetlands for Domestic Wastewater Treatment

    National Research Council Canada - National Science Library

    Butterworth, Eleanor; Richards, Andrew; Jones, Mark; Mansi, Gabriella; Ranieri, Ezio; Dotro, Gabriela; Jefferson, Bruce

    2016-01-01

      A comparison of the performance of four full-scale aerated horizontal flow constructed wetlands was conducted to determine the efficacy of the technology on sites receiving high and variable ammonia...

  2. Analyzing the ecosystem carbon and hydrologic characteristics of forested wetland using a biogeochemical process model

    Science.gov (United States)

    Jianbo Cui; Changsheng Li; Carl Trettin

    2005-01-01

    A comprehensive biogeochemical model, Wetland-DNDC, was applied to analyze the carbon and hydrologic characteristics of forested wetland ecosystem at Minnesota (MN) and Florida (FL) sites. The model simulates the flows of carbon, energy, and water in forested wetlands. Modeled carbon dynamics depends on physiological plant factors, the size of plant pools,...

  3. Modelling Microwave Devices Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Andrius Katkevičius

    2012-04-01

    Full Text Available Artificial neural networks (ANN have recently gained attention as fast and flexible equipment for modelling and designing microwave devices. The paper reviews the opportunities to use them for undertaking the tasks on the analysis and synthesis. The article focuses on what tasks might be solved using neural networks, what challenges might rise when using artificial neural networks for carrying out tasks on microwave devices and discusses problem-solving techniques for microwave devices with intermittent characteristics.Article in Lithuanian

  4. Modelling constructed wetlands: scopes and aims - a comparative review

    OpenAIRE

    Meyer, D; Chazarenc, Florent; Claveau Mallet, D.; Dittmer, D; Forquet, N.; Molle, P.; Morvannou, A.; Palfy, T.; Petitjean, A; Rizzo, A.; Samso Campa, R.; Scholz, M.; Soric, Audrey; Langergraber, G.

    2015-01-01

    International audience; During the last two decades a couple of models were developed for constructed wetlands with differing purposes. Meanwhile the usage of this kind of tool is generally accepted, but the misuse of the models still confirms the skepticism. Generally three some groups of models can be distinguished: On one hand mechanistic models try to display the complex and diffuse interaction of occurring processes, on the other hand the same kind of models is are used to investigate si...

  5. Impact of wetlands mapping on parameterization of hydrologic simulation models

    Science.gov (United States)

    Viger, R.

    2015-12-01

    Wetlands and other surface depressions can impact hydrologic response within the landscape in a number of ways, such as intercepting runoff and near-surface flows or changing the potential for evaporation and seepage into the soil. The role of these features is increasingly being integrated into hydrological simulation models, such as the USGS Precipitation-Runoff Modeling System (PRMS) and the Soil Water Assessment Tool (SWAT), and applied to landscapes where wetlands are dominating features. Because the extent of these features varies widely through time, many modeling applications rely on delineations of the maximum possible extent to define total capacity of a model's spatial response unit. This poster presents an evaluation of several wetland map delineations for the Pipestem River basin in the North Dakota Prairie-pothole region. The featured data sets include the US Fish and Wildlife Service National Wetlands Inventory (NWI), surface water bodies extracted from the US Geological Survey National Hydrography Dataset (NHD), and elevation depressions extracted from 1 meter LiDAR data for the area. In addition to characterizing differences in the quality of these datasets, the poster will assess the impact of these differences when parameters are derived from them for the spatial response units of the PRMS model.

  6. Identifying determinants of nations' wetland management programs using structural equation modeling: An exploratory analysis

    Science.gov (United States)

    La Peyre, M.K.; Mendelssohn, I.A.; Reams, M.A.; Templet, P.H.; Grace, J.B.

    2001-01-01

    Integrated management and policy models suggest that solutions to environmental issues may be linked to the socioeconomic and political Characteristics of a nation. In this study, we empirically explore these suggestions by applying them to the wetland management activities of nations. Structural equation modeling was used to evaluate a model of national wetland management effort and one of national wetland protection. Using five predictor variables of social capital, economic capital, environmental and political characteristics, and land-use pressure, the multivariate models were able to explain 60% of the variation in nations' wetland protection efforts based on data from 90 nations, as defined by level of participation, in the international wetland convention. Social capital had the largest direct effect on wetland protection efforts, suggesting that increased social development may eventually lead to better wetland protection. In contrast, increasing economic development had a negative linear relationship with wetland protection efforts, suggesting the need for explicit wetland protection programs as nations continue to focus on economic development. Government, environmental characteristics, and land-use pressure also had a positive direct effect on wetland protection, and mediated the effect of social capital on wetland protection. Explicit wetland protection policies, combined with a focus on social development, would lead to better wetland protection at the national level.

  7. Modelling trade-offs between livelihoods and wetland ecosystem services: the case of Ga-Mampa wetland,South Africa

    OpenAIRE

    Morardet, S.; Masiyandima, M.; Jogo, W.; Juizo, D.

    2010-01-01

    International audience; This paper presents an integrated dynamic simulation model that represents the functioning of a small South African wetland. The model was developed using the STELLA platform and comprises six interactive sectors namely: hydrology, crop production, crop economics, use of natural wetland resources, land use decision and community well-being. These sectors are inter- linked and changes in one sector impact on other sectors through feedback loops between sectors. Key para...

  8. Modelling the Hydraulic Processes on Constructed Stormwater Wetland

    Directory of Open Access Journals (Sweden)

    Isri Ronald Mangangka

    2017-03-01

    Full Text Available Constructed stormwater wetlands are manmade, shallow, and extensively vegetated water bodies which promote runoff volume and peak flow reduction, and also treat stormwater runoff quality. Researchers have noted that treatment processes of runoff in a constructed wetland are influenced by a range of hydraulic factors, which can vary during a rainfall event, and their influence on treatment can also vary as the event progresses. Variation in hydraulic factors during an event can only be generated using a detailed modelling approach, which was adopted in this research by developing a hydraulic conceptual model. The developed model was calibrated using trial and error procedures by comparing the model outflow with the measured field outflow data. The accuracy of the developed model was analyzed using a well-known statistical analysis method developed based on the regression analysis technique. The analysis results show that the developed model is satisfactory.

  9. Applying Process-Based Models for Subsurface Flow Treatment Wetlands: Recent Developments and Challenges

    Directory of Open Access Journals (Sweden)

    Guenter Langergraber

    2016-12-01

    Full Text Available To date, only few process-based models for subsurface flow treatment wetlands have been developed. For modelling a treatment wetland, these models have to comprise a number of sub-models to describe water flow, pollutant transport, pollutant transformation and degradation, effects of wetland plants, and transport and deposition of suspended particulate matter. The two most advanced models are the HYDRUS Wetland Module and BIO-PORE. These two models are briefly described. This paper shows typical simulation results for vertical flow wetlands and discusses experiences and challenges using process-based wetland models in relation to the sub-models describing the most important wetland processes. It can be demonstrated that existing simulation tools can be applied for simulating processes in treatment wetlands. Most important for achieving a good match between measured and simulated pollutant concentrations is a good calibration of the water flow and transport models. Only after these calibrations have been made and the effect of the influent fractionation on simulation results has been considered, should changing the parameters of the biokinetic models be taken into account. Modelling the effects of wetland plants is possible and has to be considered when important. Up to now, models describing clogging are the least established models among the sub-models required for a complete wetland model and thus further development and research is required.

  10. Psychometric Measurement Models and Artificial Neural Networks

    Science.gov (United States)

    Sese, Albert; Palmer, Alfonso L.; Montano, Juan J.

    2004-01-01

    The study of measurement models in psychometrics by means of dimensionality reduction techniques such as Principal Components Analysis (PCA) is a very common practice. In recent times, an upsurge of interest in the study of artificial neural networks apt to computing a principal component extraction has been observed. Despite this interest, the…

  11. Recirculation or artificial aeration in vertical flow constructed wetlands: a comparative study for treating high load wastewater.

    Science.gov (United States)

    Foladori, Paola; Ruaben, Jenny; Ortigara, Angela R C

    2013-12-01

    Vertical subsurface-flow constructed wetlands at pilot-scale have been applied to treat high hydraulic and organic loads by implementing the following configurations: (1) intermittent recirculation of the treated wastewater from the bottom to the top of the bed, (2) intermittent artificial aeration supplied at the bottom of the bed and (3) the combination of both. These configurations were operated with a saturated bottom layer for a 6h-treatment phase, followed by a free drainage phase prior to a new feeding. COD removal efficiency was 85-90% in all the configurations and removed loads were 54-70 gCOD m(-2)d(-1). The aerated and recirculated wetland resulted in a higher total nitrogen removal (8.6 gN m(-2)d(-1)) due to simultaneous nitrification/denitrification, even in the presence of intermittent aeration (6.8 Nm(3)m(-2)d(-1)). The extra investment needed for implementing aeration/recirculation would be compensated for by a reduction of the surface area per population equivalent, which decreased to 1.5m(2)/PE. Copyright © 2013 Elsevier Ltd. All rights reserved.

  12. Modeling Channelization in Coastal Wetlands with Ecological Feedbacks

    Science.gov (United States)

    Hughes, Z. J.; Mahadevan, A.; Pennings, S.; FitzGerald, D.

    2014-12-01

    In coastal wetlands in Georgia and South Carolina, dendritic channel networks are actively incising headward at the rate of nearly 2 m/yr. The future geomorphic evolution of these marshes remains in question as rates of relative sea-level rise increase. Our objective is to understand the mechanisms that lead to the evolution of these channel networks through field observations and modeling. We model the geomorphological evolution of tidal creeks by viewing the wetland as a permeable medium. The porosity of the medium affects its hydraulic conductivity, which in turn is altered by erosion. Our multiphase model spontaneously generates channelization and branching networks through flow and erosion. In our field studies, we find that crabs play an active role in grazing vegetation and in the bioturbation of sediments. These effects are incorporated in our model based on field and laboratory observations of crab behavior and its effects on the marsh. We find the erosional patterns and channelization are significantly altered by the faunal feedback. Crabs enhance the growth of channels, inducing the headward erosion of creeks where flow-induced stresses are weakest. They are instrumental in generating high rates of creek extension, which channelize the marsh more effectively in response to sea-level rise. This indicates that the evolution of coastal wetlands is responding to interactions between physics and ecology and highlights the importance of the faunal contribution to these feedbacks.

  13. Wetland modeling and information needs at Stillwater National Wildlife Refuge

    Science.gov (United States)

    Hamilton, David B.; Auble, Gregor T.

    1993-01-01

    The marshes in and around Stillwater National Wildlife Refuge (the Refuge) are extremely dynamic; expanding and contracting in size both seasonally, due to runoff and subsequent evapotranspiration, and over longer periods, due to climatic variation. The dynamic nature of these marshes results in a diversity of wetland habitats, which support a variety of migratory birds. To maintain this wetland diversity and control the loss of migratory bird habitat in the Lahontan Valley, the Refuge was established and currently manages a complex of marsh units. However, changes in the hydrology, and changes that will occur as a result of the Fallon Paiute-Shoshone and Truckee-Carson-Pyramid Lake Water Rights Settlement Act (Public Law 101-618, 104 Stat. 3389), greatly affect the Refuge's wetland management capability. In light of these changes, and the legal requirements associated with environmental impact assessments, the Refuge convened a workshop to discuss several aspects of wetland management in the Lahontan Valley. The workshop, described in this report, had three primary objectives: 1. discuss the types and relative proportions of primary wetland habitats that should be provided as described in the settlement act; 2. discuss wetland management models that might be developed to help manage these marshes under hydrologic regimes likely in the future; and 3. discuss future information and monitoring needs, including proposals for valley-wide biodiversity surveys, which would be helpful when considering withdrawn Bureau of Reclamation (BR) lands for possible incorporation into the Refuge. Several presentations at the beginning of the workshop provided a common basis for discussing these objectives. Refuge staff provided background on the history and past management. The Nature Conservatory discussed their role in the settlement act, proposals for valley-wide biodiversity surveys, and results of a literature review for Stillwater Marsh and the Lahontan Valley (Nachlinger

  14. Global coastal wetland change under sea-level rise and related stresses: The DIVA Wetland Change Model

    Science.gov (United States)

    Spencer, Thomas; Schuerch, Mark; Nicholls, Robert J.; Hinkel, Jochen; Lincke, Daniel; Vafeidis, A. T.; Reef, Ruth; McFadden, Loraine; Brown, Sally

    2016-04-01

    The Dynamic Interactive Vulnerability Assessment Wetland Change Model (DIVA_WCM) comprises a dataset of contemporary global coastal wetland stocks (estimated at 756 × 103 km2 (in 2011)), mapped to a one-dimensional global database, and a model of the macro-scale controls on wetland response to sea-level rise. Three key drivers of wetland response to sea-level rise are considered: 1) rate of sea-level rise relative to tidal range; 2) lateral accommodation space; and 3) sediment supply. The model is tuned by expert knowledge, parameterised with quantitative data where possible, and validated against mapping associated with two large-scale mangrove and saltmarsh vulnerability studies. It is applied across 12,148 coastal segments (mean length 85 km) to the year 2100. The model provides better-informed macro-scale projections of likely patterns of future coastal wetland losses across a range of sea-level rise scenarios and varying assumptions about the construction of coastal dikes to prevent sea flooding (as dikes limit lateral accommodation space and cause coastal squeeze). With 50 cm of sea-level rise by 2100, the model predicts a loss of 46-59% of global coastal wetland stocks. A global coastal wetland loss of 78% is estimated under high sea-level rise (110 cm by 2100) accompanied by maximum dike construction. The primary driver for high vulnerability of coastal wetlands to sea-level rise is coastal squeeze, a consequence of long-term coastal protection strategies. Under low sea-level rise (29 cm by 2100) losses do not exceed ca. 50% of the total stock, even for the same adverse dike construction assumptions. The model results confirm that the widespread paradigm that wetlands subject to a micro-tidal regime are likely to be more vulnerable to loss than macro-tidal environments. Countering these potential losses will require both climate mitigation (a global response) to minimise sea-level rise and maximisation of accommodation space and sediment supply (a regional

  15. 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...

  16. Modeling the hydrological patterns on Pantanal wetlands, Brazil

    Science.gov (United States)

    Castro, A. A.; Cuartas, A.; Coe, M. T.; Koumrouyan, A.; Panday, P. K.; Lefebvre, P.; Padovani, C.; Costa, M. H.; de Oliveira, G. S.

    2014-12-01

    The Pantanal of Brazil is one of the world's largest wetland regions. It is located within the 370,000 km2 Alto Paraguai Basin (BAP). In wet years almost 15% of the total area of the basin can be flooded (approximately 53,000 km2). The hydrological cycle is particularly important in the Pantanal in the transport of materials, and the transfer of energy between atmospheric, aquatic, and terrestrial systems. The INLAND (Integrated Land Surface Model) terrestrial ecosystem model is coupled with the THMB hydrological model to examine the hydrological balance and water dynamics for this region. The INLAND model is based on the IBIS dynamic vegetation model, while THMB represents the river, wetland and lake dynamics of the land surface. The modeled hydrological components are validated with surface and satellite-based estimates of precipitation (gridded observations from CRU v. 3.21, reanalysis data from ERA-interim, and TRMM estimates), evapotranspiration (MODIS and Land Flux-Eval dataset), total runoff (discharge data from ANA-Agência Nacional das Águas - Brazil), and terrestrial water storage (GRACE). Results show that the coupled hydrological model adequately represents the water cycle components, the river discharge and flooded areas. Model simulations are further used to study the influences of climatic variations on the hydrological components, river network, and the inundated areas in the Pantanal.

  17. Modelling natural and artificial hands with synergies.

    Science.gov (United States)

    Bicchi, Antonio; Gabiccini, Marco; Santello, Marco

    2011-11-12

    We report on recent work in modelling the process of grasping and active touch by natural and artificial hands. Starting from observations made in human hands about the correlation of degrees of freedom in patterns of more frequent use (postural synergies), we consider the implications of a geometrical model accounting for such data, which is applicable to the pre-grasping phase occurring when shaping the hand before actual contact with the grasped object. To extend applicability of the synergy model to study force distribution in the actual grasp, we introduce a modified model including the mechanical compliance of the hand's musculotendinous system. Numerical results obtained by this model indicate that the same principal synergies observed from pre-grasp postural data are also fundamental in achieving proper grasp force distribution. To illustrate the concept of synergies in the dual domain of haptic sensing, we provide a review of models of how the complexity and heterogeneity of sensory information from touch can be harnessed in simplified, tractable abstractions. These abstractions are amenable to fast processing to enable quick reflexes as well as elaboration of high-level percepts. Applications of the synergy model to the design and control of artificial hands and tactile sensors are illustrated.

  18. 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.

  19. 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.

  20. Spatial Economic-Hydroecological Modelling and Evaluation of Land Use Impacts in the Vecht Wetlands Area

    OpenAIRE

    van den Bergh, J.C.J.M.; Barendregt, A.; Gilbert, A.; Herwijnen, M. van; van Horssen, P.; P. Kandelaars

    2000-01-01

    Wetlands provide many important goods and services to human societies, and generate nonuse values as well. Wetlands are also very sensitive ecosystems that are subject to much stress from human activities. Reducing the stress on wetlands requires a spatial matching between physical planning, hydrological and ecological processes, and economic activities. Spatially integrated modelling and evaluation can support this. The present study has developed a triple layer model that integrates informa...

  1. Modeling and Understanding BOD Removal Processes in Free-Water Surface Constructed Wetlands

    Science.gov (United States)

    Deng, Z.

    2016-12-01

    Free-water surface constructed wetlands have proven to be effective systems for removal of various pollutants in wastewater and agricultural drainage water. Modeling tools are needed for understanding the processes and mechanisms responsible for the removal of pollutants and for the design of new constructed wetlands. This paper presents a new model for mimicking the processes and mechanisms controlling the removal of BOD (biochemical oxygen demand) in free-water surface constructed wetlands. The processes and mechanisms, simulated in the model, include advection, dispersion, diffusion, monod kinetics of bacterial growth, water gains (via precipitation) and losses (evaporation and seepage) and mass exchange between water column and root layers of a wetland. A novel feature of the new model is the incorporation of a dynamic diffusive root-zone. Sensitivity analysis of the model input vaiables indicates that the BOD removal in free water surface constructed wetlands is most sensitive to the biological removal process of BOD in the root zone, controlled by acetic acid and anaerobic bacteria in root zone, and the flow velocity (controlling mean hydraulic residence time) and organic carbon in the water column. The application of the new model is demonstrated through two case studies involving two distinct constructed wetlands with one (Gustine Wetland) for treatment of secondary wastewater located in the USA and another (Lake Manzala Engineered Wetland) for treatment of agricultural drainage water in Egypt. The model is relatively simple yet effective, as evidenced by the high coefficient of determination of 0.73 - 0.99 for the Gustine Wetland and 0.98 for Manzala Wetland. The model is a reliable and efficient tool for designing constructed wetlands and for understanding effects of various processes and mechanisms on the treatment efficiency of wastewater in constructed wetlands.

  2. Intercomparison of the Wetchimp-Wsl Wetland Methane Models over West Siberia: How Well Can We Simulate High-Latitude Wetland Methane Emissions?

    Science.gov (United States)

    Bohn, T. J.; Melton, J. R.; Brovkin, V.; Chen, G.; Denisov, S. N.; Eliseev, A. V.; Gallego-Sala, A. V.; Glagolev, M.; Ito, A.; Kaplan, J. O.; Kleinen, T.; Maksyutov, S. S.; McDonald, K. C.; Rawlins, M. A.; Riley, W. J.; Schroeder, R.; Spahni, R.; Stocker, B.; Subin, Z. M.; Tian, H.; Zhang, B.; Zhu, X.; Zhuang, Q.

    2014-12-01

    Wetlands are the world's largest natural source of methane, a powerful greenhouse gas. The strong sensitivity of these emissions to environmental factors such as soil temperature and moisture has led to concerns about potential positive feedbacks to climate change. This is particularly true at high latitudes, which have experienced pronounced warming and where thawing permafrost could potentially liberate large amounts of labile carbon over this century. Despite the importance of wetland methane emissions to the global carbon cycle and climate dynamics, global models exhibit little agreement as to the magnitude and spatial distribution of emissions, due to uncertainties in both wetland area and emissions per unit area driven by a scarcity of in situ observations. Recent intensive field campaigns across West Siberia 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 Model Intercomparison Project focused on the West Siberian Lowland (WETCHIMP-WSL). We assessed 17 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 dataset, several wetland maps, and two satellite inundation products. Findings include: a) estimates of total CH4 emissions from both models and inversions spanned almost an order of magnitude; b) forward models using inundation alone to estimate wetland areas suffered from severe biases in CH4 emissions; and c) aside from these area-driven biases, disagreement in flux per unit wetland area was the main driver of forward model uncertainty. We examine which forward model approaches are best suited towards simulating high-latitude wetlands and make recommendations for future modeling, remote sensing, and field campaigns to reduce model uncertainty.

  3. 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.

  4. 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.

  5. Modeling Heavy Metal Removal in Wetlands.

    Science.gov (United States)

    1992-05-01

    1976 a,b,c) and Pettersson (1976) treated heavy metals uptake according to Michaelis-Menten kinetics ( Lehninger , 1975), discussed later in detail...copper kinetics equation as used in this modeling effort is presented below, after Lehninger (1975): dv_ dV, Ca (5) dt dt C.+K, where: v = rate of copper...the bulk solution, Cb, using either the Lineweaver-Burk double reciprocal or Eadie-Hofstee graphical methods ( Lehninger , 1975). Nielsen (1976 b) used

  6. 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.

  7. 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.

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

    Science.gov (United States)

    Huang, Xiaofeng; Zhao, Feng; Yu, Gao; Song, Chao; Geng, Zhi; Zhuang, Ping

    2017-01-01

    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.

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

    Science.gov (United States)

    Zhao, Feng; Yu, Gao; Song, Chao; Geng, Zhi

    2017-01-01

    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. PMID:28717650

  10. 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.

  11. Application of Systems Model and Remote Sensing Images to Improve Wetland Management

    Science.gov (United States)

    Alminagorta, O.; Torres-Rua, A. F.

    2013-05-01

    Wetlands are complex ecosystem that involves interaction among hydrological, ecological and spatial-temporal considerations. Also, water shortages and invasive vegetation are common problems in wetlands. The present paper has the purpose to contribute with the solution of these problems: (i) Providing a tool to wetland managers to monitor changes in vegetation cover and wetland hydrology over time; (ii) Finding a relationship between vegetation response and key hydrological attributes in wetlands and (iii) Incorporating these relationship in an optimization model to recommend water allocation and invasive vegetation control to improve wetland management. This research is applied at the Bear River Migratory Bird Refuge (the Refuge), located on the northeast side of Great Salt Lake, Utah. The Refuge constitutes one of the most important habitats for migratory birds for the Pacific Flyway of North America. Water measures and coverage vegetation collected in-situ at the Refuge has been used to calibrate and evaluate the effects on wetland plant communities to the process of flooding and drought in wetland units during different years. A MATLAB-based algorithm has been developed to process LandSat images to estimate the interaction between flooded areas and invasive vegetation cover. These interactions are embedded in a system optimization model to recommend water allocations and vegetation control actions among diked wetland units that improve wetland habitat for wildlife species. This modeling effort identify the interaction between invasive vegetation and flood wetland areas and embed those interactions in a systems model that wetland managers can use to make informed decisions about allocation of water and manage vegetation cover.

  12. Research progress in the preparation of fuel ethanol from artificial wetland plants%人工湿地植物制备燃料乙醇研究进展

    Institute of Scientific and Technical Information of China (English)

    张小玲; 赵亚芳; 林燕; 王欣泽; 孔海南

    2013-01-01

    In recent years,due to the lack of oil resources,the way of using lignocellulose to produce ethanol has attracted more and more attention. Wetland plants were considered as a research object. Using artificial wetland plants to produce fuel ethanol,not only can reduce the gas of harmful emissions,alleviate the lack of grain crops raw materials,but also can reduce the accumulation of wetland plants which could lead to secondary pollution. However,it is extremely difficult to produce ethanol directly from lignocellulose,due to the low rate of ethanol production,the expensive cellulase and the not well developed lignocellulose pretreatment process. This paper is to discuss the stain resistance and decontamination,appropriate allocation and multipurpose use of artificial wetland plants together with the existing problems when wetland plants are chosen. Key process was analyzed regarding the active ingredient of artificial wetland plants and other lignocellulose when fuel ethanol is produced via the pretreatment and hydrolysis effects of artificial wetland plants and other lignocellulose. The feasibility for the preparation of fuel ethanol from artificial wetland plants was discussed,and the future research development trend is prospected. At last,it can be concluded that wetland plants can replace grain crops fermentation to produce ethanol.%近年来,石油资源短缺,利用木质纤维素制备燃料乙醇越来越受到重视,本文以人工湿地植物为研究对象,利用人工湿地植物制备燃料乙醇,可以减少有害气体的排放、缓解粮食原材料的紧缺、减少植物处理不当产生的二次污染。但同时存在乙醇产率低、纤维素酶价格贵、木质纤维素预处理过程不成熟等问题。本文首先从人工湿地植物的抗性及去污能力、种间合理搭配及综合利用价值三方面入手,论述了在选择人工湿地植物时应注意的问题。其次重点分析了人工湿地植物及其它木

  13. Electronic circuits modeling using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Andrejević Miona V.

    2003-01-01

    Full Text Available In this paper artificial neural networks (ANN are applied to modeling of electronic circuits. ANNs are used for application of the black-box modeling concept in the time domain. Modeling process is described, so the topology of the ANN, the testing signal used for excitation, together with the complexity of ANN are considered. The procedure is first exemplified in modeling of resistive circuits. MOS transistor, as a four-terminal device, is modeled. Then nonlinear negative resistive characteristic is modeled in order to be used as a piece-wise linear resistor in Chua's circuit. Examples of modeling nonlinear dynamic circuits are given encompassing a variety of modeling problems. A nonlinear circuit containing quartz oscillator is considered for modeling. Verification of the concept is performed by verifying the ability of the model to generalize i.e. to create acceptable responses to excitations not used during training. Implementation of these models within a behavioral simulator is exemplified. Every model is implemented in realistic surrounding in order to show its interaction, and of course, its usage and purpose.

  14. Performance of Four Full-Scale Artificially Aerated Horizontal Flow Constructed Wetlands for Domestic Wastewater Treatment

    Directory of Open Access Journals (Sweden)

    Eleanor Butterworth

    2016-08-01

    Full Text Available A comparison of the performance of four full-scale aerated horizontal flow constructed wetlands was conducted to determine the efficacy of the technology on sites receiving high and variable ammonia loading rates not yet reported in the literature. Performance was assessed in terms of ammonia and solids removal, hydraulic conductivity and mixing patterns. The capability of systems to produce ammonium effluent concentrations <3 mgNH4+-N/L was observed across all sites in systems receiving variable loadings between 0.1 and 13.0 gNH4+-N/m2/d. Potential resilience issues were observed in relation to response to spike loadings posited to be due to an insufficient nitrifying population within the beds. Hydraulic conductivity and flow mixing patterns observed suggested deterioration of the reactor effective volume over time. Overall, the study demonstrates the efficacy of the technology where ammonium removal is required on small sites receiving high and variable flow rates, with adequate removal of organics and solids, but no significant benefit to the long term hydraulics of the system.

  15. 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.

  16. Hydrological management for improving nutrient assimilative capacity in plant-dominated wetlands: A modelling approach.

    Science.gov (United States)

    Xu, Zhihao; Yang, Zhifeng; Yin, Xinan; Cai, Yanpeng; Sun, Tao

    2016-07-15

    Wetland eutrophication is a global environmental problem. Besides reducing pollutant emissions, improving nutrient assimilative capacity in wetlands is also significant for preventing eutrophication. Hydrological management can improve nutrient assimilative capacity in wetlands through physical effects on the dilution capacity of water body and ecological effects on wetland nutrient cycles. The ecological effects are significant while were rarely considered in previous research. This study focused on the ecological effects of hydrological management on two crucial nutrient removal processes, plant uptake and biological denitrification, in plant-dominated wetlands. A dual-objective optimization model for hydrological management was developed to improve wetland nitrogen and phosphorus assimilative capacities, using upstream reservoir release as water regulating measure. The model considered the interactions between ecological processes and hydrological cycles in wetlands, and their joint effects on nutrient assimilative capacity. Baiyangdian Wetland, the largest freshwater wetland in northern China, was chosen as a case study. The results found that the annual total assimilative capacity of nitrogen (phosphorus) was 4754 (493) t under the optimal scheme for upstream reservoir operation. The capacity of nutrient removal during the summer season accounted for over 80% of the annual total removal capacity. It was interesting to find that the relationship between water inflow and nutrient assimilative capacity in a plant-dominated wetland satisfied a dose-response relationship commonly describing the response of an organism to an external stressor in the medical field. It illustrates that a plant-dominated wetland shows similar characteristics to an organism. This study offers a useful tool and some fresh implications for future management of wetland eutrophication prevention.

  17. A bench-scale constructed wetland as a model to characterize benzene biodegradation processes in freshwater wetlands.

    Science.gov (United States)

    Rakoczy, Jana; Remy, Benjamin; Vogt, Carsten; Richnow, Hans H

    2011-12-01

    In wetlands, a variety of biotic and abiotic processes can contribute to the removal of organic substances. Here, we used compound-specific isotope analysis (CSIA), hydrogeochemical parameters and detection of functional genes to characterize in situ biodegradation of benzene in a model constructed wetland over a period of 370 days. Despite low dissolved oxygen concentrations (98% removal), we applied CSIA to study in situ benzene degradation by indigenous microbes. Combining carbon and hydrogen isotope signatures by two-dimensional stable isotope analysis revealed that benzene was degraded aerobically, mainly via the monohydroxylation pathway. This was additionally supported by the detection of the BTEX monooxygenase gene tmoA in sediment and root samples. Calculating the extent of biodegradation from the isotope signatures demonstrated that at least 85% of benzene was degraded by this pathway and thus, only a small fraction was removed abiotically. This study shows that model wetlands can contribute to an understanding of biodegradation processes in floodplains or natural wetland systems.

  18. Kinetic modelling of nitrogen and organics removal in vertical and horizontal flow wetlands.

    Science.gov (United States)

    Saeed, Tanveer; Sun, Guangzhi

    2011-05-01

    This paper provides a comparative evaluation of the kinetic models that were developed to describe the biodegradation of nitrogen and organics removal in wetland systems. Reaction kinetics that were considered in the model development included first order kinetics, Monod and multiple Monod kinetics; these kinetics were combined with continuous-stirred tank reactor (CSTR) or plug flow pattern to produce equations to link inlet and outlet concentrations of each key pollutants across a single wetland. Using three statistical parameters, a critical evaluation of five potential models was made for vertical and horizontal flow wetlands. The results recommended the models that were developed based on Monod models, for predicting the removal of nitrogen and organics in a vertical and horizontal flow wetland system. No clear correlation was observed between influent BOD/COD values and kinetic coefficients of BOD(5) in VF and HF wetlands, illustrating that the removal of biodegradable organics was insensitive to the nature of organic matter. Higher effluent COD/TN values coincided with greater denitrification kinetic coefficients, signifying the dependency of denitrification on the availability of COD in VF wetland systems. In contrast, the trend was opposite in HF wetlands, indicating that availability of NO(3)-N was the main limiting step for nitrogen removal. Overall, the results suggested the possible application of the developed alternative predictive models, for understanding the complex biodegradation routes of nitrogen and organics removal in VF and HF wetland systems.

  19. Microbial growth modelling with artificial neural networks.

    Science.gov (United States)

    Jeyamkonda, S; Jaya, D S; Holle, R A

    2001-03-20

    There is a growing interest in modelling microbial growth as an alternative to time-consuming, traditional, microbiological enumeration techniques. Several statistical models have been reported to describe the growth of different microorganisms, but there are accuracy problems. An alternate technique 'artificial neural networks' (ANN) for modelling microbial growth is explained and evaluated. Published data were used to build separate general regression neural network (GRNN) structures for modelling growth of Aeromonas hydrophila, Shigella flexneri, and Brochothrix thermosphacta. Both GRNN and published statistical model predictions were compared against the experimental data using six statistical indices. For training data sets, the GRNN predictions were far superior than the statistical model predictions, whereas the GRNN predictions were similar or slightly worse than statistical model predictions for test data sets for all the three data sets. GRNN predictions can be considered good, considering its performance for unseen data. Graphical plots, mean relative percentage residual, mean absolute relative residual, and root mean squared residual were identified as suitable indices for comparing competing models. ANN can now become a vehicle whereby predictive microbiology can be applied in food product development and food safety risk assessment.

  20. Modeling the hydrological significance of wetland restoration scenarios.

    Science.gov (United States)

    Martinez-Martinez, Edwin; Nejadhashemi, A Pouyan; Woznicki, Sean A; Love, Bradley J

    2014-01-15

    Wetlands provide multiple socio-economic benefits, among them mitigating flood through short- and long-term water storage functions and assisting with reduction of downstream flood peaks. However, their effectiveness in controlling floods is dictated by wetland size and distribution within a watershed. Due to the complexity of wetland hydrological processes at the watershed scale, the Soil and Water Assessment Tool (SWAT) was used to study the impact of wetland restoration on streamflow rates and peaks in the Shiawassee River watershed of Michigan. Wetland restoration scenarios were developed based on combinations of wetland area (50, 100, 250, and 500 ha) and wetland depth (15, 30, 61, and 91 cm). Increasing wetland area, rather than depth, had a greater impact on long-term average daily streamflow. Wetland implementation resulted in negligible reductions in daily peak flow rates and frequency of peak flow events at the watershed outlet. In developing high impact areas for wetland restoration, similar locations were identified for reduction of subbasin and watershed outlet streamflow. However, the best combinations of area/depth differed depending on the goal of the restoration plan. Copyright © 2013 Elsevier Ltd. All rights reserved.

  1. 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.

  2. Artificial Spin-Ice and Vertex Models

    Science.gov (United States)

    Cugliandolo, Leticia F.

    2017-01-01

    In classical and quantum frustrated magnets the interactions in combination with the lattice structure impede the spins to order in optimal configurations at zero temperature. The theoretical interest in their classical realisations has been boosted by the artificial manufacture of materials with these properties, that are of flexible design. This note summarises work on the use of vertex models to study bidimensional spin-ices samples, done in collaboration with R. A. Borzi, M. V. Ferreyra, L. Foini, G. Gonnella, S. A. Grigera, P. Guruciaga, D. Levis, A. Pelizzola and M. Tarzia, in recent years. It is an invited contribution to a J. Stat. Mech. special issue dedicated to the memory of Leo P. Kadanoff.

  3. Modeling Modern Methane Emissions from Natural Wetlands. 2; Interannual Variations 1982-1993

    Science.gov (United States)

    Walter, Bernadette P.; Heimann, Martin; Mattews, Elaine; Hansen, James E. (Technical Monitor)

    2001-01-01

    A global run of a process-based methane model [Walter et al., this issue] is performed using high-frequency atmospheric forcing fields from ECMWF reanalyses of the period from 1982 to 1993. We calculate global annual methane emissions to be 260 Tg/ yr. 25% of methane emissions originate from wetlands north of 30 deg. N. Only 60% of the produced methane is emitted, while the rest is re-oxidized. A comparison of zonal integrals of simulated global wetland emissions and results obtained by an inverse modeling approach shows good agreement. In a test with data from two wetlands, the seasonality of simulated and observed methane emissions agrees well. The effects of sub-grid scale variations in model parameters and input data are examined. Modeled methane emissions show high regional, seasonal and interannual variability. Seasonal cycles of methane emissions are dominated by temperature in high latitude wetlands, and by changes in the water table in tropical wetlands. Sensitivity tests show that +/- 1 C changes in temperature lead to +/- 20 % changes in methane emissions from wetlands. Uniform changes of +/- 20% in precipitation alter methane emissions by about +/- 18%. Limitations in the model are analyzed. Simulated interannual variations in methane emissions from wetlands are compared to observed atmospheric growth rate anomalies. Our model simulation results suggest that contributions from other sources than wetlands and/or the sinks are more important in the tropics than north-of 30 deg. N. In higher northern latitudes, it seems that a large part, of the observed interannual variations can be explained by variations in wetland emissions. Our results also suggest that reduced wetland emissions played an important role in the observed negative methane growth rate anomaly in 1992.

  4. 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.

  5. Interaction model of artificial fish in virtual environment

    Institute of Scientific and Technical Information of China (English)

    Meng Xiangsong; Ban Xiaojuan; Yin Yixin

    2008-01-01

    Conventional artificial fish has some shortages on the interaction with environment,other fish,and the animator.This article proposes a multi-tier interaction control model of artificial fish,realizes the interaction model through integration of virtual reality technology and Markov sequence,and provides a virtual marine world to describe the interaction between artificial fish and the virtual environment and the interaction between the artificial fish and the animator.Simulation results show that the interaction model owns not only the basic characteristics of virtual biology,but also has high trueness interaction function.

  6. Geographically Isolated Wetlands and Catchment Hydrology: A Modified Model Analyses

    Science.gov (United States)

    Evenson, G.; Golden, H. E.; Lane, C.; D'Amico, E.

    2014-12-01

    Geographically isolated wetlands (GIWs), typically defined as depressional wetlands surrounded by uplands, support an array of hydrological and ecological processes. However, key research questions concerning the hydrological connectivity of GIWs and their impacts on downgradient surface waters remain unanswered. This is particularly important for regulation and management of these systems. For example, in the past decade United States Supreme Court decisions suggest that GIWs can be afforded protection if significant connectivity exists between these waters and traditional navigable waters. Here we developed a simulation procedure to quantify the effects of various spatial distributions of GIWs across the landscape on the downgradient hydrograph using a refined version of the Soil and Water Assessment Tool (SWAT), a catchment-scale hydrological simulation model. We modified the SWAT FORTRAN source code and employed an alternative hydrologic response unit (HRU) definition to facilitate an improved representation of GIW hydrologic processes and connectivity relationships to other surface waters, and to quantify their downgradient hydrological effects. We applied the modified SWAT model to an ~ 202 km2 catchment in the Coastal Plain of North Carolina, USA, exhibiting a substantial population of mapped GIWs. Results from our series of GIW distribution scenarios suggest that: (1) Our representation of GIWs within SWAT conforms to field-based characterizations of regional GIWs in most respects; (2) GIWs exhibit substantial seasonally-dependent effects upon downgradient base flow; (3) GIWs mitigate peak flows, particularly following high rainfall events; and (4) The presence of GIWs on the landscape impacts the catchment water balance (e.g., by increasing groundwater outflows). Our outcomes support the hypothesis that GIWs have an important catchment-scale effect on downgradient streamflow.

  7. 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...

  8. Mathematical model for analysis of recirculating vertical flow constructed wetlands.

    Science.gov (United States)

    Sklarz, Menachem Y; Gross, Amit; Soares, M Ines M; Yakirevich, Alexander

    2010-03-01

    The recirculating vertical flow constructed wetland (RVFCW) was developed for the treatment of domestic wastewater (DWW). In this system, DWW is applied to a vertical flow bed through which it trickles into a reservoir located beneath the bed. It is then recirculated back to the root zone of the bed. In this study, a compartmental model was developed to simulate the RVFCW. The model, which addresses transport and removal kinetics of total suspended solids, 5-day biological oxygen demand and nitrogen, was fitted to kinetical results obtained from pilot field setups and a local sensitivity analysis was performed on the model parameters and operational conditions. This analysis showed that after 5h of treatment water quality is affected more by stochastic events than by the model parameter values, emphasizing the stability of the RVFCW system to large variations in operational conditions. Effluent quality after 1h of treatment, when the sensitivity analysis showed the parameter impacts to be largest, was compared to model predictions. The removal rate was found to be dependent on the recirculation rate. The predictions correlated well with experimental observations, leading to the conclusion that the proposed model is a satisfactory tool for studying RVFCWs. Copyright 2009 Elsevier Ltd. All rights reserved.

  9. Building an Artificial Idiotopic Immune Model Based on Artificial Neural Network Ideology

    Directory of Open Access Journals (Sweden)

    Hossam Meshref

    2013-01-01

    Full Text Available In the literature, there were many research efforts that utilized the artificial immune networks to model their designed applications, but they were considerably complicated, and restricted to a few areas that such as computer security applications. The objective of this research is to introduce a new model for artificial immune networks that adopts features from other biological successful models to overcome its complexity such as the artificial neural networks. Common concepts between the two systems were investigated to design a simple, yet a robust, model of artificial immune networks. Three artificial neural networks learning models were available to choose from in the research design: supervised, unsupervised, and reinforcement learning models. However, it was found that the reinforcement model is the most suitable model. Research results examined network parameters, and appropriate relations between concentration ranges and their dependent parameters as well as the expected reward during network learning. In conclusion, it is recommended the use of the designed model by other researchers in different applications such as controlling robots in hazardous environment to save human lives as well as using it on image retrieval in general to help the police department identify suspects.

  10. 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.

  11. 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...

  12. Credit Scoring Model Hybridizing Artificial Intelligence with Logistic Regression

    Directory of Open Access Journals (Sweden)

    Han Lu

    2013-01-01

    Full Text Available Today the most commonly used techniques for credit scoring are artificial intelligence and statistics. In this paper, we started a new way to use these two kinds of models. Through logistic regression filters the variables with a high degree of correlation, artificial intelligence models reduce complexity and accelerate convergence, while these models hybridizing logistic regression have better explanations in statistically significance, thus improve the effect of artificial intelligence models. With experiments on German data set, we find an interesting phenomenon defined as ‘Dimensional interference’ with support vector machine and from cross validation it can be seen that the new method gives a lot of help with credit scoring.

  13. 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.

  14. 2014 Report: Wetland State-and-transition Model Project

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — Report from the 2014 field season of the Wetland State-and-Transition Project. Many National Wildlife Refuges in the Intermountain West and Prairie Pothole regions...

  15. Robust Modeling of Greenhouse Gas (GHG) Fluxes from Coastal Wetland Ecosystems

    Science.gov (United States)

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

    2014-12-01

    Many critical wetland biogeochemical processes are still largely unknown or poorly understood at best. Yet, available models for predicting wetland greenhouse gas (GHG) fluxes (e.g., CO2, CH4, and N2O) are generally mechanistic in nature. This knowledge gap leads to inappropriate process descriptions or over-parameterizations in existing mechanistic models, which often fail to provide accurate and robust predictions across time and space. We developed a systematic data-analytics and informatics method to identify the dominant controls and quantify the relative linkages of wetland GHG fluxes in relation to various hydro-climatic, sea level, biogeochemical and ecological drivers. The method was applied to data collected from 2012-14 through an extensive field campaign from different blue carbon sites of Waquoit Bay, MA. Multivariate pattern recognition techniques of principal component and factor analyses were employed to identify the dominant controls of wetland GHG fluxes; classifying and grouping process variables based on their similarity and interrelation patterns. Power-law based partial least squares regression models were developed to quantify the relative linkages of major GHGs with different process drivers and stressors, as well as to achieve site-specific predictions of GHG fluxes. Wetland biogeochemical similitude and scaling laws were also investigated to unravel emergent patterns and organizing principles of wetland GHG fluxes. The research findings will guide the development of parsimonious empirical to appropriate mechanistic models for spatio-temporally robust predictions of GHGs fluxes and carbon sequestration from coastal wetland ecosystems. The research is part of two current projects funded by the National Oceanic and Atmospheric Administration and the National Science Foundation; focusing on wetland data collections, knowledge formation, formulation of robust GHGs prediction models, and development of ecological engineering tools.

  16. Case Study: Sensitivity Analysis of the Barataria Basin Barrier Shoreline Wetland Value Assessment Model

    Science.gov (United States)

    2014-07-01

    Barrier Shoreline Wetland Value Assessment Model1 by S. Kyle McKay2 and J. Craig Fischenich3 OVERVIEW: Sensitivity analysis is a technique for...relevance of questions posed during an Independent External Peer Review (IEPR). BARATARIA BASIN BARRIER SHORELINE (BBBS) STUDY: On average...scale restoration projects to reduce marsh loss and maintain these wetlands as healthy functioning ecosystems. The Barataria Basin Barrier Shoreline

  17. 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.

  18. Model estimation of land-use effects on water levels of northern prairie wetlands.

    Science.gov (United States)

    Voldseth, Richard A; Johnson, W Carter; Gilmanov, Tagir; Guntenspergen, Glenn R; Millett, Bruce V

    2007-03-01

    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.

  19. Systems modeling to improve the hydro-ecological performance of diked wetlands

    Science.gov (United States)

    Alminagorta, Omar; Rosenberg, David E.; Kettenring, Karin M.

    2016-09-01

    Water scarcity and invasive vegetation threaten arid-region wetlands and wetland managers seek ways to enhance wetland ecosystem services with limited water, labor, and financial resources. While prior systems modeling efforts have focused on water management to improve flow-based ecosystem and habitat objectives, here we consider water allocation and invasive vegetation management that jointly target the concurrent hydrologic and vegetation habitat needs of priority wetland bird species. We formulate a composite weighted usable area for wetlands (WU) objective function that represents the wetland surface area that provides suitable water level and vegetation cover conditions for priority bird species. Maximizing the WU is subject to constraints such as water balance, hydraulic infrastructure capacity, invasive vegetation growth and control, and a limited financial budget to control vegetation. We apply the model at the Bear River Migratory Bird Refuge on the Great Salt Lake, Utah, compare model-recommended management actions to past Refuge water and vegetation control activities, and find that managers can almost double the area of suitable habitat by more dynamically managing water levels and managing invasive vegetation in August at the beginning of the window for control operations. Scenario and sensitivity analyses show the importance to jointly consider hydrology and vegetation system components rather than only the hydrological component.

  20. WETSYS, a dynamic system model to assess trade-off between wetland ecosystem services at local level

    OpenAIRE

    Morardet, S.; Masiyandima, M.; Vasilishina, O.

    2012-01-01

    This report presents an integrated dynamic simulation model that represents wetland functioning. The model can be used for analysing trade-offs between the provision of ecosystem services and ecosystem integrity and resulting land use changes in the Ga-Mampa wetland in the Limpopo basin in South Africa. The purpose of the analysis is to generate knowledge that can assist decision-makers and local communities in managing wetland ecosystems in a sustainable manner. The model was developed using...

  1. 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

  2. Simulation and Prediction of Water Allocation Using Artificial Neural Networks and a Spatially Distributed Hydrological Model

    Directory of Open Access Journals (Sweden)

    A. Papagera

    2014-12-01

    Full Text Available Lake Koronia is located in the North part of Greece and is protected by the Ramsar Convention of wetlands. A deficit in the water balance has been presented at the last twenty years due to the excessive water consumption for agricultural uses. This research is an attempt to simulate water flow with MIKE SHE model in order to observe how the water is allocated in the study area. The results of water flow module used for the estimation of Lake’s water balance for 4 hydrological years (2008-2012. Furthermore the Artificial Neural Networks (ANNs was used for the prediction of water flow in two sub-catchments. The coefficient correlation (R was found for Bogdanas (0.9 and Kolxikos (0.86. The Root Mean Square Error (RMSE and the Mean Absolute Percentages Error (MAPE were also calculated in order to evaluate the quality of the ANNs results.

  3. 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.

  4. Wetland ecosystem health assessment through integrating remote sensing and inventory data with an assessment model for the Hangzhou Bay, China.

    Science.gov (United States)

    Sun, Tengteng; Lin, Wenpeng; Chen, Guangsheng; Guo, Pupu; Zeng, Ying

    2016-10-01

    Due to rapid urbanization, industrialization and population growth, wetland area in China has shrunk rapidly and many wetland ecosystems have been reported to degrade during recent decades. Wetland health assessment could raise the public awareness of the wetland condition and guide policy makers to make reasonable and sustainable policies or strategies to protect and restore wetland ecosystems. This study assessed the health levels of wetland ecosystem at the Hangzhou Bay, China using the pressure-state-response (PSR) model through synthesizing remote sensing and statistical data. Ten ecological and social-economic indicators were selected to build the wetland health assessment system. Weights of these indicators and PSR model components as well as the normalized wetland health score were assigned and calculated based on the analytic hierarchy process (AHP) method. We analyzed the spatio-temporal changes in wetland ecosystem health status during the past 20years (1990-2010) from the perspectives of ecosystem pressure, state and response. The results showed that the overall wetland health score was in a fair health level, but displayed large spatial variability in 2010. The wetland health score declined from good health level to fair health level from 1990 to 2000, then restored slightly from 2000 to 2010. Overall, wetland health levels showed a decline from 1990 to 2010 for most administrative units. The temporal change patterns in wetland ecosystem health varied significantly among administrative units. Our results could help to clarify the administrative responsibilities and obligations and provide scientific guides not only for wetland protection but also for restoration and city development planning at the Hangzhou Bay area.

  5. A model of depressional wetland formation in low-relief karst landscapes

    Science.gov (United States)

    Heffernan, J. B.; Murray, A. B.; Cohen, M. J.; Martin, J. B.; Mclaughlin, D. L.; Bianchi, T. S.; Watts, A.

    2014-12-01

    Karst landscapes are formed by the self-reinforcing dissolution of limestone and other soluble rocks, and these positive feedbacks can create a variety of landforms depending on initial topography, climate, bedrock characteristics, and potentially, the activity of biota. In Big Cypress National Preserve (BICY), a low-relief karst landscape in southwestern FL (USA), depressional wetlands, are interspersed within an upland matrix in a regular pattern. This landscape is characterized by over-dispersion of wetland patches, periodic variation in bedrock depth and soil thickness, and distinct bi-modality of these and other soil properties. We hypothesize that the structure of the BICY landscape reflects the concurrent effects of local positive feedbacks among hydroperiod, vegetation productivity and bedrock dissolution; these local processes may ultimately be constrained by landscape scale limitations of water volume. We further hypothesize that low relief and shallow water tables are essential boundary conditions for the emergence of regular patterning of wetlands. To explore these hypotheses, we have developed a quasi-spatial model of a single nascent wetland and its catchment, where the expansion of the wetland basin is driven by acidity associated with belowground root production and aquatic metabolism and their effects on carbonate mineral dissolution, and by the lateral and vertical discharge of water between wetlands and bedrock porosity. This model can, depending on boundary conditions, recreate a range of karst features, including vertical dissolution holes, extensive wetlands that overtake the entire basin, or smaller wetlands whose size equilibrates at a small proportion of the catchment area. This last endpoint, a landform similar to those observed in BICY, occurs only in response to relatively shallow water tables, limited hydrologic inputs, and strong positive feedbacks of biotic activity on dissolution.

  6. 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.

  7. A hydrological model for the Sudd wetland using remotely sensed and ground data

    Science.gov (United States)

    Remondi, Federica; Georgakakos, Aris P.; Castelletti, Andrea

    2013-04-01

    Modeling of wetland hydrology and quantification of water inputs and outputs are requisites to understand flooding dynamics, to determine wetland vulnerability to change, and to better inform water-related decision-making. Located in the Upper Nile river basin in South Sudan, the Sudd wetland is one of the largest floodplain swamps in the world. Its complex system is characterized by a seasonal inundation that is essential to the hydroecological functioning of the Sudd but is also the main cause for intensive water losses (nearly half of the inflow) by evaporation in the Nile river basin. The hydrologically characterization of the area is therefore key to assess and predict the water balance in the region The main difficulties in modeling the system are due to the inaccessibility of the area, to the vast extension, to the complexity of the dynamic behavior throughout the year (permanent and seasonal flooded areas), and to the political and institutional setting. This study integrated hydrologic data and remote sensing techniques to analyze the dynamics and spatial response of the wetlands. A new methodology using MODIS data and MNDWI-Modified Normalized Difference Water Index was designed to profile the area of the wetland throughout the years. In particular, the threshold for the MNDWI values was obtained using average annual land cover data and their temporal trends were analyzed to classify the different types of wetland (permanent, seasonal and non-wetland). A characterization of wetland dynamics was then achieved over the 10-years period Jan 2000-Dec 2009. In the second step of the research, other driving forces of the system were studied: new hydrological models were created for the Torrents and Sobat basins, existing river routing models were computed for the reach of Mongalla and Malakal, and estimates on precipitation and evapotranspiration rates were acquired from different projects based on remotely sensed data. All these information were then used to

  8. A Suitable Artificial Intelligence Model for Inventory Level Optimization

    Directory of Open Access Journals (Sweden)

    Tereza Sustrova

    2016-05-01

    Full Text Available Purpose of the article: To examine suitable methods of artificial neural networks and their application in business operations, specifically to the supply chain management. The article discusses construction of an artificial neural networks model that can be used to facilitate optimization of inventory level and thus improve the ordering system and inventory management. For the data analysis from the area of wholesale trade with connecting material is used. Methodology/methods: Methods used in the paper consists especially of artificial neural networks and ANN-based modelling. For data analysis and preprocessing, MS Office Excel software is used. As an instrument for neural network forecasting MathWorks MATLAB Neural Network Tool was used. Deductive quantitative methods for research are also used. Scientific aim: The effort is directed at finding whether the method of prediction using artificial neural networks is suitable as a tool for enhancing the ordering system of an enterprise. The research also focuses on finding what architecture of the artificial neural networks model is the most suitable for subsequent prediction. Findings of the research show that artificial neural networks models can be used for inventory management and lot-sizing problem successfully. A network with the TRAINGDX training function and TANSIG transfer function and 6-8-1 architecture can be considered the most suitable for artificial neural network, as it shows the best results for subsequent prediction.. Conclusions resulting from the paper are beneficial for further research. It can be concluded that the created model of artificial neural network can be successfully used for predicting order size and therefore for improving the order cycle of an enterprise.

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

    Science.gov (United States)

    Bohn, T. J.; Melton, J. R.; Ito, A.; Kleinen, T.; Spahni, R.; Stocker, B. D.; Zhang, B.; Zhu, X.; Schroeder, R.; Glagolev, M. V.; Maksyutov, S.; Brovkin, V.; Chen, G.; Denisov, S. N.; Eliseev, A. V.; Gallego-Sala, A.; McDonald, K. C.; Rawlins, M. A.; Riley, W. J.; Subin, Z. M.; Tian, H.; Zhuang, Q.; Kaplan, J. O.

    2015-06-01

    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 (inundation or air

  10. 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...

  11. Artificial wetlands as tools for frog conservation: stability and variability of reproduction characteristics in Sahara frog populations in Tunisian man-made lakes.

    Science.gov (United States)

    Bellakhal, Meher; Neveu, André; Fertouna-Bellakhal, Mouna; Aleya, Lotfi

    2017-09-27

    Amphibian populations are in decline principally due to climate change, environmental contaminants, and the reduction in wetlands. Even though data concerning current population trends are scarce, artificial wetlands appear to play a vital role in amphibian conservation. This study concerns the reproductive biology of the Sahara frog over a 2-year period in four Tunisian man-made lakes. Each month, gonad state (parameters: K, GSI, LCI), fecundity, and fertility of females (using 1227 clutches) were evaluated in the field under controlled conditions. Clutches were present for 110-130 days at two of the sites, but only for 60-80 days at the other two. Maximum egg laying occurred in May, corresponding to the highest point in the gonad somatic index. Clutch densities were higher in the smaller lakes. Female fecundity was in relation to body size; mean clutch fecundity attained 1416 eggs, with no differences observed according to site. Egg fertility varied over a 1-year period, with a maximum in May followed by a decrease when water temperature was at its highest. Eggs were smaller at the beginning of spawning; maximum size was in May, which might explain the higher fertility, but no maternal influence was detected. Embryonic development was strictly dependent on temperature. The population at each site appeared as a small patch within a metapopulation in overall good health, as shown by the relative temporal stability in reproduction variables. Constructed wetlands may therefore play an important role in the conservation of amphibians, especially in semi-arid zones.

  12. Using CV-GLUE procedure in analysis of wetland model predictive uncertainty.

    Science.gov (United States)

    Huang, Chun-Wei; Lin, Yu-Pin; Chiang, Li-Chi; Wang, Yung-Chieh

    2014-07-01

    This study develops a procedure that is related to Generalized Likelihood Uncertainty Estimation (GLUE), called the CV-GLUE procedure, for assessing the predictive uncertainty that is associated with different model structures with varying degrees of complexity. The proposed procedure comprises model calibration, validation, and predictive uncertainty estimation in terms of a characteristic coefficient of variation (characteristic CV). The procedure first performed two-stage Monte-Carlo simulations to ensure predictive accuracy by obtaining behavior parameter sets, and then the estimation of CV-values of the model outcomes, which represent the predictive uncertainties for a model structure of interest with its associated behavior parameter sets. Three commonly used wetland models (the first-order K-C model, the plug flow with dispersion model, and the Wetland Water Quality Model; WWQM) were compared based on data that were collected from a free water surface constructed wetland with paddy cultivation in Taipei, Taiwan. The results show that the first-order K-C model, which is simpler than the other two models, has greater predictive uncertainty. This finding shows that predictive uncertainty does not necessarily increase with the complexity of the model structure because in this case, the more simplistic representation (first-order K-C model) of reality results in a higher uncertainty in the prediction made by the model. The CV-GLUE procedure is suggested to be a useful tool not only for designing constructed wetlands but also for other aspects of environmental management.

  13. 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,...

  14. 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

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

    Directory of Open Access Journals (Sweden)

    L. Meng

    2011-06-01

    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 is 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. 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, 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. We expect this latter number may be an underestimate due to the low high-latitude inundated area captured by satellites and unrealistically low high-latitude productivity and soil carbon predicted by CLM4. Sensitivity analysis showed a large range (150–346 Tg CH4 yr−1 in

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

    Science.gov (United States)

    Wu, Qiusheng; Lane, Charles R.

    2017-07-01

    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 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.

  17. Water quality, fate of metals and predictive model validation of a constructed wetland treating acid mine drainage

    Energy Technology Data Exchange (ETDEWEB)

    Mitsch, W.J.; Wise, K.M. [Ohio State University, Columbus, OH (United States). School of Natural Resources

    1998-06-01

    The paper describes how 0.39 ha constructed wetland designed with 9 cells, including two anaerobic cells that were to stimulate dissimilatory sulfate reduction, was evaluated for its effect on water quality of a low-order acid mine drainage (AMD) stream in southeastern Ohio, USA. Emphasis was on the uptake and fate of selected metals and the accuracy of a simulation model that predicted this specific wetland`s behavior before it was built.

  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. 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

  20. 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.

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

    Science.gov (United States)

    2011-03-24

    Shelley (Member) Date //signed// 10 Mar 2011 Robert Ritzi (Member) Wright State University Date iv AFIT/GES/ENV/11-M03...members; Dr. Michael Shelley, for his invaluable insights into past research of the treatment wetland at Wright Patterson AFB; and Dr. Robert Ritzi, for...Amon, J.P., A. Agrawal, M.L. Shelley, B.C. Opperman, M.P. Enright , N.D. Clemmer, T. Slusser, J. Lach, T. Sobolewski, W. Gruner, and A.C. Entingh

  2. Neuro-Based Artificial Intelligence Model for Loan Decisions

    Directory of Open Access Journals (Sweden)

    Shorouq F. Eletter

    2010-01-01

    Full Text Available 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 backpropagation learning algorithm was used to build up the proposed model. Results: Different representative cases of loan applications were considered based on the guidelines of different banks in Jordan, to validate the neural network model. Conclusion: The results indicated that artificial neural networks are a successful technology that can be used in loan application evaluation in the Jordanian commercial banks.

  3. Impulsive Neural Networks Algorithm Based on the Artificial Genome Model

    Directory of Open Access Journals (Sweden)

    Yuan Gao

    2014-05-01

    Full Text Available To describe gene regulatory networks, this article takes the framework of the artificial genome model and proposes impulsive neural networks algorithm based on the artificial genome model. Firstly, the gene expression and the cell division tree are applied to generate spiking neurons with specific attributes, neural network structure, connection weights and specific learning rules of each neuron. Next, the gene segment duplications and divergence model are applied to design the evolutionary algorithm of impulsive neural networks at the level of the artificial genome. The dynamic changes of developmental gene regulatory networks are controlled during the whole evolutionary process. Finally, the behavior of collecting food for autonomous intelligent agent is simulated, which is driven by nerves. Experimental results demonstrate that the algorithm in this article has the evolutionary ability on large-scale impulsive neural networks

  4. Artificial Neural Networks for Modeling Knowing and Learning in Science.

    Science.gov (United States)

    Roth, Wolff-Michael

    2000-01-01

    Advocates artificial neural networks as models for cognition and development. Provides an example of how such models work in the context of a well-known Piagetian developmental task and school science activity: balance beam problems. (Contains 59 references.) (Author/WRM)

  5. Artificial Neural Networks for Modeling Knowing and Learning in Science.

    Science.gov (United States)

    Roth, Wolff-Michael

    2000-01-01

    Advocates artificial neural networks as models for cognition and development. Provides an example of how such models work in the context of a well-known Piagetian developmental task and school science activity: balance beam problems. (Contains 59 references.) (Author/WRM)

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

    Science.gov (United States)

    Wingard, Georgiana L.; 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.

  8. Modeling methane fluxes in wetlands with gas-transporting plants. 3. Plot scale.

    NARCIS (Netherlands)

    Segers, R.; Leffelaar, P.A.

    2001-01-01

    A process model based on kinetic principles was developed for methane fluxes from wetlands with gas-transporting plants and a fluctuating water table. Water dynamics are modeled with the 1-D Richards equation. For temperature a standard diffusion equation is used. The depth-dependent dynamics of met

  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. Modelling water flow and seasonal soil moisture dynamics in analluvial groundwater-fed wetland

    Directory of Open Access Journals (Sweden)

    I. Joris

    2003-01-01

    Full Text Available Complex interactions occur in riparian wetlands between groundwater, surface water and climatic conditions. Knowledge of the hydrology of these systems is necessary to understand their functioning and their value and models are a useful and probably essential tool to capture their hydrological complexity. In this study, a 2D-model describing saturated-unsaturated water flow is applied to a transect through a groundwater-fed riparian wetland located along the middle reach of the river Dijle. The transect has high levees close to the river and a depression further into the floodplain. Scaling factors are introduced to describe the variability of soil hydraulic properties along the transect. Preliminary model calculations for one year show a good agreement between model calculations and measurements and demonstrate the capability of the model to capture the internal groundwater dynamics. Seasonal variations in soil moisture are reproduced well by the model thus translating external hydrological boundary conditions to root zone conditions. The model proves to be a promising tool for assessing effects of changes in hydrological boundary conditions on vegetation type distribution and to gain more insight in the highly variable internal flow processes of riparian wetlands. Keywords: riparian wetland,eco-hydrology, upward seepage, floodplain hydrology

  11. 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.

  12. 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.

  13. Uncertainty analysis of statistical downscaling models using general circulation model over an international wetland

    Science.gov (United States)

    Etemadi, H.; Samadi, S.; Sharifikia, M.

    2014-06-01

    Regression-based statistical downscaling model (SDSM) is an appropriate method which broadly uses to resolve the coarse spatial resolution of general circulation models (GCMs). Nevertheless, the assessment of uncertainty propagation linked with climatic variables is essential to any climate change impact study. This study presents a procedure to characterize uncertainty analysis of two GCM models link with Long Ashton Research Station Weather Generator (LARS-WG) and SDSM in one of the most vulnerable international wetland, namely "Shadegan" in an arid region of Southwest Iran. In the case of daily temperature, uncertainty is estimated by comparing monthly mean and variance of downscaled and observed daily data at a 95 % confidence level. Uncertainties were then evaluated from comparing monthly mean dry and wet spell lengths and their 95 % CI in daily precipitation downscaling using 1987-2005 interval. The uncertainty results indicated that the LARS-WG is the most proficient model at reproducing various statistical characteristics of observed data at a 95 % uncertainty bounds while the SDSM model is the least capable in this respect. The results indicated a sequences uncertainty analysis at three different climate stations and produce significantly different climate change responses at 95 % CI. Finally the range of plausible climate change projections suggested a need for the decision makers to augment their long-term wetland management plans to reduce its vulnerability to climate change impacts.

  14. A Simple Harmonic Model for FAPAR Temporal Dynamics in the Wetlands of the Volga-Akhtuba Floodplain

    Directory of Open Access Journals (Sweden)

    Alexander Kozlov

    2016-09-01

    Full Text Available The paper reports a technique used to construct a reference time series for the fraction of absorbed photosynthetically-active radiation (FAPAR based on remotely-sensed data in the largest Russian arid wetland territory. For the arid Volga-Akhtuba wetlands, FAPAR appears to be an informative spectral index for estimating plant cover health and its seasonal and annual dynamics. Since FAPAR algorithms are developed for multiple satellite sensors, all FAPAR-based models are suggested to be universal and useful for future studies and long-term monitoring of plant cover, particularly in wetlands. The model developed in the present work for FAPAR temporal dynamics clearly reflects the field-observed seasonal and annual changes of plant cover in the Volga-Akhtuba floodplain wetlands. Various types of wetland plant communities were categorized by the specific parameters of the model seasonal vegetation curve. In addition, the values derived from the model function allow quantitative estimation of wetland plant cover health. This information is particularly important for the Volga-Akhtuba floodplain, because its hydrological regime is regulated by the Volzhskaya hydropower plant. The ecosystem is extremely fragile and sensitive to human impact, and wetland plant cover health is a key indicator of regulatory efficiency. The present study is another step towards developing a methodology focused on arid wetland vegetation monitoring and conservation of its biodiversity and natural conditions.

  15. A review on numerous modeling approaches for effective, economical and ecological treatment wetlands.

    Science.gov (United States)

    Kumar, J L G; Zhao, Y Q

    2011-03-01

    Constructed wetlands (CWs) for wastewater treatment have evolved substantially over the last decades and have been recognized as an effective means of "green technology" for wastewater treatment. This paper reviews the numerous modeling approaches ranging from simple first-order models to more complex dynamic models of treatment behaviour in CWs. The main objective of the modeling work is to better understand the process in CWs and optimize design criteria. A brief study in this review discusses the efforts taken to describe the process-based model for the efficient removal of pollutants in CWs. Obtaining better insights is essential to understand the hydraulic and biochemical processes in CWs. Currently, employed modeling approaches can be seen in two categories, i.e. "black-box models" and "process-based models". It is evident that future development in wetland technology will depend on improved scientific knowledge of internal treatment mechanisms.

  16. Pegylated polystyrene particles as a model system for artificial cells

    NARCIS (Netherlands)

    Meng, Fenghua; Engbers, Gerard H.M.; Gessner, Andrea; Müller, Reiner H.; Feijen, Jan

    2004-01-01

    Pegylated polystyrene particles (PS-PEG) were prepared as a model system for artificial cells, by modification of carboxyl polystyrene particles (PS-COOH) with homo- and hetero-bifunctional polyethylene glycols (PEG, MW 1500, 3400, and 5000) containing an amino end group for immobilization and an am

  17. 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…

  18. 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…

  19. Using structural equation modeling to link human activities to wetland ecological integrity

    Science.gov (United States)

    Schweiger, E. William; Grace, James B.; Cooper, David; Bobowski, Ben; Britten, Mike

    2016-01-01

    The integrity of wetlands is of global concern. A common approach to evaluating ecological integrity involves bioassessment procedures that quantify the degree to which communities deviate from historical norms. While helpful, bioassessment provides little information about how altered conditions connect to community response. More detailed information is needed for conservation and restoration. We have illustrated an approach to addressing this challenge using structural equation modeling (SEM) and long-term monitoring data from Rocky Mountain National Park (RMNP). Wetlands in RMNP are threatened by a complex history of anthropogenic disturbance including direct alteration of hydrologic regimes; elimination of elk, wolves, and grizzly bears; reintroduction of elk (absent their primary predators); and the extirpation of beaver. More recently, nonnative moose were introduced to the region and have expanded into the park. Bioassessment suggests that up to half of the park's wetlands are not in reference condition. We developed and evaluated a general hypothesis about how human alterations influence wetland integrity and then develop a specific model using RMNP wetlands. Bioassessment revealed three bioindicators that appear to be highly sensitive to human disturbance (HD): (1) conservatism, (2) degree of invasion, and (3) cover of native forbs. SEM analyses suggest several ways human activities have impacted wetland integrity and the landscape of RMNP. First, degradation is highest where the combined effects of all types of direct HD have been the greatest (i.e., there is a general, overall effect). Second, specific HDs appear to create a “mixed-bag” of complex indirect effects, including reduced invasion and increased conservatism, but also reduced native forb cover. Some of these effects are associated with alterations to hydrologic regimes, while others are associated with altered shrub production. Third, landscape features created by historical beaver

  20. Modeling of higher order systems using artificial bee colony algorithm

    Directory of Open Access Journals (Sweden)

    Aytekin Bağış

    2016-05-01

    Full Text Available In this work, modeling of the higher order systems based on the use of the artificial bee colony (ABC algorithm were examined. Proposed model parameters for the sample systems in the literature were obtained by using the algorithm, and its performance was presented comparatively with the other methods. Simulation results show that the ABC algorithm based system modeling approach can be used as an efficient and powerful method for higher order systems.

  1. 人工湿地污水处理技术在氯碱循环经济园区建设中的应用%Application of artificial wetlands wastewater treatment technology in Chlor-alkali circular economy region

    Institute of Scientific and Technical Information of China (English)

    唐琳

    2011-01-01

    通过对中泰化学阜康工业园污水污染负荷的分析及人工湿地处理与活性污泥法处理技术的比较,建议应用人工湿地处理污水技术就近处理厂前区生活废水。就潜流湿地系统设计参数的计算方法、湿地选址、填料的应用、水生植物的栽种、景观设计等应用问题提出建议。%Fukang,through China and Thailand chemical industrial park wastewater pollution load analysis,artificial wetland treatment and comparison of activated sludge treatment technology,recommends that the application of artificial wetland wastewater treatment technology wastewater treatment plant nearby area before life and determine the calculation method of design parameters of subsurface flow constructed wetland system,wetland site,packing,planting of aquatic plants,landscape design and application of field application issues,such as recommendations.

  2. A mechanistic model of microbial competition in the rhizosphere of wetland plants

    Science.gov (United States)

    Aslkhodapasand, F.; Mayer, K. U.; Neumann, R. B.

    2014-12-01

    Wetlands are the largest natural source of methane to the atmosphere. Although they cover only 4-6% of earth's surface, wetlands contribute 20-39% of global methane emissions. Hollow aerenchyma tissues inside the roots, stems and leaves of plants represent one of the most important methane emission pathways for wetlands. Up to 90% of the emitted methane can diffuse through these hollow tissues that directly connect the atmosphere to the anoxic soils where methane is generated. Thus, concentrations of methane surrounding plant roots directly impact the amount of methane emitted by wetlands. Methane concentrations are controlled by a variety of microbial processes occurring in the soil around the roots of plants (aka the rhizosphere). The rhizosphere is a microbial hotspot sustained by plant inputs of organic carbon and oxygen; plant roots exude excess organic carbon generated in photosynthesis into the rhizosphere and atmospheric oxygen diffuses down to the rhizosphere through the hollow aerenchyma tissues. This environment supports a variety of microbial communities that compete with each other for available carbon and oxygen, including methanogens, methanotrophs, and heterotrophs. Methanogens ferment organic carbon into methane, a reaction that is inhibited by oxygen; methanotrophs use oxygen to oxidize methane into carbon dioxide; and heterotrophs use oxygen to oxidize organic carbon into carbon dioxide. We are interested in understanding how competition between these communities alters methane concentrations and responds to variations in plant inputs. To this end, we have developed a mechanistic root-scale model that describes microbial competition for organic carbon and oxygen in the rhizosphere of wetland plants. Our results focus on variations in rates of methane production, methane oxidation, heterotrophic respiration, and diffusion of methane into plant roots as a result of changes in carbon and oxygen inputs. The study provides insight into how plant

  3. 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.

  4. Model-based design of horizontal subsurface flow constructed treatment wetlands: a review.

    Science.gov (United States)

    Rousseau, Diederik P L; Vanrolleghem, Peter A; De Pauw, Niels

    2004-03-01

    The increasing application of constructed wetlands for wastewater treatment coupled with increasingly strict water quality standards is an ever growing incentive for the development of better process design tools. This paper reviews design models for horizontal subsurface flow constructed treatment wetlands, ranging from simple rules of thumb and regression equations, to the well-known first-order k-C* models, Monod-type equations and more complex dynamic, compartmental models. Especially highlighted in this review are the model constraints and parameter uncertainty. A case study has been used to demonstrate the model output variability and to unravel whether or not more complex but also less manageable models offer a significant advantage to the designer.

  5. 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.

  6. modeling of modeling of reservoir in reservoir in artificial neu ...

    African Journals Online (AJOL)

    eobe

    1 ,4 DEPARTMENT OF C. 2NATIONAL CENTRE FOR ... w, Hydropower dams, Hydro-meteorological variables, Artificial Neura eed correct .... flood and average rainfall value for two year return period using ANN ..... Australia. pp. 1099-1105.

  7. Monitoring and modeling of wetland environment using time-series bi-sensor remotely sensed data

    Science.gov (United States)

    Michishita, Ryo

    More than half of the wetlands in the world have been lost in the last century mainly due to human activities. Since natural wetlands receive a significant amount of untreated runoff from urban and agricultural areas, it is necessary to account for other landscapes adjacent to wetlands, such as water bodies, agricultural areas, and urban areas, in the protection and restoration of the wetlands. The goal of this dissertation is to monitor and model land cover changes using the time-series Landsat-5 TM and Terra MODIS data in the Poyang Lake area of China from two perspectives: wetland cover changes and urbanization. A bi-scale monitoring approach was adopted in the monitoring and modeling of wetland cover changes to examine the similarities and differences derived from remotely sensed imagery with different spatial resolutions. The effect of different modeling settings of multiple endmember spectral mixture analysis (MESMA) were examined utilizing a single pair of TM and MODIS scenes. MESMA applied to nine pairs of TM and MODIS scenes acquired from July 2004 to October 2005 captured phenological and hydrological trends of land cover fractions (LCFs) and LCF agreement between the image pairs. Ground surface reflectance, rather than LCFs, was chosen as the key parameter in the blending of bi-scale remotely sensed data that utilized the spatial details of one data type and temporal details of the other. This research customized an existing fusion model to overcome the problem with the unobserved pixels in MODIS data acquired on TM data acquisition dates. It is interesting that the input data combination considering water level change achieved higher accuracy. In the monitoring of urbanization, this research investigated the relationship between urban land cover and human activities, and detected the areas of new urban development and redevelopment of built-up areas. Different urbanization processes largely influenced by the economic reforms of China were demonstrated

  8. FGD liner experiments with wetlands

    Energy Technology Data Exchange (ETDEWEB)

    Mitsch, W.J.; Ahn, C.; Wolfe, W.E.

    1999-07-01

    The construction of artificial wetlands for wastewater treatment often requires impermeable liners not only to protect groundwater resources but also to ensure that there is adequate water in the wetland to support appropriate aquatic life, particularly wetland vegetation. Liners or relatively impervious site soils are very important to the success of constructed treatment wetlands in areas where ground water levels are typically close to the ground surface. This study, carried out at the Olentangy River Wetland Research Park, investigated the use of FGD material from sulfur scrubbers as a possible liner material for constructed wetlands. While several studies have investigated the use of FGD material to line ponds, no studies have investigated the use of this material as a liner for constructed wetlands. They used experimental mesocosms to see the effect of FGD liner materials in constructed wetlands on water quality and on wetland plant growth. This paper presents the results of nutrient analyses and physicochemical investigation of leachate and surface outflow water samples collected from the mesocosms. Plant growth and biomass of wetland vegetation are also included in this paper. First two year results are reported by Ahn et al. (1998, 1999). The overall goal of this study is the identification of advantages and disadvantages of using FGD by-product as an artificial liner in constructed wetlands.

  9. 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.

  10. Uncertainties in modeling 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

    2009-03-01

    Full Text Available Methane (CH4 fluxes from northern wetlands may have influenced atmospheric CH4 concentrations at climate warming phases during the 800 000 years and at 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 been undertaken previously 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 different glacial climates and the present climate. This paper displays results on the sensitivity of modeling glacial terrestrial CH4 fluxes to basic tuning parameters of the model, to different approaches in modeling of the water table, and to model structure. For testing the model structure, PEATLAND-VU has been compared to a simpler modeling approach based on wetland primary production estimated from a vegetation model (BIOME. 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, and sensitive to microbial parameters and model structure. Glacial climate emissions are also highly sensitive to assumptions on the extent of ice cover and exposed seafloors. Wetland expansion on low relief exposed seafloor areas, may have compensated for a decrease of wetland area due to continental ice cover.

  11. Integrated Analysis of Interferometric SAR, Satellite Altimetry and Hydraulic Modeling to Quantify Louisiana Wetland Dynamics

    Science.gov (United States)

    Lee, Hyongki; Kim, Jin-woo; Lu, Zhong; Jung, Hahn Chul; Shum, C. K.; Alsdorf, Doug

    2012-01-01

    Wetland loss in Louisiana has been accelerating due primarily to anthropogenic and nature processes, and is being advocated as a problem with national importance. Accurate measurement or modeling of wetland-wide water level changes, its varying extent, its storage and discharge changes resulting in part from sediment loads, erosion and subsidence are fundamental to assessment of hurricane-induced flood hazards and wetland ecology. Here, we use innovative method to integrate interferometric SAR (InSAR) and satellite radar altimetry for measuring absolute or geocentric water level changes and applied the methodology to remote areas of swamp forest in coastal Louisiana. Coherence analysis of InSAR pairs suggested that the HH polarization is preferred for this type of observation, and polarimetric analysis can help to identi:fy double-bonnce backscattering areas in the wetland. Envisat radar altimeter-measured 18- Hz (along-track sampling of 417 m) water level data processed with regional stackfile method have been used to provide vertical references for water bodies separated by levees. The high-resolution (approx.40 m) relative water changes measured from ALOS PALSAR L-band and Radarsat-l C-band InSAR are then integrated with Envisat radar altimetry to obtain absolute water level. The resulting water level time series were validated with in situ gauge observations within the swamp forest. Furthermore, we compare our water elevation changes with 2D flood modeling from LISFLOOD hydrodynamic model. Our study demonstrates that this new technique allows retrospective reconstruction and concurrent monitoring of water conditions and flow dynamics in wetlands, especially those lacking gauge networks.

  12. Effects of Different Vegetation Zones on CH4 and N2O Emissions in Coastal Wetlands: A Model Case Study

    Directory of Open Access Journals (Sweden)

    Yuhong Liu

    2014-01-01

    Full Text Available The coastal wetland ecosystems are important in the global carbon and nitrogen cycle and global climate change. For higher fragility of coastal wetlands induced by human activities, the roles of coastal wetland ecosystems in CH4 and N2O emissions are becoming more important. This study used a DNDC model to simulate current and future CH4 and N2O emissions of coastal wetlands in four sites along the latitude in China. The simulation results showed that different vegetation zones, including bare beach, Spartina beach, and Phragmites beach, produced different emissions of CH4 and N2O in the same latitude region. Correlation analysis indicated that vegetation types, water level, temperature, and soil organic carbon content are the main factors affecting emissions of CH4 and N2O in coastal wetlands.

  13. 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...... and a continuous glucose monitor (CGM). Despite these technological advances in the treatment of type 1 diabetes, the disease still poses an enormous and constant challenge for the patients. To obtain tight glucose control the patients are required to assess how much they will eat prior to the meal. They have......, the control algorithm computes the optimal dose adjustment and sends instructions to the insulin pump. To develop control algorithms, mathematical models of the physiological dynamics are needed. They attempt to describe the significant dynamics of the system and hence they approximate the system behavior...

  14. Stochastic Modeling of Isolated Wetland Hydrologic Variability: Effects of Hydro-climatic Forcing, Wetland Bathymetry, and Groundwater-Surface Water Connectivity

    Science.gov (United States)

    Park, Jeryang; Botter, Gianluca; Jawitz, Jim; Rao, Suresh

    2014-05-01

    Hydrological regimes regulate many wetland eco-hydrological functions, such as aquatic habitat integrity and biogeochemical processes. We examined hydrologic temporal variability of geographically isolated wetlands (GIWs), and derived analytical expressions for probability density functions (pdfs) of water storage volume, water stage, and water surface area. We conceptualize a GIW as a non-linear reservoir, subject to stochastic "shot-noise" (Poisson rainfall inputs) modulated by recession through evapotranspiration and drainage during inter-event periods. The derived analytical pdfs are defined by three dimensionless parameters: scaled aridity index; mean daily stage increment (during rainfall events); and wetland shape coefficient. These key parameters define the similarity or diversity of hydrologic regimes of different GIWs at a location, or at different sites by capturing the essential features of the wetlandscape: stochastic hydro-climatic forcing, bathymetry, and connectivity to groundwater and/or upland. Numerical simulation of hydrologic variability of groundwater-dependent GIWs allowed us to further examine the role of groundwater-surface water connectivity, and how an adjustment to the effective rate of water loss can be made to match the derived analytical pdf solutions. We also compared the analytical pdfs with observed data from an isolated wetland in Florida. This model framework has utility for managers seeking to achieve target eco-hydrological regimes of GIWs.

  15. Phytoremediation of urban wastewater by model wetlands with ornamental hydrophytes

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Phytoremediation offers a cost-effective, non-intrusive, and safe alternative to conventional cleanup techniques. In this study, we used ornamental hydrophytes plants as constructed wetlands to treat urban or rural domestic wastewater. Most ornamental hydrophytes adapted to the wastewater well, and were fairly efficient in scavenging BOD5 (biological oxygen demand 5 d), COD (chemical oxygen demand), TN (total nitrogen), TP (total phosphorus) and heavy metals (Cr, Pb, Cd) in the wastewater. However, the efficiency varied a lot for various species to different contaminants, Iris pseudacorus L. and Acorus gramineus Soland were good choices for treatment of composite-polluted urban wastewater. Some variation in the change of membrane peroxidation and endogenous protective system in responses to wastewater was found among six hydrophytes, which have a correlation with the efficiency of wastewater treatment. It may demonstrate that the developed antioxidative systems of Iris pseudacorus L. and Acorus gramineus Soland contributed much to their superiority. On the other hand, interaction of different components in the wastewater might have certain effects on phytoremediation.

  16. Artificial neural network modeling of p-cresol photodegradation.

    Science.gov (United States)

    Abdollahi, Yadollah; Zakaria, Azmi; Abbasiyannejad, Mina; Masoumi, Hamid Reza Fard; Moghaddam, Mansour Ghaffari; Matori, Khamirul Amin; Jahangirian, Hossein; Keshavarzi, Ashkan

    2013-06-03

    The complexity of reactions and kinetic is the current problem of photodegradation processes. Recently, artificial neural networks have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the non-linear relationships between variables in complex systems. In this study, an artificial neural network was applied for modeling p-cresol photodegradation. To optimize the network, the independent variables including irradiation time, pH, photocatalyst amount and concentration of p-cresol were used as the input parameters, while the photodegradation% was selected as output. The photodegradation% was obtained from the performance of the experimental design of the variables under UV irradiation. The network was trained by Quick propagation (QP) and the other three algorithms as a model. To determine the number of hidden layer nodes in the model, the root mean squared error of testing set was minimized. After minimizing the error, the topologies of the algorithms were compared by coefficient of determination and absolute average deviation. The comparison indicated that the Quick propagation algorithm had minimum root mean squared error, 1.3995, absolute average deviation, 3.0478, and maximum coefficient of determination, 0.9752, for the testing data set. The validation test results of the artificial neural network based on QP indicated that the root mean squared error was 4.11, absolute average deviation was 8.071 and the maximum coefficient of determination was 0.97. Artificial neural network based on Quick propagation algorithm with topology 4-10-1 gave the best performance in this study.

  17. Searching for turbulence models by artificial neural network

    Science.gov (United States)

    Gamahara, Masataka; Hattori, Yuji

    2017-05-01

    An artificial neural network (ANN) is tested as a tool for finding a new subgrid model of the subgrid-scale (SGS) stress in large-eddy simulation. An ANN is used to establish a functional relation between the grid-scale flow field and the SGS stress without any assumption of the form of function. Data required for training and test of the ANN are provided by direct numerical simulation of a turbulent channel flow. It is shown that an ANN can establish a model similar to the gradient model. The correlation coefficients between the real SGS stress and the output of the ANN are comparable to or larger than similarity models, but smaller than a two-parameter dynamic mixed model. Large-eddy simulations using the trained ANN are also performed. Although ANN models show no advantage over the Smagorinsky model, the results confirm that the ANN is a promising tool for establishing a new subgrid model with further improvement.

  18. Runoff forecasting by artificial neural network and conventional model

    Directory of Open Access Journals (Sweden)

    A.R. Ghumman

    2011-12-01

    Full Text Available Rainfall runoff models are highly useful for water resources planning and development. In the present study rainfall–runoff model based on Artificial Neural Networks (ANNs was developed and applied on a watershed in Pakistan. The model was developed to suite the conditions in which the collected dataset is short and the quality of dataset is questionable. The results of ANN models were compared with a mathematical conceptual model. The cross validation approach was adopted for the generalization of ANN models. The precipitation used data was collected from Meteorological Department Karachi Pakistan. The results confirmed that ANN model is an important alternative to conceptual models and it can be used when the range of collected dataset is short and data is of low standard.

  19. 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.

  20. 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.

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

    Directory of Open Access Journals (Sweden)

    A. D. Clulow

    2012-02-01

    Full Text Available The contribution of freshwater supply from the Mfabeni Mire to Lake St. Lucia during dry periods is important to the survival of certain plant and animal species in the iSimangaliso Wetland Park. This freshwater supply is mainly dependent on the variability of the major components of the water balance, namely rainfall and total evaporation (ET. Attempts to quantify the water balance have been limited through uncertainties in quantifying ET from the Mfabeni Mire. Despite advances in evaporation measurement and modelling from wetlands, there still exists some doubt as to which methods are best suited to characterise wetland ET with most authors suggesting a combination of methods.

    In this study, the surface renewal (SR method was successfully used to determine the long-term ET (12 months 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. The annual ET was lower (900 mm yr−1 than expected, due to cloud cover in summer and low atmospheric demand throughout the year, despite the available water and high windspeeds. Daily ET estimates were compared to the Priestley-Taylor results and a site specific calibration α = 1.0 was obtained for the site. The Priestley-Taylor results agreed well with the actual ET from the surface renewal technique (R2 = 0.96 throughout the 12 month period. 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

  2. Surrogate Modeling of Deformable Joint Contact using Artificial Neural Networks

    Science.gov (United States)

    Eskinazi, Ilan; Fregly, Benjamin J.

    2016-01-01

    Deformable joint contact models can be used to estimate loading conditions for cartilage-cartilage, implant-implant, human-orthotic, and foot-ground interactions. However, contact evaluations are often so expensive computationally that they can be prohibitive for simulations or optimizations requiring thousands or even millions of contact evaluations. To overcome this limitation, we developed a novel surrogate contact modeling method based on artificial neural networks (ANNs). The method uses special sampling techniques to gather input-output data points from an original (slow) contact model in multiple domains of input space, where each domain represents a different physical situation likely to be encountered. For each contact force and torque output by the original contact model, a multi-layer feed-forward ANN is defined, trained, and incorporated into a surrogate contact model. As an evaluation problem, we created an ANN-based surrogate contact model of an artificial tibiofemoral joint using over 75,000 evaluations of a fine-grid elastic foundation (EF) contact model. The surrogate contact model computed contact forces and torques about 1000 times faster than a less accurate coarse grid EF contact model. Furthermore, the surrogate contact model was seven times more accurate than the coarse grid EF contact model within the input domain of a walking motion. For larger input domains, the surrogate contact model showed the expected trend of increasing error with increasing domain size. In addition, the surrogate contact model was able to identify out-of-contact situations with high accuracy. Computational contact models created using our proposed ANN approach may remove an important computational bottleneck from musculoskeletal simulations or optimizations incorporating deformable joint contact models. PMID:26220591

  3. 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.

  4. 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

  5. Modeling the Interaction of H2 on Root Exudate Degradation and Methanogenesis in Wetland Sediments

    Science.gov (United States)

    Pal, D. S.; Jaffe, P. R.

    2014-12-01

    CH4 is produced in wetland sediments from the microbial degradation of organic carbon through multiple fermentation steps and methanogenesis pathways. There are many potential 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 is synthesized 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. While this may decrease the direct emissions of methane, there is little understanding about how H2 dynamics may feedback into overall methane production. Since H2 is used in methane production and produced in propionate fermentation, increased subsurface H2 concentrations can simultaneously inhibit propionate fermentation and acetate production and enhance hydrogenotrophic methanogenesis. For this study, we incubated soil samples from vegetated wetland sediments with propionate or acetate and four different hydrogen concentrations. The headspaces from these incubations were simultaneously analyzed for H2 and CH4 at multiple time points over two months. The comparison of methane production between different hydrogen concentrations and different carbon sources can indicate which process is most affected by increased hydrogen concentrations. The results from this study were combined with a newly formulated steady-state model of propionate degradation and formation of methane, that also accounts for the venting off both gases via plants. The resulting model indicates how methane production and emissions would be affected by plant volatilization.

  6. Artificial Neural Network Model of Hydrocarbon Migration and Accumulation

    Institute of Scientific and Technical Information of China (English)

    刘海滨; 吴冲龙

    2002-01-01

    Based on the dynamic simulation of the 3-D structure the sedimentary modeling, the unit entity model has been adopted to transfer the heterogeneous complex pas sage system into limited simple homogeneous entity, and then the traditional dyn amic simulation has been used to calculate the phase and the drive forces of the hyd rocarbon , and the artificial neural network(ANN) technology has been applied to resolve such problems as the direction, velocity and quantity of the hydrocarbo n migration among the unit entities. Through simulating of petroleum migration a nd accumulation in Zhu Ⅲ depression, the complex mechanism of hydrocarbon migra tion and accumulation has been opened out.

  7. Artificial Neural Network Model for Optical Fiber Direction Coupler Design

    Institute of Scientific and Technical Information of China (English)

    李九生; 鲍振武

    2004-01-01

    A new approach to the design of the optical fiber direction coupler by using neural network is proposed. To train the artificial neural network,the coupling length is defined as the input sample, and the coupling ratio is defined as the output sample. Compared with the numerical value calculation of the theoretical formula, the error of the neural network model output is 1% less.Then, through the model, to design a broadband or a single wavelength optical fiber direction coupler becomes easy. The method is proved to be reliable, accurate and time-saving. So it is promising in the field of both investigation and application.

  8. Restoring Wetlands

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    FERTILE LAND:The Qixing River Wetland in Heilongjiang Province was recently named a wetland of international importance at the Sixth Asian Wetland Symposium held in Wuxi City, east China’s Jiangsu Province, on October 13

  9. A multi-scale comparison of modeled and observed seasonal methane emissions in northern wetlands

    Science.gov (United States)

    Xu, Xiyan; Riley, William J.; Koven, Charles D.; Billesbach, Dave P.; Chang, Rachel Y.-W.; Commane, Róisín; Euskirchen, Eugénie S.; Hartery, Sean; Harazono, Yoshinobu; Iwata, Hiroki; McDonald, Kyle C.; Miller, Charles E.; Oechel, Walter C.; Poulter, Benjamin; Raz-Yaseef, Naama; Sweeney, Colm; Torn, Margaret; Wofsy, Steven C.; Zhang, Zhen; Zona, Donatella

    2016-09-01

    Wetlands are the largest global natural methane (CH4) source, and emissions between 50 and 70° N latitude contribute 10-30 % to this source. Predictive capability of land models for northern wetland CH4 emissions is still low due to limited site measurements, strong spatial and temporal variability in emissions, and complex hydrological and biogeochemical dynamics. To explore this issue, we compare wetland CH4 emission predictions from the Community Land Model 4.5 (CLM4.5-BGC) with site- to regional-scale observations. A comparison of the CH4 fluxes with eddy flux data highlighted needed changes to the model's estimate of aerenchyma area, which we implemented and tested. The model modification substantially reduced biases in CH4 emissions when compared with CarbonTracker CH4 predictions. CLM4.5 CH4 emission predictions agree well with growing season (May-September) CarbonTracker Alaskan regional-level CH4 predictions and site-level observations. However, CLM4.5 underestimated CH4 emissions in the cold season (October-April). The monthly atmospheric CH4 mole fraction enhancements due to wetland emissions are also assessed using the Weather Research and Forecasting-Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) model coupled with daily emissions from CLM4.5 and compared with aircraft CH4 mole fraction measurements from the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) campaign. Both the tower and aircraft analyses confirm the underestimate of cold-season CH4 emissions by CLM4.5. The greatest uncertainties in predicting the seasonal CH4 cycle are from the wetland extent, cold-season CH4 production and CH4 transport processes. We recommend more cold-season experimental studies in high-latitude systems, which could improve the understanding and parameterization of ecosystem structure and function during this period. Predicted CH4 emissions remain uncertain, but we show here that benchmarking against observations across spatial scales can

  10. ENVIRONMENTAL IMPACT ON THE WATERBIRD DISTRIBUTION DURING WINTER AND SPRING AT THE ARTIFICIAL WETLANDS IN CHONGMING DONTAN,SHANGHAI%上海崇明东滩人工湿地冬春季水鸟的生境因子分析

    Institute of Scientific and Technical Information of China (English)

    张美; 牛俊英; 杨晓婷; 汤臣栋; 王天厚

    2013-01-01

    为了解围垦后不同土地利用方式对水鸟的影响,于2011年11月~2012年5月对上海市崇明东滩湿地公园、北八(傚)鱼塘、98大堤内抛荒鱼塘、以及捕鱼港互花米草控制示范区的芦苇塘4类人工湿地开展水鸟调查,在冬季共统计到水鸟20 050只,隶属于5目9科34种,春季共统计到水鸟5 080只,隶属于6目7科47种.方差分析表明,冬、春季4类人工湿地水鸟种类、密度、多样性均有显著差异.运用相关分析对水鸟种类、密度、物种多样性、均匀性指数等群落特征及调查样方内明水面面积、平均水位、人为干扰、裸露浅滩面积、植被面积等环境因子进行水鸟生境因子选择分析,结果表明,冬季水鸟种类、多样性与明水面面积呈极显著正相关,水鸟种类与植被面积呈极显著负相关;春季种类、密度、多样性都与裸露浅滩面积呈极显著正相关.崇明东滩人工湿地在水鸟保育中起到了重要的作用,根据不同水鸟对生境因子的要求,冬季应保持较大的明水面面积和一定的水深,为雁鸭类建立合适的栖息地.春季应保持一定的裸露浅滩面积,为鸻鹬类提供良好的避难所.因此,水位调控成为崇明东滩人工湿地自然保育的重要手段.%With natural wetlands loss and degradation as a consequence of human activities around the world,artificial wetland as alternative habitats for waterbirds receives more and more attention.Chongming Dongtan,located at the Yangtze River Mouth,is one of most important wintering and stopover sites for thousands of migratory waterbirds.During last decade,this area was reclaimed for multi-purposes,and part of area was converted into four patterns of the artificial wetlands:(1) wetland park,(2) aquacultural ponds,(3) abandoned fishpond,and (4)artificial wetland restoration demonstration area.In order to understand the environmental impacts of different land use on the waterbirds after the coastal

  11. Comparative quantification of oxygen release by wetland plants: electrode technique and oxygen consumption model.

    Science.gov (United States)

    Wu, Haiming; Liu, Jufeng; Zhang, Jian; Li, Cong; Fan, Jinlin; Xu, Xiaoli

    2014-01-01

    Understanding oxygen release by plants is important to the design of constructed wetlands for wastewater treatment. Lab-scale systems planted with Phragmites australis were studied to evaluate the amount of oxygen release by plants using electrode techniques and oxygen consumption model. Oxygen release rate (0.14 g O2/m(2)/day) measured using electrode techniques was much lower than that (3.94-25.20 gO2/m(2)/day) calculated using the oxygen consumption model. The results revealed that oxygen release by plants was significantly influenced by the oxygen demand for the degradation of pollutants, and the oxygen release rate increased with the rising of the concentration of degradable materials in the solution. The summary of the methods in qualifying oxygen release by wetland plants demonstrated that variations existed among different measuring methods and even in the same measuring approach. The results would be helpful for understanding the contribution of plants in constructed wetlands toward actual wastewater treatment.

  12. 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.

  13. Searching for turbulence models by artificial neural network

    CERN Document Server

    Gamahara, Masataka

    2016-01-01

    Artificial neural network (ANN) is tested as a tool for finding a new subgrid model of the subgrid-scale (SGS) stress in large-eddy simulation. ANN is used to establish a functional relation between the grid-scale (GS) flow field and the SGS stress without any assumption of the form of function. Data required for training and test of ANN are provided by direct numerical simulation (DNS) of a turbulent channel flow. It is shown that ANN can establish a model similar to the gradient model. The correlation coefficients between the real SGS stress and the output of ANN are comparable to or larger than similarity models, but smaller than a two-parameter dynamic mixed model.

  14. 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. ?? 2010 Elsevier Ltd.

  15. 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

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

    Indian Academy of Sciences (India)

    Ali Aytek; M Asce; Murat Alp

    2008-04-01

    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 different ANN techniques, the feed forward back propagation (FFBP) and generalized regression neural network (GRNN) methods are compared with one EC method, Gene Expression Programming (GEP) which is a new evolutionary algorithm that evolves computer programs. The daily hydrometeorological data of three rainfall stations and one streamflow station for Juniata River Basin in Pennsylvania state of USA are taken into consideration in the model development. Statistical parameters such as average, standard deviation, coefficient of variation, skewness, minimum and maximum values, as well as criteria such as mean square error (MSE) and determination coefficient (2) are used to measure the performance of the models. The results indicate that the proposed genetic programming (GP) formulation performs quite well compared to results obtained by ANNs and is quite practical for use. It is concluded from the results that GEP can be proposed as an alternative to ANN models.

  17. Landslide susceptibility analysis using an artificial neural network model

    Science.gov (United States)

    Mansor, Shattri; Pradhan, Biswajeet; Daud, Mohamed; Jamaludin, Normalina; Khuzaimah, Zailani

    2007-10-01

    This paper deals with landslide susceptibility analysis using an artificial neural network model for Cameron Highland, Malaysia. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical/geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for the landslide hazards. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide hazard was analyzed using landslide occurrence factors employing the logistic regression model. The results of the analysis were verified using the landslide location data and compared with logistic regression model. The accuracy of hazard map observed was 85.73%. The qualitative landslide susceptibility analysis was carried out using an artificial neural network model by doing map overlay analysis in GIS environment. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network.

  18. 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

  19. 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

  20. Study on driving forces of wetland change in the Western Liaohe River basin based on random forest model

    Science.gov (United States)

    Wu, Menghong; Yang, Changbao; Zhang, Yanhong; Lin, Nan

    2017-05-01

    Based on the platform of RS and GIS, random forest progression model is used for study driving force of wetland change in western Liaohe river basin, five influencing factors which include elevation, slope, temperature, precipitation and population density are chosen to establish random forest progression model about the wetland change and the driving factors. Using the the mean value of the prediction accuracy outside the bag calculated by the model to evaluate the importance of the variables. The result indicates that the coefficient of partial correlation between precipitation and wetland density is the largest among the five influencing factors, followed by temperature, population density, elevation and slope is smallest. The influence of natural factors on the change of wetland density is mainly reflected in precipitation and temperature factors, and the precipitation is obviously higher than that of temperature, under the influence of human factors, the influence of population density factor on wetland density is higher than that of elevation and slope factor. The result shows that in the past 40 years, the human activities in the study area have increased the density of wetland to some extent, but it is not the main factor.

  1. Modeling the effects of nutria (Myocastor coypus) on wetland loss

    Science.gov (United States)

    Carter, J.; Foote, A.L.; Johnson-Randall, L. A.

    1999-01-01

    We created a model to study the process in which nutria (Myocastor coypus) feeding activities lead to erosion and loss of marsh area. This model ties together data on nutria population dynamics and feeding behavior from the literature with data from field studies on the phenology of Scirpus americanus and Spartina patens conducted in the Barataria Basin, Louisiana, USA in 1992. The complete model consists of three linked models: a model of nutria population dynamics (nutria model), a model of the annual marsh biomass cycle of Scirpus americanus and Spartina patens (biomass model), and a plant-biomass density-dependent marsh area model (area model). When all three models are linked together, they form the 'nutria-biomass-area model.' Analysis of the models indicated the following. (1) The high population densities and low survivorship rates as reported in the literature are incompatible. (2) the nutria model is sensitive to adult and juvenile survivorship and, to a lesser extent, young born per female. It is not particularly sensitive to gestation periods, impregnation rates, or time to maturity. (3) The marsh area model is not sensitive to the marsh loss equation nor to the density at which loss of marsh area begins but is sensitive to the amount of biomass destroyed per nutria. (4) Nutria numbers do not significantly decrease in the nutria-biomass-area model until the total marsh area approaches zero because marsh loss occurs only during winter when marsh biomass is at its annual low.

  2. 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.

  3. Application of Artificial Intelligence for Bridge Deterioration Model.

    Science.gov (United States)

    Chen, Zhang; Wu, Yangyang; Li, Li; Sun, Lijun

    2015-01-01

    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.

  4. Modeling biodegradation and kinetics of glyphosate by artificial neural network.

    Science.gov (United States)

    Nourouzi, Mohsen M; Chuah, Teong G; Choong, Thomas S Y; Rabiei, F

    2012-01-01

    An artificial neural network (ANN) model was developed to simulate the biodegradation of herbicide glyphosate [2-(Phosphonomethylamino) acetic acid] in a solution with varying parameters pH, inoculum size and initial glyphosate concentration. The predictive ability of ANN model was also compared with Monod model. The result showed that ANN model was able to accurately predict the experimental results. A low ratio of self-inhibition and half saturation constants of Haldane equations (glyphosate on bacteria growth. The value of K(i)/K(s) increased when the mixed inoculum size was increased from 10(4) to 10(6) bacteria/mL. It was found that the percentage of glyphosate degradation reached a maximum value of 99% at an optimum pH 6-7 while for pH values higher than 9 or lower than 4, no degradation was observed.

  5. 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.

  6. 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.

  7. 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.

  8. Application of computational fluid dynamic to model the hydraulic performance of subsurface flow wetlands

    Institute of Scientific and Technical Information of China (English)

    FAN Liwei; Hai Reti; WANG Wenxing; LU Zexiang; YANG Zhiming

    2008-01-01

    A subsurface flow wetland (SSFW) was simulated using a commercial computational fluid dynamic (CFD) code. The constructed media was simulated using porous media and the liquid resident time distribution (RTD) in the SSFW was obtained using the particle trajectory model. The effect of wetland configuration and operating conditions on the hydraulic performance of the SSFW were investigated. The results indicated that the hydraulic performance of the SSFW was predominantly affected by the wetland configuration. The hydraulic efficiency of the SSFW with an inlet at the middle edge of the upper media was 0.584 and the best among the SSFWs with an inlet at the top, the middle, and the bottom edge of the upper media. The constructed media affected the hydraulic performance by the ratio (K) of the upper and lower media resistance. The selection of appropriate media resistance in the protection layer can improve the hydraulic efficiency. When the viscous resistance coefficient of the media in the protection layer changed from 2.315×105 to 1.200×108, the hydraulic efficiency of the SSFW increased from 0.301 to 0.751. However, the effect of operating conditions on the hydraulic efficiency of the SSFW was slight.

  9. 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.

  10. 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.

  11. National Wetlands Inventory - Wetlands

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This data set represents the extent, approximate location and type of wetlands and deepwater habitats in the United States and its Territories. These data delineate...

  12. National Wetlands Inventory - Wetlands

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This data set represents the extent, approximate location and type of wetlands and deepwater habitats in the United States and its Territories. These data delineate...

  13. 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.

  14. 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

  15. 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.

  16. Artificial neural network modeling of dissolved oxygen in reservoir.

    Science.gov (United States)

    Chen, Wei-Bo; Liu, Wen-Cheng

    2014-02-01

    The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan.

  17. 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

  18. 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....

  19. An Application of Finite Element Modelling to Pneumatic Artificial Muscle

    Directory of Open Access Journals (Sweden)

    R. Ramasamy

    2005-01-01

    Full Text Available The purpose of this article was to introduce and to give an overview of the Pneumatic Artificial Muscles (PAMs as a whole and to discuss its numerical modelling, using the Finite Element (FE Method. Thus, more information to understand on its behaviour in generating force for actuation was obtained. The construction of PAMs was mainly consists of flexible, inflatable membranes which having orthotropic material behaviour. The main properties influencing the PAMs will be explained in terms of their load-carrying capacity and low weight in assembly. Discussion on their designs and capacity to function as locomotion device in robotics applications will be laid out, followed by FE modelling to represent PAMs overall structural behaviour under any potential operational conditions.

  20. Ecohydrologic Response of a Wetland Indicator Species to Climate Change and Streamflow Regulation: A Conceptual Model

    Science.gov (United States)

    Ward, E. M.; Gorelick, S.

    2015-12-01

    The Peace-Athabasca Delta ("Delta") in northeastern Alberta, Canada, is a UNESCO World Heritage Site and a Ramsar Wetland of International Importance. Delta ecohydrology is expected to respond rapidly to upstream water demand and climate change, with earlier spring meltwater, decreased springtime peak flow, and a decline in springtime ice-jam flooding. We focus on changes in the population and distribution of muskrat (Ondatra zibethicus), an ecohydrologic indicator species. We present a conceptual model linking hydrology and muskrat ecology. Our conceptual model links seven modules representing (1) upstream water demand, (2) streamflow and snowmelt, (3) floods, (4) the water balance of floodplain lakes, (5) muskrat habitat suitability, (6) wetland vegetation, and (7) muskrat population dynamics predicted using an agent-based model. Our goal is to evaluate the effects of different climate change and upstream water demand scenarios on the abundance and distribution of Delta muskrat, from present-2100. Moving from the current conceptual model to a predictive quantitative model, we will rely on abundant existing data and Traditional Ecological Knowledge of muskrat and hydrology in the Delta.

  1. An improved representation of geographically isolated wetlands in a watershed-scale hydrologic model

    Science.gov (United States)

    Geographically isolated wetlands (GIWs), defined as wetlands surrounded by uplands, provide an array of ecosystem goods and services. Within the United States, federal regulatory protections for GIWs are contingent, in part, on the quantification of their singular or aggregate ef...

  2. Integrating wetland connectivity into models for watershed-scale analyses: Current and future approaches

    Science.gov (United States)

    Geographically isolated wetlands (GIW), or wetlands embedded in uplands, exist along a spatial and temporal hydrologic connectivity continuum to downstream waters. Via these connections and disconnections, GIWs provide numerous hydrological, biogeochemical, and biological functio...

  3. Modelling the SOFC behaviours by artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Milewski, Jaroslaw; Swirski, Konrad [Institute of Heat Engineering, Warsaw University of Technology, 25 Nowowiejska Street, 00-665 Warsaw (Poland)

    2009-07-15

    The Artificial Neural Network (ANN) can be applied to simulate an object's behaviour without an algorithmic solution merely by utilizing available experimental data. The ANN is used for modelling singular cell behaviour. The optimal network architecture is shown and commented. The error backpropagation algorithm was used for an ANN training procedure. The ANN based SOFC model has the following input parameters: current density, temperature, fuel volume flow density (ml min{sup -1} cm{sup -2}), and oxidant volume flow density. Based on these input parameters, cell voltage is predicted by the model. Obtained results show that the ANN can be successfully used for modelling the singular solid oxide fuel cell. The self-learning process of the ANN provides an opportunity to adapt the model to new situations (e.g. certain types of impurities at inlet streams etc.). Based on the results from this study it can be concluded that, by using the ANN, an SOFC can be modelled with relatively high accuracy. In contrast to traditional models, the ANN is able to predict cell voltage without knowledge of numerous physical, chemical, and electrochemical factors. (author)

  4. Quantifying wetland methane emissions with process-based models of different complexities

    Directory of Open Access Journals (Sweden)

    J. Tang

    2010-08-01

    Full Text Available Bubbling is an important pathway of methane emissions from wetland ecosystems; however the concentration-based threshold function approach in current biogeochemistry models of methane is not sufficient to represent the complex ebullition process. Here we revise an extant process-based biogeochemistry model, the Terrestrial Ecosystem Model into a multi-substance model (CH4, O2, CO2 and N2 to simulate methane production, oxidation, and transport (particularly ebullition with different model complexities. When ebullition is modeled with a concentration-based threshold function and if the inhibition effect of oxygen on methane production and the competition for oxygen between methanotrophy and heterotrophic respiration are retained, the model is a two-substance system. Ignoring the role of oxygen, while still modeling ebullition with a concentration-based threshold function, reduces the model to a one-substance system. These models were tested through a group of sensitivity analyses at two temperate peatland sites in Michigan. We demonstrate that only the four-substance model with a pressure-based ebullition algorithm is able to capture the episodic emissions induced by a sudden decrease in atmospheric pressure. All models captured the retardation effect on methane efflux from an increase in surface standing water which results from the inhibition of diffusion and the increase in rhizospheric oxidation. We conclude that to more accurately account for the effects of atmospheric pressure dynamics and standing water on methane effluxes, the multi-substance model with a pressure-based ebullition algorithm should be used in the future to quantify global wetland CH4 emissions. Further, to more accurately simulate the pore water gas concentrations and different pathways of methane transport, an exponential root distribution function should be used and the phase-related parameters should be treated as

  5. Quantifying wetland methane emissions with process-based models of different complexities

    Directory of Open Access Journals (Sweden)

    J. Tang

    2010-11-01

    Full Text Available Bubbling is an important pathway of methane emissions from wetland ecosystems. However the concentration-based threshold function approach in current biogeochemistry models of methane is not sufficient to represent the complex ebullition process. Here we revise an extant process-based biogeochemistry model, the Terrestrial Ecosystem Model into a multi-substance model (CH4, O2, CO2 and N2 to simulate methane production, oxidation, and transport (particularly ebullition with different model complexities. When ebullition is modeled with a concentration-based threshold function and if the inhibition effect of oxygen on methane production and the competition for oxygen between methanotrophy and heterotrophic respiration are retained, the model becomes a two-substance system. Ignoring the role of oxygen, while still modeling ebullition with a concentration-based threshold function, reduces the model to a one-substance system. These models were tested through a group of sensitivity analyses using data from two temperate peatland sites in Michigan. We demonstrate that only the four-substance model with a pressure-based ebullition algorithm is able to capture the episodic emissions induced by a sudden decrease in atmospheric pressure or by a sudden drop in water table. All models captured the retardation effect on methane efflux from an increase in surface standing water which results from the inhibition of diffusion and the increase in rhizospheric oxidation. We conclude that to more accurately account for the effects of atmospheric pressure dynamics and standing water on methane effluxes, the multi-substance model with a pressure-based ebullition algorithm should be used in the future to quantify global wetland CH4 emissions. Further, to more accurately simulate the pore water gas concentrations and different pathways of methane transport, an exponential root distribution function should be used

  6. Consumer Choice Prediction: Artificial Neural Networks versus Logistic Models

    Directory of Open Access Journals (Sweden)

    Christopher Gan

    2005-01-01

    Full Text Available Conventional econometric models, such as discriminant analysis and logistic regression have been used to predict consumer choice. However, in recent years, there has been a growing interest in applying artificial neural networks (ANN to analyse consumer behaviour and to model the consumer decision-making process. The purpose of this paper is to empirically compare the predictive power of the probability neural network (PNN, a special class of neural networks and a MLFN with a logistic model on consumers’ choices between electronic banking and non-electronic banking. Data for this analysis was obtained through a mail survey sent to 1,960 New Zealand households. The questionnaire gathered information on the factors consumers’ use to decide between electronic banking versus non-electronic banking. The factors include service quality dimensions, perceived risk factors, user input factors, price factors, service product characteristics and individual factors. In addition, demographic variables including age, gender, marital status, ethnic background, educational qualification, employment, income and area of residence are considered in the analysis. Empirical results showed that both ANN models (MLFN and PNN exhibit a higher overall percentage correct on consumer choice predictions than the logistic model. Furthermore, the PNN demonstrates to be the best predictive model since it has the highest overall percentage correct and a very low percentage error on both Type I and Type II errors.

  7. 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.

  8. Boussinesq modeling of wave-induced hydrodynamics in coastal wetlands

    Science.gov (United States)

    Chakrabarti, Agnimitro; Brandt, Steven R.; Chen, Qin; Shi, Fengyan

    2017-05-01

    In this paper, an improved formulation of the vegetation drag force, applicable for the fully nonlinear Boussinesq equations and based on the use of the depth-varying, higher-order expansion of the horizontal velocity, in the quadratic vegetation drag law has been presented. The model uses the same numerical schemes as FUNWAVE TVD but is based on the CACTUS framework. The model is validated for wave height and setup, against laboratory experiments with and without vegetation cover. The wave attenuation results using the improved formulation were compared with those using the first-order reference velocity as well as with analytical solutions using linear wave theory. The analytical solution using the depth-varying velocity, predicted by the linear wave theory, was shown to match the model results with the fully expanded velocity approach very well for all wave cases, except under near-emergent and emergent conditions (when the ratio of stem height to water depth is greater than 0.75) and when the Ursell (Ur) number is less than 5. Simulations during peak storm waves, during Hurricane Isaac, showed that vegetation is very effective in reducing setup on platforms and in reducing the wave energy within the first few hundred meters.

  9. 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...

  10. 菖蒲人工湿地对煤矿废水中镉的处理研究%Study on the treatment of cadmium-containing wastewater from coal mine in calamus artificial wetlands

    Institute of Scientific and Technical Information of China (English)

    曹优明; 戴涛

    2012-01-01

    The removing rate of cadmium in wastewater from coal mine in the calamus artificial wetland,whose substrates consists of soil+sand and soil +cinder,respectively,has been studied,and the effect of flow rate on the cadmium removing rate in wastewater from coal mine is investigated. The results show that the highest removing rate of cadmium in the calamus articial wetland whose substrate is constructed by soil and sand can reach 100% ,at least 86.23%. Meanwhile the highest removing rate of cadmium in the calamus artificial wetland whose substrate is constructed with soil and cinder can reach 99.63%. But,when the flow rate has increased tol5 L/d,the removing rate of cadmium in the system is only 66.11%. Therefore, the (soil+sand) calamus artificial wetland system has stronger purifying ability than the (soil+cinder) calamus wetl and system.%研究了分别以泥土+河沙与泥土+炭渣2种基质构建的菖莆人工湿地对煤矿废水中镉的去除率,并探讨了流量对镉去除率的影响.结果表明:用泥土+河沙基质构建的菖蒲人工湿地对镉的去除率最高可达100%,最低也为86.23%.以泥土+炭渣为基质构建的菖蒲人工湿地对镉的去除率最高能达到99.63%,但当废水流量增大到15 L/d时,系统对镉的去除率仅为66.11%.因此,菖莆(泥土+河沙)人工湿地系统比菖莆(泥土+炭渣)人工温地系统对煤矿废水中镉的净化能力强.

  11. Purifying effects of artificial wetland with different vegetation systems on domestic sewage%不同植物人工湿地对生活污水净化效果试验研究

    Institute of Scientific and Technical Information of China (English)

    张雪琪; 吴晖; 黄发明; 夏世斌

    2012-01-01

    The present paper cherishes its purpose to study the purifying effects of artificial wetland with different vegetation systems on domestic leftover sewage. So far as we know, there has been exiting quite a large number of studies in this way that claim to have discovered some plants can be used for this purpose. Many kinds of plants in wetland, so far as we know, can provide a substrate ( roots, stems, and leaves) upon which microorganisms can grow as they break down some organic materials. The plants should have the ability of enduring pollution, good effect of decontamination, developed root system and strong resistance to diseases and insect-attacking, great economic value. For example, Canna India has been widely used in such artificial wetlands as a kind of pollution-resistant plant. However, Pennisetum purpureum is still under experiments. The purification effect of Pennisetum purpureum and Canna india on such sewage in the surface-flowing artificial wetland was studied. For this purpose, we have done testing and observation to study water quality of the flowing water, and purifying effect of TN and TP by using the above-said plant systems under the conditions of different temperatures are discussed. Our experiments are of practical significance in the sense of scientific research, data assistance and theoretical reasoning for Pennisetum purpureum , which can help to improve the wetland water environment quality and repair the sewage body. The results of our study indicate that both Pennisetum purpureum and Canna india enjoy a good purifying effect on the domestic sewage with a nice removal efficiency of TP and TN. Statistically, the average removal rate of COD is 58.38% and 49.49%, TN can be as high as 76.77% and 66.49%, and TP can be as high as 82.99% and 87.99%., respectively. Moreover, the sewage purification efficiency of the two plants in the artificial wetland has no direct correlation with the change of temperature, because the purification efficiency

  12. 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

  13. 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.

  14. Numerical modelling to determine freshwater/saltwater interface configuration in a low-gradient coastal wetland aquifer

    Science.gov (United States)

    Swain, E.; Wolfert, M.

    2007-01-01

    A coupled hydrodynamic surface-water/groundwater model with salinity transport is used to examine the aquifer salinity interface in the coastal wetlands of Everglades National Park in Florida, USA. The hydrology differs from many other coastal areas in that inland water levels are often higher than land surface, the flow gradients are small, and, along parts of the coastline, the wetland is separated from the offshore waters by a natural embankment. Examining the model-simulated aquifer salinities along a transect that cuts the coastal embankment, a small zone of fresh groundwater is seen beneath the embankment, which varies seasonally in size and salinity. The simulated surface-water and groundwater levels suggest that this zone exists because of ponding of surface water at the coastal embankment, creating freshwater underflow to the offshore waters. The seasonal variability in the freshwater zone indicates that it is sensitive to the wetland flows and water levels. The small size of the zone in the simulation indicates that a model with a higher spatial resolution could probably depict the zone more accurately. The coastal ecology is strongly affected by the salinity of the shallow groundwater and the coastal freshwater zone is sensitive to wetland flows and levels. In this environment, predicting the aquifer salinity interface in coastal wetlands is important in examining the effects of changing water deliveries associated with ecosystem restoration efforts.

  15. 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

  16. 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.

  17. 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.

  18. RESEARCH ADVANCES IN WETLAND ECOSYSTEM MODELS%湿地生态系统模型研究进展

    Institute of Scientific and Technical Information of China (English)

    崔保山; 杨志峰

    2001-01-01

    Wetland ecosystem models play an important role in the wetland assessment,restoration and construction.Models can be constructed by sampling and monitoring.The paper first gives the frame construction of wetland ecosystem models,then discusses three types of models.In riverine,the models includes hydrologic and hydrodynamic models,sediment accumulation models,and water quality models.Such as water budget,biomass,P,N models are the main types in mires and floodplain models.Wetlands degeneration is also an important problem for wetland study,so the paper also presents the degeneration model,especially the phyton control models.At the same time,the paper presents some problems existing in the modelling and the model studies in the future.%介绍了湿地生态系统模型概念及类型,重点分析了河流湿地、沼泽及河漫滩湿地、湿地退化模型。在河流湿地中,重点讨论了水文水动力学模型、泥沙冲淤模型、河流水质模型;在沼泽及河漫滩湿地中,阐述了水量模型、生物量模型、P模型和N模型;最后在分析湿地退化的数学模型基础上,探讨了湿地大型植物的控制模型。

  19. Constitutive models of artificial muscles:a review

    Institute of Scientific and Technical Information of China (English)

    Hui-ming WANG; Shao-xing QU

    2016-01-01

    Artificial muscles are materials which possess muscle-like characteristics; they have many promising applications and many materials have been exploited as artificial muscles. In this review, the artificial muscles discussed are confined to die-lectric elastomers and responsive gels. We focus on their constitutive models based on free energy function theory. For dielectric elastomers, both hyperelastic and visco-hyperelastic models are involved. For responsive gels, we consider different kinds of gels, such as hydrogel, pH-sensitive gel, temperature-sensitive gel, polyelectrolyte gel, reactive gel, etc. With an accurate, relia-ble, and powerful constitutive model, exact theoretical analysis can be achieved and the important intrinsic characteristics of artificial muscle based systems can be revealed.%中文概要题目:人工肌肉本构模型的综述人工肌肉是指具有类似肌肉特性的材料,这些材料在外界激励下,可以实现大变形,且响应速度快。本文总结两类人工肌肉本构模型的研究成果:一类是介电高弹体,另一类是响应性凝胶。本文中提到的本构模型仅限于用自由能函数导出的情形。对于介电高弹体材料,分别综述超弹性模型和粘性超弹性模型。在超弹性模型中,列出目前研究中使用较多的一些本构模型的自由能函数具体表达式;比较 neo-Hookean、Gent、Arruda-Boyce和 Ogden四种模型在单轴拉伸和等双轴拉伸两种情形下的名义应力-伸长曲线;给出了考虑一些重要因素的研究模型,这些因素包括材料可压缩性、取向极化、变介电常数、热耦合、受纤维约束、流体耦合以及空气耦合等。对于响应性凝胶,分别综述水凝胶、pH 敏感性凝胶、温度敏感性凝胶、聚电解质凝胶以及反应性凝胶等的本构模型。这些精确、可靠和有效的本构模型,将有助于开展人工肌肉系统的性能分析和预测,甚至揭示

  20. Clay particle retention in small constructed wetlands.

    Science.gov (United States)

    Braskerud, B C

    2003-09-01

    Constructed wetlands (CWs) can be used to mitigate non-point source pollution from arable fields. Previous investigations have shown that the relative soil particle retention in small CWs increases when hydraulic load increases. This paper investigates why this phenomenon occurs, even though common retention models predict the opposite, by studying clay and silt particle retention in two Norwegian CWs. Retention was measured with water flow proportional sampling systems in the inlet and outlet of the wetlands, and the texture of the suspended solids was analyzed. The surface area of the CWs was small compared to the watershed area (approximately 0.07%), giving high average hydraulic loads (1.1 and 2.0 md(-1)). One of the watersheds included only old arable land, whereas the other included areas with disturbed topsoil after artificial land leveling. Clay particle retention was 57% for the CW in the first watershed, and 22% for the CW in the disturbed watershed. The different behavior of the wetlands could be due to differences in aggregate size and stability of the particles entering the wetlands. Results showed that increased hydraulic loads did affect CW retention negatively. However, as runoff increased, soil particles/aggregates with higher sedimentation velocities entered the CWs (e.g., the clay particles behaved as silt particles). Hence, clay particle settling velocity is not constant as assumed in many prediction models. The net result was increased retention.

  1. Modeling of the growth of filamentous fungi in artificial microstructures

    Science.gov (United States)

    Nicolau, Dan V., Jr.; Hanson, Kristi; Nicolau, Dan V.

    2006-01-01

    We present a stochastic and spatial Monte Carlo model for the growth of a fungal colony in microstructures. This model is based on an "L-system-like" representation of filaments as individual objects. Each of these can both grow in space (and be diverted by obstacles) and can send new branches. All parameters in the model such as filament dimensions, the growth speed, behavior at and around obstacles, branching angle and frequency and others are obtained from experimental studies of growth in artificial microstructures. We investigate four different possible "strategies" the colony might use to achieve the tasks of (a) filling the available space and (2) finding its way out of the structures. The simulation results indicate that a combination of directional memory and a stop-and-branch behavior at corners gives the best results and observe that in fact this is similar to the experimentally observed behavior of the fungi. The model is expected to be of use in studying the colonization of microstructures by fungi and in the design of devices either using fungal growth or aiming to inhibit it.

  2. Phosphorus fate, management, and modeling in artificially drained systems.

    Science.gov (United States)

    Kleinman, Peter J A; Smith, Douglas R; Bolster, Carl H; Easton, Zachary M

    2015-03-01

    Phosphorus (P) losses in agricultural drainage waters, both surface and subsurface, are among the most difficult form of nonpoint source pollution to mitigate. This special collection of papers on P in drainage waters documents the range of field conditions leading to P loss in drainage water, the potential for drainage and nutrient management practices to control drainage losses of P, and the ability of models to represent P loss to drainage systems. A review of P in tile drainage and case studies from North America, Europe, and New Zealand highlight the potential for artificial drainage to exacerbate watershed loads of dissolved and particulate P via rapid, bypass flow and shorter flow path distances. Trade-offs are identified in association with drainage intensification, tillage, cover crops, and manure management. While P in drainage waters tends to be tied to surface sources of P (soil, amendments or vegetation) that are in highest concentration, legacy sources of P may occur at deeper depths or other points along drainage flow paths. Most startling, none of the major fate-and-transport models used to predict management impacts on watershed P losses simulate the dominant processes of P loss to drainage waters. Because P losses to drainage waters can be so difficult to manage and to model, major investment are needed (i) in systems that can provide necessary drainage for agronomic production while detaining peak flows and promoting P retention and (ii) in models that can adequately describe P loss to drainage waters.

  3. 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

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

    Directory of Open Access Journals (Sweden)

    R. F. Grant

    2012-05-01

    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, mycorrhizal and shoot 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 test of the model, annual NEP

  5. 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

  6. 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.

  7. Assessment of the Impact of the Spatial Distribution of Isolated and Riparian Wetlands on Watershed Hydrology using a Mathematical Modelling Framework

    Science.gov (United States)

    Fossey, M.; Rousseau, A. N.; Savary, S.; Royer, A.

    2014-12-01

    Wetlands play a significant role on the hydrological cycle, reducing peak flows through water storage functions and sustaining low flows through slow release of water. However, their impacts on water resource availability and flood control are mainly driven by wetland types and locations within a watershed. So, despite the general agreement about these major hydrological functions, little is known about their spatial and typological influences. Consequently, assessing the quantitative impact of wetlands on hydrological regimes has become a relevant issue for both the scientific community and the decision-maker community. To investigate the hydrologic response at the watershed scale, mathematical modelling has been a well-accepted framework. Specific isolated and riparian wetland modules were implemented in the PHYSITEL/HYDROTEL distributed hydrological modelling platform to assess the impact of the spatial distribution of isolated and riparian wetlands on the stream flows of the Becancour River watershed, Quebec, Canada. More specifically, the focus was on assessing whether stream flow parameters, including peak flow, low flow and flow volume, were related to: (i) the percentage and the distribution of wetlands in the watershed, (ii) geographic location of wetlands, and (iii) seasons. Preliminary results suggest that: (i) integration of specific wetland modules can slightly improve HYDROTEL's ability to replicate basic hydrograph characteristics; and (ii) isolated and riparian wetlands have individual space- and time-dependent impacts on the hydrologic response of the study watershed.

  8. Network modeling of membrane-based artificial cellular systems

    Science.gov (United States)

    Freeman, Eric C.; Philen, Michael K.; Leo, Donald J.

    2013-04-01

    Computational models are derived for predicting the behavior of artificial cellular networks for engineering applications. The systems simulated involve the use of a biomolecular unit cell, a multiphase material that incorporates a lipid bilayer between two hydrophilic compartments. These unit cells may be considered building blocks that enable the fabrication of complex electrochemical networks. These networks can incorporate a variety of stimuli-responsive biomolecules to enable a diverse range of multifunctional behavior. Through the collective properties of these biomolecules, the system demonstrates abilities that recreate natural cellular phenomena such as mechanotransduction, optoelectronic response, and response to chemical gradients. A crucial step to increase the utility of these biomolecular networks is to develop mathematical models of their stimuli-responsive behavior. While models have been constructed deriving from the classical Hodgkin-Huxley model focusing on describing the system as a combination of traditional electrical components (capacitors and resistors), these electrical elements do not sufficiently describe the phenomena seen in experiment as they are not linked to the molecular scale processes. From this realization an advanced model is proposed that links the traditional unit cell parameters such as conductance and capacitance to the molecular structure of the system. Rather than approaching the membrane as an isolated parallel plate capacitor, the model seeks to link the electrical properties to the underlying chemical characteristics. This model is then applied towards experimental cases in order that a more complete picture of the underlying phenomena responsible for the desired sensing mechanisms may be constructed. In this way the stimuli-responsive characteristics may be understood and optimized.

  9. Applying artificial vision models to human scene understanding

    Directory of Open Access Journals (Sweden)

    Elissa Michele Aminoff

    2015-02-01

    Full Text Available How do we understand the complex patterns of neural responses that underlie scene understanding? Studies of the network of brain regions held to be scene-selective – the parahippocampal/lingual region (PPA, the retrosplenial complex (RSC, and the occipital place area (TOS – have typically focused on single visual dimensions (e.g., size, rather than the high-dimensional feature space in which scenes are likely to be neurally represented. Here we leverage well-specified artificial vision systems to explicate a more complex understanding of how scenes are encoded in this functional network. We correlated similarity matrices within three different scene-spaces arising from: 1 BOLD activity in scene-selective brain regions; 2 behavioral measured judgments of visually-perceived scene similarity; and 3 several different computer vision models. These correlations revealed: 1 models that relied on mid- and high-level scene attributes showed the highest correlations with the patterns of neural activity within the scene-selective network; 2 NEIL and SUN – the models that best accounted for the patterns obtained from PPA and TOS – were different from the GIST model that best accounted for the pattern obtained from RSC; 3 The best performing models outperformed behaviorally-measured judgments of scene similarity in accounting for neural data. One computer vision method – NEIL (Never-Ending-Image-Learner, which incorporates visual features learned as statistical regularities across web-scale numbers of scenes – showed significant correlations with neural activity in all three scene-selective regions and was one of the two models best able to account for variance in the PPA and TOS. We suggest that these results are a promising first step in explicating more fine-grained models of neural scene understanding, including developing a clearer picture of the division of labor among the components of the functional scene-selective brain network.

  10. 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.

  11. Promising synergies of simulation model management, software engineering, artificial intelligence, and general system theories

    Energy Technology Data Exchange (ETDEWEB)

    Oren, T.I.

    1982-01-01

    Simulation is viewed within the model management paradigm. Major components of simulation systems as well as elements of model management are outlined. Possible synergies of simulation model management, software engineering, artificial intelligence, and general system theories are systematized. 21 references.

  12. Modelling artificial sea salt emission in large eddy simulations.

    Science.gov (United States)

    Maalick, Z; Korhonen, H; Kokkola, H; Kühn, T; Romakkaniemi, S

    2014-12-28

    We study the dispersion of sea salt particles from artificially injected sea spray at a cloud-resolving scale. Understanding of how different aerosol processes affect particle dispersion is crucial when designing emission sources for marine cloud brightening. Compared with previous studies, we include for the first time an explicit treatment of aerosol water, which takes into account condensation, evaporation and their effect on ambient temperature. This enables us to capture the negative buoyancy caused by water evaporation from aerosols. Additionally, we use a higher model resolution to capture aerosol loss through coagulation near the source point. We find that, with a seawater flux of 15 kg s(-1), the cooling due to evaporation can be as much as 1.4 K, causing a delay in particle dispersion of 10-20 min. This delay enhances particle scavenging by a factor of 1.14 compared with simulations without aerosol water. We further show that both cooling and particle dispersion depend on the model resolution, with a maximum particle scavenging efficiency of 20% within 5 h after emission at maximum resolution of 50 m. Based on these results, we suggest further regional high-resolution studies which model several injection periods over several weeks.

  13. Modelling artificial sea salt emission in large eddy simulations

    Science.gov (United States)

    Maalick, Z.; Korhonen, H.; Kokkola, H.; Kühn, T.; Romakkaniemi, S.

    2014-01-01

    We study the dispersion of sea salt particles from artificially injected sea spray at a cloud-resolving scale. Understanding of how different aerosol processes affect particle dispersion is crucial when designing emission sources for marine cloud brightening. Compared with previous studies, we include for the first time an explicit treatment of aerosol water, which takes into account condensation, evaporation and their effect on ambient temperature. This enables us to capture the negative buoyancy caused by water evaporation from aerosols. Additionally, we use a higher model resolution to capture aerosol loss through coagulation near the source point. We find that, with a seawater flux of 15 kg s−1, the cooling due to evaporation can be as much as 1.4 K, causing a delay in particle dispersion of 10–20 min. This delay enhances particle scavenging by a factor of 1.14 compared with simulations without aerosol water. We further show that both cooling and particle dispersion depend on the model resolution, with a maximum particle scavenging efficiency of 20% within 5 h after emission at maximum resolution of 50 m. Based on these results, we suggest further regional high-resolution studies which model several injection periods over several weeks. PMID:25404679

  14. 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. 

  15. 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.

  16. The effect of vegetation on pesticide dissipation from ponded treatment wetlands: quantification using a simple model.

    Science.gov (United States)

    Rose, Michael T; Crossan, Angus N; Kennedy, Ivan R

    2008-07-01

    Field data shows that plants accelerate pesticide dissipation from aquatic systems by increasing sedimentation, biofilm contact and photolysis. In this study, a graphical model was constructed and calibrated with site-specific and supplementary data to describe the loss of two pesticides, endosulfan and fluometuron, from a vegetated and a non-vegetated pond. In the model, the major processes responsible for endosulfan dissipation were alkaline hydrolysis and sedimentation, with the former process being reduced by vegetation and the latter enhanced. Fluometuron dissipation resulted primarily from biofilm reaction and photolysis, both of which were increased by vegetation. Here, greater photolysis under vegetation arose from faster sedimentation and increased light penetration, despite shading. Management options for employing constructed wetlands to polish pesticide-contaminated agricultural runoff are discussed. The lack of easily fulfilled sub-models and data describing the effect of aquatic vegetation on water chemistry and sedimentation is also highlighted.

  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. Spatial Modeling of Iron Transformations Within Artificial Soil Aggregates

    Science.gov (United States)

    Kausch, M.; Meile, C.; Pallud, C.

    2008-12-01

    Structured soils exhibit significant variations in transport characteristics at the aggregate scale. Preferential flow occurs through macropores while predominantly diffusive exchange takes place in intra-aggregate micropores. Such environments characterized by mass transfer limitations are conducive to the formation of small-scale chemical gradients and promote strong spatial variation in processes controlling the fate of redox-sensitive elements such as Fe. In this study, we present a reactive transport model used to spatially resolve iron bioreductive processes occurring within a spherical aggregate at the interface between advective and diffusive domains. The model is derived from current conceptual models of iron(hydr)oxide (HFO) transformations and constrained by literature and experimental data. Data were obtained from flow-through experiments on artificial soil aggregates inoculated with Shewanella putrefaciens strain CN32, and include the temporal evolution of the bulk solution composition, as well as spatial information on the final solid phase distribution within aggregates. With all iron initially in the form of ferrihydrite, spatially heterogeneous formation of goethite/lepidocrocite, magnetite and siderite was observed during the course of the experiments. These transformations were reproduced by the model, which ascribes a central role to divalent iron as a driver of HFO transformations and master variable in the rate laws of the considered reaction network. The predicted dissolved iron breakthrough curves also match the experimental ones closely. Thus, the computed chemical concentration fields help identify factors governing the observed trends in the solid phase distribution patterns inside the aggregate. Building on a mechanistic description of transformation reactions, fluid flow and solute transport, the model was able to describe the observations and hence illustrates the importance of small-scale gradients and dynamics of bioreductive

  19. 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.

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

    Science.gov (United States)

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

    2014-03-01

    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 km2 to 99 179.6 km2 a net increase of 6107.3 km2. The area of natural wetland decreased by 4043.7 km2 while the area of artificial wetland increased by 10 166.2 km2. 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.

  1. 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...

  2. 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

  3. 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.

  4. Statistical modelling of organic matter and emerging pollutants removal in constructed wetlands.

    Science.gov (United States)

    Hijosa-Valsero, María; Sidrach-Cardona, Ricardo; Martín-Villacorta, Javier; Cruz Valsero-Blanco, M; Bayona, Josep M; Bécares, Eloy

    2011-04-01

    Multiple regression models, clustering tree diagrams, regression trees (CHAID) and redundancy analysis (RDA) were applied to the study of the removal of organic matter and pharmaceuticals and personal care products (PPCPs) from urban wastewater by means of constructed wetlands (CWs). These four statistical analyses pointed out the importance of physico-chemical parameters, plant presence and chemical structure in the elimination of most pollutants. Temperature, pH values, dissolved oxygen concentration, redox potential and conductivity were related to the removal of the studied substances. Plant presence (Typha angustifolia and Phragmites australis) enhanced the removal of organic matter and some PPCPs. Multiple regression equations and CHAID trees provided numerical estimations of pollutant removal efficiencies in CWs. These models were validated and they could be a useful and interesting tool for the quick estimation of removal efficiencies in already working CWs and for the design of new systems which must fulfil certain quality requirements. Copyright © 2011 Elsevier Ltd. All rights reserved.

  5. 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

  6. Systemic Planning and Programming of the Yangtze Estuarine Wetlands Based on the Construction of Artificial Alternative Habitat%基于人工替代栖息地的长江口湿地系统规划

    Institute of Scientific and Technical Information of China (English)

    高宇; 张婷婷; 庄平

    2016-01-01

    人工替代栖息地是一种用于滨海河口区重建丧失的滩涂等栖息地的替代方法。按照长江口自然栖息地的分布特点以及不同栖息地的服务功能,基于人工替代栖息的生态修复和保护可以分为“两圈两带”。“两圈”是指崇明-长兴-横沙“三岛联动”保护圈,以及九段沙新生栖息地为核心的保护圈;“两带”分为城市生态安全带和城市生态调控带。长江口栖息地保护建设规划涉及具有特殊科学研究价值的栖息地和水源地、退化栖息地的生态修复和重建、景观水系整治等多个环节的建设内容,它们与人工替代栖息地的构建相辅相成。%Artificial alternative habitat is a type of habitat reconstruction or habitat alternative method for dealing with habitat loss in coastal estuary such as the loss of tidal flats�According to the geographical distribution and service functions of typical wetland habitats in the Yangtze estuary, we divided wetland of Yangtze estuary into two types based on the theories of ecological conservation and artificial alternative reconstruction�The classification were termed as “two circles, two strips”�One circle of the“Two circles” is the circle which enclose Chongming—Changxing—Hengsha three islands, the other circle is the protected circle whose core is the newly growing wetland Jiuduansha island; “two strips” include city ecological safety strip and city ecological modulative strip�The habitat protection and construction planning in Yangtze estuary involve the measures of reconstructing habitats and water sources with special scientific value, restoring and reconstructing of degraded habitats, improving landscape water system, etc�These multiple measures and the construction of artificial alternative habitat are complement to each other.

  7. 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

  8. Forecasting Financial Time-Series using Artificial Market Models

    CERN Document Server

    Gupta, N; Johnson, N F; Gupta, Nachi; Hauser, Raphael; Johnson, Neil F.

    2005-01-01

    We discuss the theoretical machinery involved in predicting financial market movements using an artificial market model which has been trained on real financial data. This approach to market prediction - in particular, forecasting financial time-series by training a third-party or 'black box' game on the financial data itself -- was discussed by Johnson et al. in cond-mat/0105303 and cond-mat/0105258 and was based on some encouraging preliminary investigations of the dollar-yen exchange rate, various individual stocks, and stock market indices. However, the initial attempts lacked a clear formal methodology. Here we present a detailed methodology, using optimization techniques to build an estimate of the strategy distribution across the multi-trader population. In contrast to earlier attempts, we are able to present a systematic method for identifying 'pockets of predictability' in real-world markets. We find that as each pocket closes up, the black-box system needs to be 'reset' - which is equivalent to sayi...

  9. An artificial blood vessel implanted three-dimensional microsystem for modeling transvascular migration of tumor cells.

    Science.gov (United States)

    Wang, Xue-Ying; Pei, Ying; Xie, Min; Jin, Zi-He; Xiao, Ya-Shi; Wang, Yang; Zhang, Li-Na; Li, Yan; Huang, Wei-Hua

    2015-02-21

    Reproducing a tumor microenvironment consisting of blood vessels and tumor cells for modeling tumor invasion in vitro is particularly challenging. Here, we report an artificial blood vessel implanted 3D microfluidic system for reproducing transvascular migration of tumor cells. The transparent, porous and elastic artificial blood vessels are obtained by constructing polysaccharide cellulose-based microtubes using a chitosan sacrificial template, and possess excellent cytocompatibility, permeability, and mechanical characteristics. The artificial blood vessels are then fully implanted into the collagen matrix to reconstruct the 3D microsystem for modeling transvascular migration of tumor cells. Well-defined simulated vascular lumens were obtained by proliferation of the human umbilical vein endothelial cells (HUVECs) lining the artificial blood vessels, which enables us to reproduce structures and functions of blood vessels and replicate various hemodynamic parameters. Based on this model, the adhesion and transvascular migration of tumor cells across the artificial blood vessel have been well reproduced.

  10. 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

    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...

  11. Removal of emerging organic pollutants in constructed wetlands: imazalil and tebuconazole as model pesticides

    DEFF Research Database (Denmark)

    Lyu, Tao

    2016-01-01

    The pesticides imazalil and tebuconazole are commonly used to protect various agricultural crops against fungal attack or as biocides for wood protection, as such, they have been found in both rural and urban water bodies. The emerging pesticides are gaining prominence due to the toxic effects...... model. Moreover, the removal ability was strongly influenced by CWs design, dissolved oxygen (DO) level, season (temperature), initial concentrations, hydraulic loading rate (HLR), plant present and species, and potentially nitrification processes. The pesticides biodegradation inside plant tissue after...... plant uptake may play a crucial role on the pesticides degradation. In terms of the microbial metabolic function, CWs designs, the season, present and species of wetland plant were the main drivers for defining the interstitial water and biofilm microbial community metabolic profiles. The presence...

  12. Linking a Large-Watershed Hydrogeochemical Model to a Wetland Community-Ecosystem Model to Estimate Plant Invasion Risk in the Coastal Great Lakes Region, USA

    Science.gov (United States)

    Currie, W. S.; Bourgeau-Chavez, L. L.; Elgersma, K. J.; French, N. H. F.; Goldberg, D. E.; Hart, S.; Hyndman, D. W.; Kendall, A. D.; Martin, S. L.; Martina, J. P.

    2014-12-01

    In the Laurentian Great Lakes region of the Upper Midwest, USA, agricultural and urban land uses together with high N deposition are contributing to elevated flows of N in rivers and groundwater to coastal wetlands. The functioning of coastal wetlands, which provide a vital link between land and water, are imperative to maintaining the health of the entire Great Lakes Basin. Elevated N inflows are believed to facilitate the spread of large-stature invasive plants (cattails and Phragmites) that reduce biodiversity and have complex effects on other ecosystem services including wetland N retention and C accretion. We enhanced the ILHM (Integrated Landscape Hydrology Model) to simulate the effects of land use on N flows in streams, rivers, and groundwater throughout the Lower Peninsula of Michigan. We used the hydroperiods and N loading rates simulated by ILHM as inputs to the Mondrian model of wetland community-ecosystem processes to estimate invasion risk and other ecosystem services in coastal wetlands around the Michigan coast. Our linked models produced threshold behavior in the success of invasive plants in response to N loading, with the threshold ranging from ca. 8 to 12 g N/m2 y, depending on hydroperiod. Plant invasions increased wetland productivity 3-fold over historically oligotrophic native communities, decreased biodiversity but slightly increased wetland N retention. Regardless of invasion, elevated N loading resulted in significantly enhanced rates of C accretion, providing an important region-wide mechanism of C storage. The linked models predicted a general pattern of greater invasion risk in the southern basins of lakes Michigan and Huron relative to northern areas. The basic mechanisms of invasion have been partially validated in our field mesocosms constructed for this project. The general regional patterns of increased invasion risk have been validated through our field campaigns and remote sensing conducted for this project.

  13. 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

    2017-08-01

    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.

  14. Treating of Simulated Sewage by Artificial Wetland with Surface Flowing Water%表面流人工湿地处理农村生活污水的研究

    Institute of Scientific and Technical Information of China (English)

    虞益江

    2011-01-01

    文章通过小试装置研究了表面流人工湿地对人工模拟生活污水的处理效果及相关运行参数.实验结果表明,表面流人工湿地系统对污水中的COD,TN,TP均有较好的综合净化能力,对各种污染物指标的去除率可分别达到COD 75%,TN 75%,TP 73%,其中COD和TN达到一级排放标准分别需3d和4d的表观停留时间,TP要达到二级排放标准则需6d的表观停留时间.而且,湿地植物对TN和TP的去除有比较明显的促进作用.%Simulated sewage was treated by laboratory artificial wetland with surface flowing water, and the performance and related operation parameters were investigated. The result showed that the removal of COD, TN, and TP reached 75% , 75% , and 73% respectively. And the COD and TN could reach discharge standard Class 1 under 3 d and 4 d of apparent retention time respectively, but 6 d of apparent retention time was needed for TP reaching discharge standard Class 2. In addtion, the wetland plants could obviously improve the TN and TP removal.

  15. On the Effects of Artificial Feeding on Bee Colony Dynamics: A Mathematical Model

    Science.gov (United States)

    Paiva, Juliana Pereira Lisboa Mohallem; Paiva, Henrique Mohallem; Esposito, Elisa; Morais, Michelle Manfrini

    2016-01-01

    This paper proposes a new mathematical model to evaluate the effects of artificial feeding on bee colony population dynamics. The proposed model is based on a classical framework and contains differential equations that describe the changes in the number of hive bees, forager bees, and brood cells, as a function of amounts of natural and artificial food. The model includes the following elements to characterize the artificial feeding scenario: a function to model the preference of the bees for natural food over artificial food; parameters to quantify the quality and palatability of artificial diets; a function to account for the efficiency of the foragers in gathering food under different environmental conditions; and a function to represent different approaches used by the beekeeper to feed the hive with artificial food. Simulated results are presented to illustrate the main characteristics of the model and its behavior under different scenarios. The model results are validated with experimental data from the literature involving four different artificial diets. A good match between simulated and experimental results was achieved. PMID:27875589

  16. 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

  17. 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.

  18. NONLINEAR MODELING AND CONTROLLING OF ARTIFICIAL MUSCLE SYSTEM USING NEURAL NETWORKS

    Institute of Scientific and Technical Information of China (English)

    Tian Sheping; Ding Guoqing; Yan Detian; Lin Liangming

    2004-01-01

    The pneumatic artificial muscles are widely used in the fields of medical robots,etc.Neural networks are applied to modeling and controlling of artificial muscle system.A single-joint artificial muscle test system is designed.The recursive prediction error (RPE) algorithm which yields faster convergence than back propagation (BP) algorithm is applied to train the neural networks.The realization of RPE algorithm is given.The difference of modeling of artificial muscles using neural networks with different input nodes and different hidden layer nodes is discussed.On this basis the nonlinear control scheme using neural networks for artificial muscle system has been introduced.The experimental results show that the nonlinear control scheme yields faster response and higher control accuracy than the traditional linear control scheme.

  19. Modelling CH4 emissions from arctic wetlands: effects of hydrological parameterization

    Directory of Open Access Journals (Sweden)

    P. M. Crill

    2007-09-01

    Full Text Available This study compares the CH4 fluxes from two arctic wetland sites of different annual temperatures during 2004 to 2006. The PEATLAND-VU model was used to simulate the emissions. The CH4 module of PEATLAND-VU is based on the Walter-Heimann model. The first site is located in northeast Siberia, Indigirka lowlands, Kytalyk reserve (70° N, 147° E in a continuous permafrost region with mean annual temperatures of –14.3°C. The other site is Stordalen mire in the eastern part of Lake Torneträsk (68° N, 19° E, ten kilometres east of Abisko, northern Sweden. It is located in a discontinuous permafrost region. Stordalen has a sub arctic climate with a mean annual temperature of –0.7°C. Model input consisted of observed temperature, precipitation and snow cover data. In all cases, modelled CH4 emissions show a direct correlation between variations in water table and soil temperature variations. The differences in CH4 emissions between the two sites are caused by different climate, hydrology, soil physical properties, vegetation type and NPP. For Kytalyk the simulated CH4 fluxes show similar trends during the growing season, having average values for 2004 to 2006 between 1.29–2.09 mg CH4 m−2 h−1. At Stordalen the simulated fluxes show a slightly lower average value for the same years (3.52 mg CH4 m−2 h−1 than the observed 4.7 mg CH4 m−2 h−1. The effect of the longer growing season at Stordalen is simulated correctly. Our study shows that modelling of arctic CH4 fluxes is improved by adding a relatively simple hydrological model that simulates the water table position from generic weather data. We conclude that CH4 fluxes at these sites are less sensitive to temperature variation than to water table variations. Furthermore, parameter uncertainty at site level in wetland CH4 process models is an important factor in large scale modelling of CH4 fluxes.

  20. Sunlight inactivation of viruses in open-water unit process treatment wetlands: modeling endogenous and exogenous inactivation rates.

    Science.gov (United States)

    Silverman, Andrea I; Nguyen, Mi T; Schilling, Iris E; Wenk, Jannis; Nelson, Kara L

    2015-03-03

    Sunlight inactivation is an important mode of disinfection for viruses in surface waters. In constructed wetlands, for example, open-water cells can be used to promote sunlight disinfection and remove pathogenic viruses from wastewater. To aid in the design of these systems, we developed predictive models of virus attenuation that account for endogenous and exogenous sunlight-mediated inactivation mechanisms. Inactivation rate models were developed for two viruses, MS2 and poliovirus type 3; laboratory- and field-scale experiments were conducted to evaluate the models' ability to estimate inactivation rates in a pilot-scale, open-water, unit-process wetland cell. Endogenous inactivation rates were modeled using either photoaction spectra or total, incident UVB irradiance. Exogenous inactivation rates were modeled on the basis of virus susceptibilities to singlet oxygen. Results from both laboratory- and field-scale experiments showed good agreement between measured and modeled inactivation rates. The modeling approach presented here can be applied to any sunlit surface water and utilizes easily measured inputs such as depth, solar irradiance, water matrix absorbance, singlet oxygen concentration, and the virus-specific apparent second-order rate constant with singlet oxygen (k2). Interestingly, the MS2 k2 in the open-water wetland was found to be significantly larger than k2 observed in other waters in previous studies. Examples of how the model can be used to design and optimize natural treatment systems for virus inactivation are provided.

  1. From hydrodynamic to hydrological modelling: Investigating long-term hydrological regimes of key wetlands in the Macquarie Marshes, a semi-arid lowland floodplain in Australia

    Science.gov (United States)

    Wen, Li; Macdonald, Rohan; Morrison, Tim; Hameed, Tahir; Saintilan, Neil; Ling, Joanne

    2013-09-01

    The Macquarie Marshes is an intermittently flooded wetland complex covering nearly 200,000 ha. It is one of the largest semi-permanent wetland systems in the Murray-Darling Basin, Australia, and portions of the Marshes are listed as internationally important under the Ramsar Convention. Previous studies indicate that the Marshes have undergone accelerated ecological degradation since the 1980s. The ecological degradation is documented in declining biodiversity, encroaching of terrestrial species, colonisation of exotic species, and deterioration of floodplain forests. There is strong evidence that reduction in river flows is the principal cause of the decrease in ecological values. Although the streams are relatively well gauged and modelled, the lack of hydrological records within the Marshes hampers any attempts to quantitatively investigate the relationship between hydrological variation and ecosystem integrity. To enable a better understanding of the long-term hydrological variations within the key wetland systems, and in particular, to investigate the impacts of the different water management policies (e.g. environmental water) on wetlands, a river system model including the main wetland systems was needed. The morphological complex nature of the Marshes means that the approximation of hydrological regimes within wetlands using stream hydrographs would have been difficult and inaccurate. In this study, we built a coupled 1D/2D MIKE FLOOD floodplain hydrodynamic model based on a 1 m DEM derived from a LiDAR survey. Hydrological characteristics of key constituent wetlands such as the correlation between water level and inundation area, relationships between stream and wetlands and among wetlands were estimated using time series extracted from hydrodynamic simulations. These relationships were then introduced into the existing river hydrological model (IQQM) to represent the wetlands. The model was used in this study to simulate the daily behaviours of inflow

  2. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2009-07-01

    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)

  4. 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

  5. A new biogeochemical model to simulate regional scale carbon emission from lakes, ponds and wetlands

    Science.gov (United States)

    Bayer, Tina; Brakebusch, Matthias; Gustafsson, Erik; Beer, Christian

    2016-04-01

    Small aquatic systems are receiving increasing attention for their role in global carbon cycling. For instance, lakes and ponds in permafrost are net emitters of carbon to the atmosphere, and their capacity to process and emit carbon is significant on a landscape scale, with a global flux of 8-103 Tg methane per year which amounts to 5%-30% of all natural methane emissions (Bastviken et al 2011). However, due to the spatial and temporal highly localised character of freshwater methane emissions, fluxes remain poorly qualified and are difficult to upscale based on field data alone. While many models exist to model carbon cycling in individual lakes and ponds, we perceived a lack of models that can work on a larger scale, over a range of latitudes, and simulate regional carbon emission from a large number of lakes, ponds and wetlands. Therefore our objective was to develop a model that can simulate carbon dioxide and methane emission from freshwaters on a regional scale. Our resulting model provides an additional tool to assess current aquatic carbon emissions as well as project future responses to changes in climatic drivers. To this effect, we have combined an existing large-scale hydrological model (the Variable Infiltration Capacity Macroscale Hydrologic Model (VIC), Liang & Lettenmaier 1994), an aquatic biogeochemical model (BALTSEM, Savchuk et al., 2012; Gustafsson et al., 2014) and developed a new methane module for lakes. The resulting new process-based biogeochemical model is designed to model aquatic carbon emission on a regional scale, and to perform well in high-latitude environments. Our model includes carbon, oxygen and nutrient cycling in lake water and sediments, primary production and methanogenesis. Results of calibration and validation of the model in two catchments (Torne-Kalix in Northern Sweden and of a large arctic river catchment) will be presented.

  6. Improving model prediction reliability through enhanced representation of wetland soil processes and constrained model auto calibration - A paired watershed study

    Science.gov (United States)

    Sharifi, Amirreza; Lang, Megan W.; McCarty, Gregory W.; Sadeghi, Ali M.; Lee, Sangchul; Yen, Haw; Rabenhorst, Martin C.; Jeong, Jaehak; Yeo, In-Young

    2016-10-01

    Process based, distributed watershed models possess a large number of parameters that are not directly measured in field and need to be calibrated, in most cases through matching modeled in-stream fluxes with monitored data. Recently, concern has been raised regarding the reliability of this common calibration practice, because models that are deemed to be adequately calibrated based on commonly used metrics (e.g., Nash Sutcliffe efficiency) may not realistically represent intra-watershed responses or fluxes. Such shortcomings stem from the use of an evaluation criteria that only concerns the global in-stream responses of the model without investigating intra-watershed responses. In this study, we introduce a modification to the Soil and Water Assessment Tool (SWAT) model, and a new calibration technique that collectively reduce the chance of misrepresenting intra-watershed responses. The SWAT model was modified to better represent NO3 cycling in soils with various degrees of water holding capacity. The new calibration tool has the capacity to calibrate paired watersheds simultaneously within a single framework. It was found that when both proposed methodologies were applied jointly to two paired watersheds on the Delmarva Peninsula, the performance of the models as judged based on conventional metrics suffered, however, the intra-watershed responses (e.g., mass of NO3 lost to denitrification) in the two models automatically converged to realistic sums. This approach also demonstrates the capacity to spatially distinguish areas of high denitrification potential, an ability that has implications for improved management of prior converted wetlands under crop production and for identifying prominent areas for wetland restoration.

  7. 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...

  8. 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

  9. Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model

    National Research Council Canada - National Science Library

    Chen, Hui; Xiong, Shenghua; Ren, Xuan

    2014-01-01

    ... 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...

  10. A simple, closed-form, mathematical model for gas exchange in microchannel artificial lungs.

    Science.gov (United States)

    Potkay, Joseph A

    2013-06-01

    Microfabrication techniques are attractive for constructing artificial lungs due to the ability to create features similar in size to those in the natural lung. However, a simple and intuitive mathematical model capable of accurately predicting the gas exchange performance of microchannel artificial lungs does not currently exist. Such a model is critical to understanding and optimizing these devices. Here, we describe a simple, closed-form mathematical model for gas exchange in microchannel artificial lungs and qualify it through application to experimental data from several research groups. We utilize lumped parameters and several assumptions to obtain a closed-form set of equations that describe gas exchange. This work is intended to augment computational models by providing a more intuitive, albeit potentially less accurate, understanding of the operation and trade-offs inherent in microchannel artificial lung devices.

  11. 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.

  12. Dynamic modeling of total suspended solids in the wetland Jaboque, Bogotá (Colombia

    Directory of Open Access Journals (Sweden)

    Julio Eduardo Beltrán-Vargas

    2012-12-01

    Full Text Available We propose a dynamic simulation model to provide a general explanation for the behavior of total suspended solids in the Jaboque wetland (Bogotá DC. The analyses were performed in three areas with distinct physical and chemical characteristics. The model predicted concentrations of total suspended solids in the months of April, May, June, October and November. Values ranged from 85 mg L-1 and 101 mg L-1 with an average of 65.12 mg L-1 with a hydraulic time retention between eight and nine days per year for the first area, the second area between 57 and 69 with an average of 50 mg L-1 with a hydraulic time between 20 and 23 days per year and in the third area between 56 mg L-1 and 67 mg L-1 with a mean of 48.8 mg L-1 and a hydraulic retention time between 24 and 26 days per year. The months of December, January, February, August and September showed a tendency to have lower values. The estimated values of total suspended solids based on our model had an acceptable correspondence, R2 = 0.95, 0.71, 0.67, with the actual values in all cases. The relative error for each area was 0.10, 0.20 and 0.26, demonstrating that the model does not overestimate the results.

  13. PERCEPTION OF MEDICAL STUDENTS TOWARDS ARTIFICIAL BONES AND POP MODELS OF VISCERA

    OpenAIRE

    Sumit Tulshidas Patil; Nazia Quadir; Rashmi Deopujari; Vivekanand Gajbhiye

    2015-01-01

    Background: In learning of anatomy, bones and viscera are very important. Now days, artificial bones are replacing the original bones for study purpose due to unavailability. Original viscera are available for students only at dissection hours. So we have tried to find out perception of medical students towards artificial bones and POP models of viscera. Materials and Methods: We had prepared a questionnaire consisting of 20 questions, 10 related to bones and 10 related to the POP models o...

  14. Soil Accretionary Dynamics, Sea-Level Rise and the Survival of Wetlands in Venice Lagoon: A Field and Modelling Approach

    Science.gov (United States)

    Day, J. W.; Rybczyk, J.; Scarton, F.; Rismondo, A.; Are, D.; Cecconi, G.

    1999-11-01

    Over the past century, Venice Lagoon (Italy) has experienced a high rate of wetland loss. To gain an understanding of the factors leading to this loss, from March 1993 until May 1996 the soil accretionary dynamics of these wetlands were studied. Vertical accretion, short term sedimentation, soil vertical elevation change and horizontal shoreline change were measured at several sites with varying sediment availability and wave energy. Short term sedimentation averaged 3-7 g dry m -2day -1per site with a maximum of 76 g m -2 day -1. The highest values were measured during strong pulsing events, such as storms and river floods, that mobilized and transported suspended sediments. Accretion ranged from 2-23 mm yr -1and soil elevation change ranged from -32 to 13·8 mm yr -1. The sites with highest accretion were near a river mouth and in an area where strong wave energy resuspended bottom sediments that were deposited on the marsh surface. A marsh created with dredged spoil had a high rate of elevation loss, probably due mainly to compaction. Shoreline retreat and expansion of tidal channels also occurred at several sites due to high wave energy and a greater tidal prism. The current rate of elevation gain at some sites was not sufficient to offset relative sea-level rise. The results suggest that reduction of wave energy and increasing sediment availability are needed to offset wetland loss in different areas of the lagoon. Using the data collected as part of this project, we developed a wetland elevation model designed to predict the effect of increasing rates of eustatic sea-level rise on wetland sustainability. The advantage of this model, in conjunction with measured short-term rates of soil elevation change, to determine sustainability is that the model integrates the effects of long term processes (e.g. compaction and decomposition) and takes into account feedback mechanisms that affect elevation. Specifically, changes in elevation can result in changes in

  15. Isolation and characterization of Magnetospirillum sp strain 15-1 as a representative anaerobic toluene-degrader from a constructed wetland model

    DEFF Research Database (Denmark)

    Meyer-Cifuentes, Ingrid; Lavanchy, Paula Maria Martinez; Marin-Cevada, Vianey

    2017-01-01

    Previously, Planted Fixed-Bed Reactors (PFRs) have been used to investigate microbial toluene removal in the rhizosphere of constructed wetlands. Aerobic toluene degradation was predominant in these model systems although bulk redox conditions were hypoxic to anoxic. However, culture...

  16. A LOW-COST THREE-DIMENSIONAL SAMPLE COLLECTION ARRAY TO EVALUATE AND MONITOR CONSTRUCTED WETLANDS

    Science.gov (United States)

    Artificially constructed wetlands are gaining acceptance as a low cost treatment alternative to remove a number of undesirable constituents from water. Wetlands can be used to physically remove compounds such as suspended solids through sedimentation. Dissolved nutrients, biochemical oxygen demand, ...

  17. Screening of the Salt Tolerant Plants for High Salinity Wastewater Treatment by the Artificial Wetland%高盐废水人工湿地处理中耐盐植物的筛选

    Institute of Scientific and Technical Information of China (English)

    尚克春; 刘宪斌; 陈晓英

    2014-01-01

    Tanggu, as the core area in Binhai New Area, is currently one of the fastest developing areas in Tianjin City. Because of the saline alkali soil and other natural conditions, wastewater reuse is restricted by high salinity. The removal of high concentration chloride by Phrag-mites australis, Suaeda salsa, Artemisia anethifolia Weber, Iris wilsonii, Salicornia europaea, and Spartina anglica in light polluted water was compared by the simulation experiment of artificial wetland. The plants with stronger removal ability were selected and the ecosystem condi-tion with maximum removal rate was determined. The results showed that the removal effect of chloride by salt-tolerant plants in artificial wetland was:Phragmites australis>Suaeda salsa>Artemisia anethifolia>Iris wilsonii>Salicornia europaea>Spartina anglica. The removal efficiency reached balance after four days. This study provided a scientific basis for the high salinity wastewater treatment by artificial wet-land.%天津塘沽作为滨海新区核心区,是目前天津发展最快的地区之一。由于本区盐碱土壤等自然条件,污水中盐分含量较高,制约了废水的回用。本文通过模拟人工湿地实验,比较了芦苇(Phragmites australis)、盐地碱篷(Suaeda salsa)、碱蒿(Artemisia anethifo原lia)、黄花鸢尾(Iris wilsonii)、盐角草(Salicornia europaea)和大米草(Spartina anglica)等耐盐植物对轻污染水体中高浓度氯离子的去除能力,筛选出去除能力较强的植物,并确定植物对盐分去除率达到最大时的生态系统条件。结果表明,适合人工湿地的耐盐碱植物对氯离子的去除效果依次为:芦苇>盐地碱篷>碱蒿>黄花鸢尾>盐角草>大米草,停留时间一般在第4d时可达到平衡。该研究为利用人工湿地处理高盐废水提供了科学依据。

  18. Modelling and evaluation of nitrogen removal performance in subsurface flow and free water surface constructed wetlands.

    Science.gov (United States)

    Tunçsiper, B; Ayaz, S C; Akça, L

    2006-01-01

    With the aim of protecting drinking water sources in rural regions, pilot-scale subsurface water flow (SSF) and free water surface flow (FWS) constructed wetland systems were evaluated for removal efficiencies of nitrogenous pollutants in tertiary stage treated wastewaters (effluent from the Pasaköy biological nutrient removal plant). Five different hydraulic application rates and emergent (Canna, Cyperus, Typhia sp., Phragmites sp., Juncus, Poaceae, Paspalum and Iris) and floating (Pistia, Salvina and Lemna) plant species were assayed. The average annual NH4-N, NO3-N and organic-N treatment efficiencies were 81, 40 and 74% in SSFs and 76, 59 and 75% in FWSs, respectively. Two types of the models (first-order plug flow and multiple regression) were tried to estimate the system performances. Nitrification, denitrification and ammonification rate constants (k20) values in SSF and FWS systems were 0.898 d-1 and 0.541 d(-1), 0.486 d(-1) and 0.502 d(-1), 0.986 d(-1) and 0.908, respectively. Results show that the first-order plug flow model clearly estimates slightly higher or lower values than observed when compared with the other model.

  19. Aerobic Toluene Degraders in the Rhizosphere of a Constructed Wetland Model Show Diurnal Polyhydroxyalkanoate Metabolism.

    Science.gov (United States)

    Lünsmann, Vanessa; Kappelmeyer, Uwe; Taubert, Anja; Nijenhuis, Ivonne; von Bergen, Martin; Heipieper, Hermann J; Müller, Jochen A; Jehmlich, Nico

    2016-07-15

    Constructed wetlands (CWs) are successfully applied for the treatment of waters contaminated with aromatic compounds. In these systems, plants provide oxygen and root exudates to the rhizosphere and thereby stimulate microbial degradation processes. Root exudation of oxygen and organic compounds depends on photosynthetic activity and thus may show day-night fluctuations. While diurnal changes in CW effluent composition have been observed, information on respective fluctuations of bacterial activity are scarce. We investigated microbial processes in a CW model system treating toluene-contaminated water which showed diurnal oscillations of oxygen concentrations using metaproteomics. Quantitative real-time PCR was applied to assess diurnal expression patterns of genes involved in aerobic and anaerobic toluene degradation. We observed stable aerobic toluene turnover by Burkholderiales during the day and night. Polyhydroxyalkanoate synthesis was upregulated in these bacteria during the day, suggesting that they additionally feed on organic root exudates while reutilizing the stored carbon compounds during the night via the glyoxylate cycle. Although mRNA copies encoding the anaerobic enzyme benzylsuccinate synthase (bssA) were relatively abundant and increased slightly at night, the corresponding protein could not be detected in the CW model system. Our study provides insights into diurnal patterns of microbial processes occurring in the rhizosphere of an aquatic ecosystem. Constructed wetlands are a well-established and cost-efficient option for the bioremediation of contaminated waters. While it is commonly accepted knowledge that the function of CWs is determined by the interplay of plants and microorganisms, the detailed molecular processes are considered a black box. Here, we used a well-characterized CW model system treating toluene-contaminated water to investigate the microbial processes influenced by diurnal plant root exudation. Our results indicated stable

  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. Scaling wetland green infrastructure?practices to watersheds using modeling approaches

    Science.gov (United States)

    Green infrastructure practices are typically implemented at the plot or local scale. Wetlands in the landscape can serve important functions at these scales and can mediate biogeochemical and hydrological processes, particularly when juxtaposed with low impact development (LID)....

  2. 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. ...

  3. 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.

  4. Application of Projection Pursuit Evaluation Model Based on Real-Coded Accelerating Genetic Algorithm in Evaluating Wetland Soil Quality Variations in the Sanjiang Plain,China

    Institute of Scientific and Technical Information of China (English)

    FU QIANG; XIE YONGGANG; WEI ZIMIN

    2003-01-01

    A new technique of dimension reduction named projection pursuit is applied to model and evaluatewetland soil quality variations in the Sanjiang Plain, Helongjiang Province, China. By adopting the im-proved real-coded accelerating genetic algorithm (RAGA), the projection direction is optimized and multi-dimensional indexes are converted into low-dimensional space. Classification of wetland soils and evaluationof wetland soil quality variations are realized by pursuing optimum projection direction and projection func-tion value. Therefore, by adopting this new method, any possible human interference can be avoided andsound results can be achieved in researching quality changes and classification of wetland soils.

  5. STUDY ON THERMODYNAMIC MODEL OF A COMPRESSOR WITH ARTIFICIAL NEURAL NETWORKS

    Institute of Scientific and Technical Information of China (English)

    1999-01-01

    A new compressor thermodynamic model is set up. Artificial neural networks(ANN) which have self-adjusting functions are adopted to calculate volumetric efficiency and electrical efficiency of a compressor. The new compressor model composed of the theoretical model and ANN reaches more precise results than traditional ones. Furthermore, the new compressor model is of better flexibility in a large scale.

  6. An exemplar model of performance in the artificial grammar task: holographic representation.

    Science.gov (United States)

    Jamieson, Randall K; Hauri, Brian R

    2012-06-01

    We apply a multitrace model of memory to explain performance in the artificial grammar task. The model blends the convolution method for representation from Jones and Mewhort's BEAGLE model (Jones, M. N., & Mewhort, D. J. K. (2007). Representing word meaning and order information in a composite holographic lexicon. Psychological Review, 114, 1-37) of semantic memory with the multitrace storage and retrieval model from Hintzman's MINERVA 2 model (Hintzman, D. L. (1986). "Schema abstraction" in a multiple-trace memory model. Psychological Review, 93, 411-428) of episodic memory. We report an artificial grammar experiment, and we fit the model to those data at the level of individual items. We argue that performance in the artificial grammar task is best understood as a process of retrospective inference from memory.

  7. Wetland Loss.

    Science.gov (United States)

    Barrett, Marilyn

    1994-01-01

    Examines what wetland conservation means to different groups of Louisiana's coastal residents. Describes coastal resources, reasons for their deterioration, conservation efforts, and the impact of a public perception that conservation of wetlands is closely tied to conservation of the existing lifestyle. (LZ)

  8. Freshwater Wetlands.

    Science.gov (United States)

    Naturescope, 1986

    1986-01-01

    Provides descriptions about freshwater wetlands, such as marshes, swamps, and bogs. Contains three learning activities which deal with unusual wetland plants, the animals and plants in a typical marsh, and the effects of a draught on a swamp. Included are reproducible handouts and worksheets for two of the activities. (TW)

  9. Ecophysiology of wetland plant roots: A modelling comparison of aeration in relation to species distribution

    Science.gov (United States)

    Sorrell, B.K.; Mendelssohn, I.A.; McKee, K.L.; Woods, R.A.

    2000-01-01

    This study examined the potential for inter-specific differences in root aeration to determine wetland plant distribution in nature. We compared aeration in species that differ in the type of sediment and depth of water they colonize. Differences in root anatomy, structure and physiology were applied to aeration models that predicted the maximum possible aerobic lengths and development of anoxic zones in primary adventitious roots. Differences in anatomy and metabolism that provided higher axial fluxes of oxygen allowed deeper root growth in species that favour more reducing sediments and deeper water. Modelling identified factors that affected growth in anoxic soils through their effects on aeration. These included lateral root formation, which occurred at the expense of extension of the primary root because of the additional respiratory demand they imposed, reducing oxygen fluxes to the tip and stele, and the development of stelar anoxia. However, changes in sediment oxygen demand had little detectable effect on aeration in the primary roots due to their low wall permeability and high surface impedance, but appeared to reduce internal oxygen availability by accelerating loss from laterals. The development of pressurized convective gas flow in shoots and rhizomes was also found to be important in assisting root aeration, as it maintained higher basal oxygen concentrations at the rhizome-root junctions in species growing into deep water. (C) 2000 Annals of Botany Company.

  10. Developing an algorithm for enhancement of a digital terrain model for a densely vegetated floodplain wetland

    Science.gov (United States)

    Mirosław-Świątek, Dorota; Szporak-Wasilewska, Sylwia; Michałowski, Robert; Kardel, Ignacy; Grygoruk, Mateusz

    2016-07-01

    Airborne laser scanning survey data were conducted with a scanning density of 4 points/m2 to accurately map the surface of a unique central European complex of wetlands: the lower Biebrza River valley (Poland). A method to correct a degrading effect of vegetation (so-called "vegetation effect") on digital terrain models (DTMs) was applied utilizing remotely sensed images, real-time kinematic global positioning system elevation measurements, topographical surveys, and vegetation height measurements. Geographic object-based image analysis (GEOBIA) was performed to map vegetation within the study area that was used as categories from which vegetation height information was derived for the DTM correction. The final DTM was compared with a model obtained, where additional correction of the "vegetation effect" was neglected. A comparison between corrected and uncorrected DTMs demonstrated the importance of accurate topography through a simple presentation of the discrepancies arising in features of the flood using various DTM products. An overall map classification accuracy of 80% was attained with the use of GEOBIA. Correction factors developed for various types of the vegetation reached values from 0.08 up to 0.92 m and were dependent on the vegetation type.

  11. Biokinetic model for nitrogen removal in free water surface constructed wetlands.

    Science.gov (United States)

    Gargallo, S; Martín, M; Oliver, N; Hernández-Crespo, C

    2017-06-01

    In this article, a mechanistic biokinetic model for nitrogen removal in free water surface constructed wetlands treating eutrophic water was developed, including organic matter performance due to its importance in nitrogen removal by denitrification. Ten components and fourteen processes were introduced in order to simulate the forms of nitrogen and organic matter, the mechanisms of autotrophic and heterotrophic microorganisms in both aerobic and anoxic conditions, as well as macrophytes nitrogen uptake and release. Dissolved oxygen was introduced as an input variable with a time step of 0.5days for mimicking eutrophic environments: aerobic conditions were assigned during daylight hours and anoxic conditions during the night. The sensitivity analysis showed that the most influential parameters were those related to the growth of heterotrophic and autotrophic microorganisms. The model was properly calibrated and validated in two full scale systems working in real conditions for treating eutrophic water from Lake L'Albufera (València). In the studied systems, ammonium was mainly removed by the growth of autotrophic microorganisms (nitrification) whereas nitrate was removed by the anoxic growth of heterotrophic microorganisms (denitrification). Macrophyte uptake removed between 9 and 19% of the ammonium entering to the systems, although degradation of dead standing macrophytes returned a significant part to water column.

  12. 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.

  13. Modeling land use and land cover changes in a vulnerable coastal region using artificial neural networks and cellular automata.

    Science.gov (United States)

    Qiang, Yi; Lam, Nina S N

    2015-03-01

    As one of the most vulnerable coasts in the continental USA, the Lower Mississippi River Basin (LMRB) region has endured numerous hazards over the past decades. The sustainability of this region has drawn great attention from the international, national, and local communities, wanting to understand how the region as a system develops under intense interplay between the natural and human factors. A major problem in this deltaic region is significant land loss over the years due to a combination of natural and human factors. The main scientific and management questions are what factors contribute to the land use land cover (LULC) changes in this region, can we model the changes, and how would the LULC look like in the future given the current factors? This study analyzed the LULC changes of the region between 1996 and 2006 by utilizing an artificial neural network (ANN) to derive the LULC change rules from 15 human and natural variables. The rules were then used to simulate future scenarios in a cellular automation model. A stochastic element was added in the model to represent factors that were not included in the current model. The analysis was conducted for two sub-regions in the study area for comparison. The results show that the derived ANN models could simulate the LULC changes with a high degree of accuracy (above 92 % on average). A total loss of 263 km(2) in wetlands from 2006 to 2016 was projected, whereas the trend of forest loss will cease. These scenarios provide useful information to decision makers for better planning and management of the region.

  14. McGill Wetland Model: evaluation of a peatland carbon simulator developed for global assessments

    Directory of Open Access Journals (Sweden)

    F. St-Hilaire

    2008-04-01

    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 [g C m−2 d−1]: 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 modest temperature increases could lead to converting the bog from a sink to a source of CO2. General weaknesses and further developments of MWM are discussed.

  15. 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.

  16. Comparative characterization of the microbial diversities of an artificial microbialite model and a natural stromatolite.

    Science.gov (United States)

    Havemann, Stephanie A; Foster, Jamie S

    2008-12-01

    Microbialites are organosedimentary structures that result from the trapping, binding, and lithification of sediments by microbial mat communities. In this study we developed a model artificial microbialite system derived from natural stromatolites, a type of microbialite, collected from Exuma Sound, Bahamas. We demonstrated that the morphology of the artificial microbialite was consistent with that of the natural system in that there was a multilayer community with a pronounced biofilm on the surface, a concentrated layer of filamentous cyanobacteria in the top 5 mm, and a lithified layer of fused oolitic sand grains in the subsurface. The fused grain layer was comprised predominantly of the calcium carbonate polymorph aragonite, which corresponded to the composition of the Bahamian stromatolites. The microbial diversity of the artificial microbialites and that of natural stromatolites were also compared using automated ribosomal intergenic spacer analysis (ARISA) and 16S rRNA gene sequencing. The ARISA profiling indicated that the Shannon indices of the two communities were comparable and that the overall diversity was not significantly lower in the artificial microbialite model. Bacterial clone libraries generated from each of the three artificial microbialite layers and natural stromatolites indicated that the cyanobacterial and crust layers most closely resembled the ecotypes detected in the natural stromatolites and were dominated by Proteobacteria and Cyanobacteria. We propose that such model artificial microbialites can serve as experimental analogues for natural stromatolites.

  17. 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...

  18. 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.

  19. NEW ANTIMICROBIAL SENSITIVITY TESTS OF BIOFILM OF STREPTOCOCCUS MUTANS IN ARTIFICIAL MOUTH MODEL

    Institute of Scientific and Technical Information of China (English)

    李鸣宇; 汪俊; 刘正; 朱彩莲

    2004-01-01

    Objective To develop a new antimicrobial sensitivity test model for oral products in vitro.Methods A biofilm artificial mouth model for antimicrobial sensitivity tests was established by modifying the LKI chromatography chamber. Using sodium fluoride and Tea polyphenol as antimicrobial agent and Streptococcus mutans as target, sensitivity tests were studied. Results The modeling biofilm assay resulted in a MIC of 1.28mg/ml for fluoride against S. mutans, which was 32 times the MIC for broth maco-dilution method. The differential resistance of bacteria bioflim to antimicrobial agent relative to planktonic cells was also demonstrated. Conclusion The biofilm artificial mouth model may be useful in oral products test.

  20. 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

  1. Microbial Toluene Removal in Hypoxic Model Constructed Wetlands Occurs Predominantly via the Ring Monooxygenation Pathway.

    Science.gov (United States)

    Martínez-Lavanchy, P M; Chen, Z; Lünsmann, V; Marin-Cevada, V; Vilchez-Vargas, R; Pieper, D H; Reiche, N; Kappelmeyer, U; Imparato, V; Junca, H; Nijenhuis, I; Müller, J A; Kuschk, P; Heipieper, H J

    2015-09-01

    In the present study, microbial toluene degradation in controlled constructed wetland model systems, planted fixed-bed reactors (PFRs), was queried with DNA-based methods in combination with stable isotope fractionation analysis and characterization of toluene-degrading microbial isolates. Two PFR replicates were operated with toluene as the sole external carbon and electron source for 2 years. The bulk redox conditions in these systems were hypoxic to anoxic. The autochthonous bacterial communities, as analyzed by Illumina sequencing of 16S rRNA gene amplicons, were mainly comprised of the families Xanthomonadaceae, Comamonadaceae, and Burkholderiaceae, plus Rhodospirillaceae in one of the PFR replicates. DNA microarray analyses of the catabolic potentials for aromatic compound degradation suggested the presence of the ring monooxygenation pathway in both systems, as well as the anaerobic toluene pathway in the PFR replicate with a high abundance of Rhodospirillaceae. The presence of catabolic genes encoding the ring monooxygenation pathway was verified by quantitative PCR analysis, utilizing the obtained toluene-degrading isolates as references. Stable isotope fractionation analysis showed low-level of carbon fractionation and only minimal hydrogen fractionation in both PFRs, which matches the fractionation signatures of monooxygenation and dioxygenation. In combination with the results of the DNA-based analyses, this suggests that toluene degradation occurs predominantly via ring monooxygenation in the PFRs. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

  2. RBF-Type Artificial Neural Network Model Applied in Alloy Design of Steels

    Institute of Scientific and Technical Information of China (English)

    YOU Wei; LIU Ya-xiu; BAI Bing-zhe; FANG Hong-sheng

    2008-01-01

    RBF model, a new type of artificial neural network model was developed to design the content of carbon in low-alloy engineering steels. The errors of the ANN model are. MSE 0. 052 1, MSRE 17. 85%, and VOF 1. 932 9. The results obtained are satisfactory. The method is a powerful aid for designing new steels.

  3. [Building artificial neural networks model on portable NIR integrity wheat component measuring apparatus].

    Science.gov (United States)

    Ji, Hai-yan; Wen, Ming; Hao, Bin

    2006-01-01

    The quantitative analysis model of protein in integrity wheat was built by three layers back propagation artificial neural networks for portable near infrared (NIR) integrity wheat component measuring apparatus. The structure diagram of integrity wheat component measuring apparatus, light route structure of apparatus and the spectrum of integrity wheat were given in the present paper. The theory of artificial neural network was briefly introduced and the results of quantitative analysis model of protein were given. For calibration set and prediction set, the correlation coefficient was 0.90 and 0.96 respectively; the relative standard deviation is 3.77% and 4.46% respectively. Because of the influence of light route structure, electrical circuit, and integrity sample forms on the measuring apparatus, some nonlinearity exists between the spectral parameters and chemical values. The results of artificial neural networks nonlinear model were superior to linear model.

  4. Modeling organic matter and nitrogen removal from domestic wastewater in a pilot-scale vertical subsurface flow constructed wetland.

    Science.gov (United States)

    Bustillo-Lecompte, Ciro Fernando; Mehrvar, Mehrab; Quiñones-Bolaños, Edgar; Castro-Faccetti, Claudia Fernanda

    2016-01-01

    Constructed wetlands have become an attractive alternative for wastewater treatment. However, there is not a globally accepted mathematical model to predict their performance. In this study, the VS2DTI software was used to predict the effluent biochemical oxygen demand (BOD) and total nitrogen (TN) in a pilot-scale vertical flow constructed wetland (VFCW) treating domestic wastewater. After a 5-week adaptation period, the pilot system was monitored for another 6 weeks. Experiments were conducted at hydraulic retention times (HRTs) in the range of 2-4 days with Typha latifolia as the vegetation. The raw wastewater concentrations ranged between 144-430 and 122-283 mg L(-1) for BOD5 and TN, respectively. A first-order kinetic model coupled with the advection/dispersion and Richards' equations was proposed to predict the removal rates of BOD5 and TN from domestic wastewater. Two main physical processes were modeled in this study, porous material water flow and solute transport through the different layers of the VFCW to simulate the constructed wetland (CW) conditions. The model was calibrated based on the BOD5 and TN degradation constants. The model indicated that most of BOD and TN (88 and 92%, respectively) were removed through biological activity followed by adsorption. It was also observed that the evapotranspiration was seen to have a smaller impact. An additional data series of effluent BOD and TN was used for model validation. The residual analysis of the calibrated model showed a relatively random pattern, indicating a decent fit. Thus, the VS2DTI was found to be a useful tool for CW simulation.

  5. Forecasting Stock Exchange Movements Using Artificial Neural Network Models and Hybrid Models

    Science.gov (United States)

    Güreşen, Erkam; Kayakutlu, Gülgün

    Forecasting stock exchange rates is an important financial problem that is receiving increasing attention. During the last few years, a number of neural network models and hybrid models have been proposed for obtaining accurate prediction results, in an attempt to outperform the traditional linear and nonlinear approaches. This paper evaluates the effectiveness of neural network models; recurrent neural network (RNN), dynamic artificial neural network (DAN2) and the hybrid neural networks which use generalized autoregressive conditional heteroscedasticity (GARCH) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) to extract new input variables. The comparison for each model is done in two view points: MSE and MAD using real exchange daily rate values of Istanbul Stock Exchange (ISE) index XU10).

  6. Wetland InSAR

    Science.gov (United States)

    Wdowinski, S.; Kim, S.; Amelung, F.; Dixon, T.

    2006-12-01

    probably the densest stage network in the world (more than 200 stations), located 5-10 km from one another. The stage data is very important in evaluating the uncertainty of the InSAR observations. Stage data also allow us to tie the relative InSAR observations (water level changes) to absolute reference frame and to produce high spatial-resolution (10-100 m resolution) maps of absolute water levels. High resolution wetland interferograms also provide direct observations of flow patterns and flow discontinuities and serve as excellent constraints for high resolution flow models. Because many wetlands are located in coastal zones, the high spatial resolution InSAR observations provide an opportunity to study dynamic interaction of tides and freshwater inflow, and the role of vegetation resistance to surface water flow.

  7. Using chaotic artificial neural networks to model memory in the brain

    Science.gov (United States)

    Aram, Zainab; Jafari, Sajad; Ma, Jun; Sprott, Julien C.; Zendehrouh, Sareh; Pham, Viet-Thanh

    2017-03-01

    In the current study, a novel model for human memory is proposed based on the chaotic dynamics of artificial neural networks. This new model explains a biological fact about memory which is not yet explained by any other model: There are theories that the brain normally works in a chaotic mode, while during attention it shows ordered behavior. This model uses the periodic windows observed in a previously proposed model for the brain to store and then recollect the information.

  8. 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.

  9. Restoring Wetlands

    Institute of Scientific and Technical Information of China (English)

    WANG HAIRONG

    2011-01-01

    Watching flocks of waterfowl taking off and landing in the large expanse of wetland near his home is a favorite pastime of Li Qiwen a middle-aged primary school teacher in Weichang Township,Luobei County in Heilongjiang Province.The wetland is home to hundreds of species of birds,including rare white storks and red-crowned cranes,as well as more common geese and ducks.

  10. 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.

  11. Multiscale Modeling of Gene-Behavior Associations in an Artificial Neural Network Model of Cognitive Development.

    Science.gov (United States)

    Thomas, Michael S C; Forrester, Neil A; Ronald, Angelica

    2016-01-01

    In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such associations can be detected despite the remoteness of these levels of description, and the fact that behavior is the outcome of an extended developmental process involving interaction of the whole organism with a variable environment. Given that they have been detected, how do such associations inform cognitive-level theories? To investigate this question, we employed a multiscale computational model of development, using a sample domain drawn from the field of language acquisition. The model comprised an artificial neural network model of past-tense acquisition trained using the backpropagation learning algorithm, extended to incorporate population modeling and genetic algorithms. It included five levels of description-four internal: genetic, network, neurocomputation, behavior; and one external: environment. Since the mechanistic assumptions of the model were known and its operation was relatively transparent, we could evaluate whether cross-level associations gave an accurate picture of causal processes. We established that associations could be detected between artificial genes and behavioral variation, even under polygenic assumptions of a many-to-one relationship between genes and neurocomputational parameters, and when an experience-dependent developmental process interceded between the action of genes and the emergence of behavior. We evaluated these associations with respect to their specificity (to different behaviors, to function vs. structure), to their developmental stability, and to their replicability, as well as considering issues of missing heritability and gene-environment interactions. We argue that gene

  12. What Makes a Wetland a Wetland?

    Science.gov (United States)

    Naturescope, 1986

    1986-01-01

    Provides descriptions of and activities about various kinds of wetlands. Contains seven learning activities ranging from creating wetland scenes with picture cutouts to actually exploring a wetland. Includes reproducible handouts and worksheets for several of the activities. (TW)

  13. 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

  14. Review of artificial wetland treatment technique for initial rainwater runoff pollutant removal%人工湿地技术削减雨水初期径流污染负荷研究进展

    Institute of Scientific and Technical Information of China (English)

    钱嫦萍; 陈振楼; 曹承进; 王军

    2011-01-01

    拟从技术特点、技术流程、技术原理、技术参数、技术使用中存在的问题以及技术的应用前景等方面对雨水初期径流污染人工湿地技术进行分析和描述.介绍了该技术在国内和国外的研究现状,并展望了其未来研究方向.以期为我国城市雨水初期径流污染控制工程提供参考依据.%This paper analyzed and described the technical characteristics, technical procedures,technical principles, technical parameters, technical problems and technology prospects in the artificial wetland treatment. The development prospect of the technique at home and abroad was also anticipated. This investigation will provide references for the project of controlling the city black-odor river pollution.

  15. Using a simple mixing model to assess the role of riparian wetlands in moderating stream water temperatures

    Science.gov (United States)

    Dick, Jonathan; Tetzlaff, Doerthe; Soulsby, Chris

    2016-04-01

    Stream water temperature is a fundamental physical characteristic of riverine systems, influencing many processes; from biological productivity to many other aspects of water quality. Given climatic global warming projections, and the implications for stream thermal regimes, they are increasingly considered as part of river basin management plans. Along with the effects of energy exchanges at the water-air interface and riparian vegetation cover, advective heat transport from the different sources of water generating stream flow can strongly influence temperature within the stream channel. Riparian wetland areas are important geomorphic components of landscapes in many parts of the world, and are often a dominant source of stream flow during hydrological events. During wet periods large volumes of water may be displaced into stream channels via near-surface flow paths, which typically have high variability. In dry conditions, more groundwater with less variable temperatures dominate. The mixing of these waters can have great influence over the thermal regimes of streams over a range of flow conditions. Here, we present the use of a simple mixing model to predict daily mean stream water temperature on the basis of mixing groundwater and near surface riparian waters as the end-members in a 3.2km2 watershed in the Scottish Highlands. The resulting model fit was analysed against energy balance components and the spatial extent of the wetland to investigate the importance of energy-exchange in riparian wetlands in determining stream temperatures. Results showed generally good agreement between modelled results and measured temperatures under wet conditions. Model fit was generally better in winter than during the summer months (when the model under predicted temperatures), with a strong correlation evident between net radiation and the fit of the model. This indicated the limited skill of the simple mixing structure to account for the increased importance of energy

  16. Applications of artificial neural networks for microbial water quality modeling

    Energy Technology Data Exchange (ETDEWEB)

    Brion, G.M.; Lingireddy, S. [Univ. of Kentucky, Dept. of Civil Engineering, Lexington, Kentucky (United States)]. E-mail: gbrion@engr.uky.edu

    2002-06-15

    There has been a significant shift in the recent past towards protecting chemical and microbial quality of source waters rather than developing advanced methods to treat heavily polluted water. The key to successful best management practices in protecting the source waters is to identify sources of non-point pollution and their collective impact on the quality of water at the intake. This article presents a few successful applications where artificial neural networks (ANN) have proven to be the useful mathematical tools in correlating the nonlinear relationships between routinely measured parameters (such as rainfall, turbidity, fecal coliforms etc.) and quality of source waters and/or nature of fecal sources. These applications include, prediction of peak concentrations of Giardia and Cryptosporidium, sorting of fecal sources (e.g. agricultural animals vs. urban animals), predicting relative ages of the runoff sources, identifying the potential for sewage contamination. The ability of ANNs to work with complex, inter-related multiparameter databases, and provide superior predictive power in non-linear relationships has been the key for their successful application to microbial water quality studies. (author)

  17. Virginia ESI: Wetlands (Wetland Polygons)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains vector polygons representing the coastal wetlands for Virginia, classified according to the Environmental Sensitivity Index (ESI)...

  18. Bayesian model selection applied to artificial neural networks used for water resources modeling

    Science.gov (United States)

    Kingston, Greer B.; Maier, Holger R.; Lambert, Martin F.

    2008-04-01

    Artificial neural networks (ANNs) have proven to be extremely valuable tools in the field of water resources engineering. However, one of the most difficult tasks in developing an ANN is determining the optimum level of complexity required to model a given problem, as there is no formal systematic model selection method. This paper presents a Bayesian model selection (BMS) method for ANNs that provides an objective approach for comparing models of varying complexity in order to select the most appropriate ANN structure. The approach uses Markov Chain Monte Carlo posterior simulations to estimate the evidence in favor of competing models and, in this study, three known methods for doing this are compared in terms of their suitability for being incorporated into the proposed BMS framework for ANNs. However, it is acknowledged that it can be particularly difficult to accurately estimate the evidence of ANN models. Therefore, the proposed BMS approach for ANNs incorporates a further check of the evidence results by inspecting the marginal posterior distributions of the hidden-to-output layer weights, which unambiguously indicate any redundancies in the hidden layer nodes. The fact that this check is available is one of the greatest advantages of the proposed approach over conventional model selection methods, which do not provide such a test and instead rely on the modeler's subjective choice of selection criterion. The advantages of a total Bayesian approach to ANN development, including training and model selection, are demonstrated on two synthetic and one real world water resources case study.

  19. Macrophytes may not contribute significantly to removal of nutrients, pharmaceuticals, and antibiotic resistance in model surface constructed wetlands.

    Science.gov (United States)

    Cardinal, Pascal; Anderson, Julie C; Carlson, Jules C; Low, Jennifer E; Challis, Jonathan K; Beattie, Sarah A; Bartel, Caitlin N; Elliott, Ashley D; Montero, Oscar F; Lokesh, Sheetal; Favreau, Alex; Kozlova, Tatiana A; Knapp, Charles W; Hanson, Mark L; Wong, Charles S

    2014-06-01

    Outdoor shallow wetland mesocosms, designed to simulate surface constructed wetlands to improve lagoon wastewater treatment, were used to assess the role of macrophytes in the dissipation of wastewater nutrients, selected pharmaceuticals, and antibiotic resistance genes (ARGs). Specifically, mesocosms were established with or without populations of Typha spp. (cattails), Myriophyllum sibiricum (northern water milfoil), and Utricularia vulgaris (bladderwort). Following macrophyte establishment, mesocosms were seeded with ARG-bearing organisms from a local wastewater lagoon, and treated with a single pulse of artificial municipal wastewater with or without carbamazepine, clofibric acid, fluoxetine, and naproxen (each at 7.6μg/L), as well as sulfamethoxazole and sulfapyridine (each at 150μg/L). Rates of pharmaceutical dissipation over 28d ranged from 0.073 to 3.0d(-1), corresponding to half-lives of 0.23 to 9.4d. Based on calculated rate constants, observed dissipation rates were consistent with photodegradation driving clofibric acid, naproxen, sulfamethoxazole, and sulfapyridine removal, and with sorption also contributing to carbamazepine and fluoxetine loss. Of the seven gene determinants assayed, only two genes for both beta-lactam resistance (blaCTX and blaTEM) and sulfonamide resistance (sulI and sulII) were found in sufficient quantity for monitoring. Genes disappeared relatively rapidly from the water column, with half-lives ranging from 2.1 to 99d. In contrast, detected gene levels did not change in the sediment, with the exception of sulI, which increased after 28d in pharmaceutical-treated systems. These shallow wetland mesocosms were able to dissipate wastewater contaminants rapidly. However, no significant enhancement in removal of nutrients or pharmaceuticals was observed in mesocosms with extensive aquatic plant communities. This was likely due to three factors: first, use of naïve systems with an unchallenged capacity for nutrient assimilation and

  20. Benefit Indicators for Flood Regulation Services of Wetlands: A Modeling Approach

    Science.gov (United States)

    This report describes a method for developing indicators of the benefits of flood regulation services of wetlands and presents a companion case study. We demonstrate our approach through an application to the Woonasquatucket River watershed in northern Rhode Island. This work is ...

  1. 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...

  2. Benefit Indicators for Flood Regulation Services of Wetlands: A Modeling Approach

    Science.gov (United States)

    This report describes a method for developing indicators of the benefits of flood regulation services of wetlands and presents a companion case study. We demonstrate our approach through an application to the Woonasquatucket River watershed in northern Rhode Island. This work is ...

  3. Fractional snow cover mapping from MODIS data using wavelet-artificial intelligence hybrid models

    Science.gov (United States)

    Moosavi, Vahid; Malekinezhad, Hossein; Shirmohammadi, Bagher

    2014-04-01

    This study was carried out to evaluate the wavelet-artificial intelligence hybrid models to produce fractional snow cover maps. At first, cloud cover was removed from MODIS data and cloud free images were produced. SVM-based binary classified ETM+ imagery were then used as reference maps in order to obtain train and test data for sub-pixel classification models. ANN and ANFIS-based modeling were performed using raw data (without wavelet-based preprocessing). In the next step, several mother wavelets and levels were used in order to decompose the original data to obtain wavelet coefficients. Then, the decomposed data were used for further modeling processes. ANN, ANFIS, wavelet-ANN and wavelet-ANFIS models were compared to evaluate the effect of wavelet transformation on the ability of artificial intelligence models. It was demonstrated that wavelet transformation as a preprocessing approach can significantly enhance the performance of ANN and ANFIS models. This study indicated an overall accuracy of 92.45% for wavelet-ANFIS model, 86.13% for wavelet-ANN, 72.23% for ANFIS model and 66.78% for ANN model. In fact, hybrid wavelet-artificial intelligence models can extract the characteristics of the original signals (i.e. model inputs) accurately through decomposing the non-stationary and complex signals into several stationary and simpler signals. The positive effect of fuzzification as well as wavelet transformation in the wavelet-ANFIS model was also confirmed.

  4. Forecasting TRY/USD Exchange Rate with Various Artificial Neural Network Models

    Directory of Open Access Journals (Sweden)

    Cagatay Bal

    2017-02-01

    Full Text Available Exchange rate forecasting is one of the most common subjects among the forecasting problem field. Researchers and academicians from many different disciplines proposed various approaches for better exchange rate forecasting. In recent years, for solving the stated forecasting problem artificial neural networks have become successful tool to obtain solutions. Many different artificial neural networks have been used, developed and still developing for even better and trustable forecasts. In this study, TRY/USD exchange rate forecasting is modeled with different learning algorithms, activations functions and performance measures. Various Artificial Neural Network (ANN models for better forecasting were investigated, compared and the obtained forecasting results interpreted respectively. The results of the application show that Variable Learning Rate Backpropagation learning algorithm with tan-sigmoid activation function has the best performance for TRY/USD exchange rate forecasting.

  5. Macrobenthos habitat potential mapping using GIS-based artificial neural network models.

    Science.gov (United States)

    Lee, Saro; Park, Inhye; Koo, Bon Joo; Ryu, Joo-Hyung; Choi, Jong-Kuk; Woo, Han Jun

    2013-02-15

    This paper proposes and tests a method of producing macrobenthos habitat potential maps in Hwangdo tidal flat, Korea based on an artificial neural network. Samples of macrobenthos were collected during field work, and eight control factors were compiled as a spatial database from remotely sensed data and GIS analysis. The macrobenthos habitat potential maps were produced using an artificial neural network model. Macrobenthos habitat potential maps were made for Macrophthalmus dilatatus, Cerithideopsilla cingulata, and Armandia lanceolata. The maps were validated by compared with the surveyed habitat locations. A strong correlation between the potential maps and species locations was revealed. The validation result showed average accuracies of 74.9%, 78.32%, and 73.27% for M. dilatatus, C. cingulata, and A. lanceolata, respectively. A GIS-based artificial neural network model combined with remote sensing techniques is an effective tool for mapping the areas of macrobenthos habitat potential in tidal flats.

  6. Dynamic Behavior of Artificial Hodgkin-Huxley Neuron Model Subject to Additive Noise.

    Science.gov (United States)

    Kang, Qi; Huang, BingYao; Zhou, MengChu

    2016-09-01

    Motivated by neuroscience discoveries during the last few years, many studies consider pulse-coupled neural networks with spike-timing as an essential component in information processing by the brain. There also exists some technical challenges while simulating the networks of artificial spiking neurons. The existing studies use a Hodgkin-Huxley (H-H) model to describe spiking dynamics and neuro-computational properties of each neuron. But they fail to address the effect of specific non-Gaussian noise on an artificial H-H neuron system. This paper aims to analyze how an artificial H-H neuron responds to add different types of noise using an electrical current and subunit noise model. The spiking and bursting behavior of this neuron is also investigated through numerical simulations. In addition, through statistic analysis, the intensity of different kinds of noise distributions is discussed to obtain their relationship with the mean firing rate, interspike intervals, and stochastic resonance.

  7. Vadose zone-attenuated artificial recharge for input to a ground water model.

    Science.gov (United States)

    Nichols, William E; Wurstner, Signe K; Eslinger, Paul W

    2007-01-01

    Accurate representation of artificial recharge is requisite to calibration of a ground water model of an unconfined aquifer for a semiarid or arid site with a vadose zone that imparts significant attenuation of liquid transmission and substantial anthropogenic liquid discharges. Under such circumstances, artificial recharge occurs in response to liquid disposal to the vadose zone in areas that are small relative to the ground water model domain. Natural recharge, in contrast, is spatially variable and occurs over the entire upper boundary of a typical unconfined ground water model. An improved technique for partitioning artificial recharge from simulated total recharge for inclusion in a ground water model is presented. The improved technique is applied using data from the semiarid Hanford Site. From 1944 until the late 1980s, when Hanford's mission was the production of nuclear materials, the quantities of liquid discharged from production facilities to the ground vastly exceeded natural recharge. Nearly all hydraulic head data available for use in calibrating a ground water model at this site were collected during this period or later, when the aquifer was under the diminishing influence of the massive water disposals. The vadose zone is typically 80 to 90 m thick at the Central Plateau where most production facilities were located at this semiarid site, and its attenuation of liquid transmission to the aquifer can be significant. The new technique is shown to improve the representation of artificial recharge and thereby contribute to improvement in the calibration of a site-wide ground water model.

  8. 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

  9. 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

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

    Energy Technology Data Exchange (ETDEWEB)

    Kavaklioglu, Kadir; Ozturk, Harun Kemal; Canyurt, Olcay Ersel [Pamukkale University, Mechanical Engineering Department, Denizli (Turkey); Ceylan, Halim [Pamukkale University, Civil Engineering Department, Denizli (Turkey)

    2009-11-15

    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)

  11. Artificial Neural Networks Based Modeling and Control of Continuous Stirred Tank Reactor

    Directory of Open Access Journals (Sweden)

    R. S.M.N. Malar

    2009-01-01

    Full Text Available Continuous Stirred Tank Reactor (CSTR is one of the common reactors in chemical plant. Problem statement: Developing a model incorporating the nonlinear dynamics of the system warrants lot of computation. An efficient control of the product concentration can be achieved only through accurate model. Approach: In this study, attempts were made to alleviate the above mentioned problem using “Artificial Intelligence” (AI techniques. One of the AI techniques namely Artificial Neural Networks (ANN was used to model the CSTR incorporating its non-linear characteristics. Two nonlinear models based control strategies namely internal model control and direct inverse control were designed using the neural networks and applied to the control of isothermal CSTR. Results: The simulation results for the above control schemes with set point tracking were presented. Conclusion: Results indicated that neural networks can learn accurate models and give good non-linear control when model equations are not known.

  12. 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.

  13. Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation

    NARCIS (Netherlands)

    Vos, de N.J.; Rientjes, T.H.M.

    2005-01-01

    The application of Artificial Neural Networks (ANNs) in rainfall-runoff modelling needs to be researched more extensively in order to appreciate and fulfil the potential of this modelling approach. This paper reports on the application of multi-layer feedforward ANNs for rainfall-runoff modelling of

  14. Detection of wetland dynamics with ENVISAT ASAR in support of methane modelling at high latitudes

    Directory of Open Access Journals (Sweden)

    A. Bartsch

    2011-08-01

    Full Text Available Spatial information on inundation dynamics is expected to improve greenhouse gas estimates in climate models. Satellite data can provide land cover information from local to global scale. The detection capability for dynamics is however limited. Cloud cover and daylight independent methods are required for frequent updates. Suitable are therefore sensors which make use of microwaves. The purpose of the present study is to assess such data for determination of wetland dynamics from the viewpoint of use in climate models of the boreal and tundra environments. The focus is on synthetic aperture radar (SAR operating in C-band due to, among microwave systems, comparably good spatial resolution and data availability. Continuity is also expected for such systems. Simple classification algorithms can be applied to detect open water in an automatised way allowing the processing of time series. Such approaches are robust when the water surface is smooth. C-band data from ENVISAT ASAR (Advanced SAR operating in wide swath mode (150 m resolution have been investigated for implementation of an automated detection procedure of open water fraction. More than 4000 samples (single acquisitions tiled into 0.5 degree grid cells have been analysed for July/August 2007 and 2008. Modification of input parameters results in differences below 1 % open water fraction. The actual challenge is the frequent occurrence of waves due to wind and precipitation. This reduces the separability of the water class from other land cover. The possible update intervals for surface water extent are therefore decreased considerably. Statistical measures of the backscatter distribution can be applied in order to retrieve the for classification suitable data. The Pearson correlation between each sample dataset and a location specific representation of the bimodal distribution has been used for assessment. On average only 40 % of acquisitions allow a separation of the open water class

  15. 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.

  16. PERCEPTION OF MEDICAL STUDENTS TOWARDS ARTIFICIAL BONES AND POP MODELS OF VISCERA

    Directory of Open Access Journals (Sweden)

    Sumit Tulshidas Patil

    2015-03-01

    Full Text Available Background: In learning of anatomy, bones and viscera are very important. Now days, artificial bones are replacing the original bones for study purpose due to unavailability. Original viscera are available for students only at dissection hours. So we have tried to find out perception of medical students towards artificial bones and POP models of viscera. Materials and Methods: We had prepared a questionnaire consisting of 20 questions, 10 related to bones and 10 related to the POP models of viscera and asked 150 students of 1st year MBBS to answer it. Results: All the 150 students agreed that bones are necessary for study but only 36 students have bone set (14 original & 22 artificial. Maximum students get the bones only when made available from department. While 107 (71% students said they would prefer original specimen of viscera, over POP models, for studying; but still 126 (84% wanted to keep the POP models of viscera while studying its relations from text book. Conclusions: Good quality artificial bones should be promoted for students, if original bones are not available. It will be better than having nothing. POP models of viscera cannot replace original viscera but due to its handy quality will be helpful for understanding.

  17. Artificial Intelligence for Constructing Accurate, Low-Cost Models and

    Science.gov (United States)

    2005-01-01

    0.5 1 1.5 2 2.5 -12 -7 -2 3 8 13 AOA C l Equation Model Human Predicition Machine Learning Test Data Figure C-14 NACA 4421 Model Comparison...Natrajan, Anand, and Srinivasan, Sudhir, (1997) “Consistency Maintenance in Multiresolution Simulation”, ACM Transactions on Modeling and

  18. On models for landscape connectivity:a case study of the new-born wetland of the Yellow River Delta

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    The models for landscape connectivity are distinguished into models for line connectivity,vertex connectivity, network connectivity and patch connectivity separately. Because the models for line connectivity, for vertex connectivity, and for network connectivity have long been studied and have become ripe, the model for patch connectivity is paid special attention in this paper. The patch connectivity is defined as the average movement efficiency (minimizing movement distance) of animal migrants or plant propagules in patches of a region under consideration. According to this definition, a model for landscape connectivity is mathematically deduced to apply to GIS data. The application of model for patch connectivity in the new-bom wetland of the Yellow River Delta shows patch connectivity has a negative interrelation with human impact intensity and landscape diversity.

  19. 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

  20. 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.

  1. The influence of artificial-thunderstorm cell polarity on discharge initiation by model hydrometeor arrays

    Science.gov (United States)

    Temnikov, A. G.; Chernenskii, L. L.; Orlov, A. V.; Lysov, N. Yu.; Belova, O. S.; Kalugina, I. E.; Gerastenok, T. K.; Zhuravkova, D. S.

    2017-02-01

    The initiation of discharge by model hydrometeors between an artificial-thunderstorm cell (aerosol cloud) of negative or positive polarity and ground has been experimentally studied. It is established for the first time that the conditions of cloud-ground spark discharge initiation by hydrometeors, as well as the characteristics of discharge significantly depend on the polarity of charged cloud. The effect of hydrometeor arrays can be manifested by the cloud-ground lightning initiated in a thundercloud and used for developing scientific principles of artificial lightning discharge.

  2. Modeling of human colonic blood flow for a novel artificial anal sphincter system

    Institute of Scientific and Technical Information of China (English)

    Peng ZAN; Guo-zheng YAN; Hua LIU

    2008-01-01

    A novel artificial anal sphincter system has been developed to simulate the normal physiology of the human anorectum. With the goal of engineering a safe and reliable device, the model of human colonic blood flow has been built and the relationship between the colonic blood flow rate and the operating occlusion pressure of the anorectum is achieved. The tissue ischemia is analyzed based on constitutive relations for human anorectum. The results suggest that at the planned operating occlusion pressure of less than 4 kPa the artificial anal sphincter should not risk the vaseularity of the human colon.

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

    Science.gov (United States)

    Li, Yanxia; 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.

  4. 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.

  5. 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

  6. Modelling of the L-band brightness temperatures measured with ELBARA III radiometer on Bubnow wetland

    Science.gov (United States)

    Gluba, Lukasz; Sagan, Joanna; Lukowski, Mateusz; Szlazak, Radoslaw; Usowicz, Boguslaw

    2017-04-01

    Microwave radiometry has become the main tool for investigating soil moisture (SM) with remote sensing methods. ESA - SMOS (Soil Moisture and Ocean Salinity) satellite operating at L-band provides global distribution of soil moisture. An integral part of SMOS mission are calibration and validation activities involving measurements with ELBARA III which is an L-band microwave passive radiometer. It is done in order to improve soil moisture retrievals - make them more time-effective and accurate. The instrument is located at Bubnow test-site, on the border of cultivated field, fallow, meadow and natural wetland being a part of Polesie National Park (Poland). We obtain both temporal and spatial dependences of brightness temperatures for varied types of land covers with the ELBARA III directed at different azimuths. Soil moisture is retrieved from brightness temperature using L-band Microwave Emission of the Biosphere (L-MEB) model, the same as currently used radiative transfer model for SMOS. Parametrization of L-MEB, as well as input values are still under debate. We discuss the results of SM retrievals basing on data obtained during first year of the radiometer's operation. We analyze temporal dependences of retrieved SM for one-parameter (SM), two-parameter (SM, τ - optical depth) and three-parameter (SM, τ, Hr - roughness parameter) retrievals, as well as spatial dependences for specific dates. Special case of Simplified Roughness Parametrization, combining the roughness parameter and optical depth, is considered. L-MEB processing is supported by the continuous measurements of soil moisture and temperature obtained from nearby agrometeorological station, as well as studies on the soil granulometric composition of the Bubnow test-site area. Furthermore, for better estimation of optical depth, the satellite-derived Normalized Difference Vegetation Index (NDVI) was employed, supported by measured in situ vegetation parameters (such as Leaf Area Index and Vegetation

  7. Multiobjective training of artificial neural networks for rainfall-runoff modeling

    NARCIS (Netherlands)

    De Vos, N.J.; Rientjes, T.H.M.

    2008-01-01

    This paper presents results on the application of various optimization algorithms for the training of artificial neural network rainfall-runoff models. Multilayered feed-forward networks for forecasting discharge from two mesoscale catchments in different climatic regions have been developed for thi

  8. Multiobjective training of artificial neural networks for rainfall-runoff modeling

    NARCIS (Netherlands)

    De Vos, N.J.; Rientjes, T.H.M.

    2008-01-01

    This paper presents results on the application of various optimization algorithms for the training of artificial neural network rainfall-runoff models. Multilayered feed-forward networks for forecasting discharge from two mesoscale catchments in different climatic regions have been developed for

  9. 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 Urba

  10. Artificial Immune Systems Metaphor for Agent Based Modeling of Crisis Response Operations

    OpenAIRE

    Khalil, Khaled M.; Abdel-Aziz, M.; Nazmy, Taymour T.; Salem, Abdel-Badeeh M.

    2010-01-01

    Crisis response requires information intensive efforts utilized for reducing uncertainty, calculating and comparing costs and benefits, and managing resources in a fashion beyond those regularly available to handle routine problems. This paper presents an Artificial Immune Systems (AIS) metaphor for agent based modeling of crisis response operations. The presented model proposes integration of hybrid set of aspects (multi-agent systems, built-in defensive model of AIS, situation management, a...

  11. Prediction Model of Soil Nutrients Loss Based on Artificial Neural Network

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian - River basin. The results by calculating show that the solution based on BP algorithms are consis tent with those based multiple-variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible.

  12. Hybrid Hot Strip Rolling Force Prediction using a Bayesian Trained Artificial Neural Network and Analytical Models

    OpenAIRE

    Abdelkrim Moussaoui; Yacine Selaimia; Hadj A. Abbassi

    2006-01-01

    The authors discuss the combination of an Artificial Neural Network (ANN) with analytical models to improve the performance of the prediction model of finishing rolling force in hot strip rolling mill process. The suggested model was implemented using Bayesian Evidence based training algorithm. It was found that the Bayesian Evidence based approach provided a superior and smoother fit to the real rolling mill data. Completely independent set of real rolling data were used to evaluate the capa...

  13. 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.

  14. Dry deposition of particulate matter at an urban forest, wetland and lake surface in Beijing

    Science.gov (United States)

    Liu, Jiakai; Zhu, Lijuan; Wang, Huihui; Yang, Yilian; Liu, Jiatong; Qiu, Dongdong; Ma, Wu; Zhang, Zhenming; Liu, Jinglan

    2016-01-01

    The dry deposition of particular matters from atmosphere to ecosystems is an undesirable consequence of this pollution while the deposition process is also influenced by different land use types. In current study, concentration of fine particles, coarse particles and meteorological data were collected during the daytime in an artificial forest, wetland and a water surface in the Beijing Olympic Park. Dry deposition velocity, fluxes and vegetation collection were calculated by different models and the results were compared. The results show: (1) the deposition velocity onto the forest canopy was higher than which onto the wetland and the water surface and the velocity varied in different seasons; (2) the fine particles deposited most in the winter while the coarse particles was in the spring; (3) the vegetation collection rates of fine particles were lower than coarse particles, and the forest collected more PMs than the wetland plants.

  15. On fitting the k-C* first order model to batch loaded sub-surface treatment wetlands.

    Science.gov (United States)

    Stein, O R; Towler, B W; Hook, P B; Biederman, J A

    2007-01-01

    The k-C* first order model was fit to time-series COD data collected from batch-loaded model wetlands. Four replicates of four plant species treatments; Carex utriculata (sedge), Schoenoplectus acutus (bulrush), Typha latifolia (cattail) and unplanted controls were compared. Temperature was varied by 4 degrees C from 24 degrees C to 4 degrees C to 24 degrees C over a year-long period. One mathematical fit was made for each wetland replicate at each temperature setting (192 fits). Temperature effects on both parameters were subsequently estimated by fitting the Arrhenius relationship to the estimated coefficients. Inherent interactions between k and C* make values dependent on sample timing and statistical technique for either time series (batch load) or distance profile (plug flow) data. Coefficients calibrated using the Levenberg-Marquardt method are compared to values previously reported using a nonlinear mixed effect regression technique. Overall conclusions are similar across approaches: (a) the magnitude of the coefficients varies strongly by species; (b) the rate constant k decreases with increasing temperature; and (c) temperature and species variation in the residual concentration C* is greater than the variation in k, such that variation in k alone is a poor predictor of performance. However, the magnitudes of the coefficients, especially the rate parameter k, vary between the statistical techniques, highlighting the need to better document the statistical routines used to calibrate the k-C* model.

  16. 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.

  17. 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

    2016-01-01

    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. PMID:27286293

  18. Application of artificial intelligence models in water quality forecasting.

    Science.gov (United States)

    Yeon, I S; Kim, J H; Jun, K W

    2008-06-01

    The real-time data of the continuous water quality monitoring station at the Pyeongchang river was analyzed separately during the rainy period and non-rainy period. Total organic carbon data observed during the rainy period showed a greater mean value, maximum value and standard deviation than the data observed during the non-rainy period. Dissolved oxygen values during the rainy period were lower than those observed during the non-rainy period. It was analyzed that the discharge due to rain fall from the basin affects the change of the water quality. A model for the forecasting of water quality was constructed and applied using the neural network model and the adaptive neuro-fuzzy inference system. Regarding the models of levenberg-marquardt neural network, modular neural network and adaptive neuro-fuzzy inference system, all three models showed good results for the simulation of total organic carbon. The levenberg-marquardt neural network and modular neural network models showed better results than the adaptive neuro-fuzzy inference system model in the forecasting of dissolved oxygen. The modular neural network model, which was applied with the qualitative data of time in addition to quantitative data, showed the least error.

  19. A top-down multi-scale modeling for actuation response of polymeric artificial muscles

    Science.gov (United States)

    Yang, Qianxi; Li, Guoqiang

    2016-07-01

    A class of innovative artificial muscles made of high-strength polymeric fibers such as fishing lines or sewing threads have been discovered recently. These muscles are fabricated by a simple "twist-insertion" procedure, which have attracted increasing attention due to their low cost and readily availability, giant tensile stroke, record energy density, and easy controllability. In the present paper, we established a multi-scale modeling framework for the thermomechanical actuation responses by a top-down strategy, spanning from macro-scale helical spring analysis down to molecular level chain interaction study. Comparison between modeling results and experimental results exhibited excellent agreement. The effect of the micro-, meso- and macro-scale parameters on the actuation responses of the artificial muscle was further discussed through a parametric study per the validated model. This work helps understand the physical origin behind the remarkable tensile actuation behavior of the twisted-then-coiled polymeric artificial muscles and also provides inspirations for optimal design of advanced artificial muscles made by twist-insertion procedure.

  20. [Problems and countermeasures in the application of constructed wetlands].

    Science.gov (United States)

    Huang, Jin-Lou; Chen, Qin; Xu, Lian-Huang

    2013-01-01

    Constructed wetlands as a wastewater eco-treatment technology are developed in recent decades. It combines sewage treatment with the eco-environment in an efficient way. It treats the sewage effectively, and meanwhile beautifies the environment, creates ecological landscape, and brings benefits to the environment and economics. The unique advantages of constructed wetlands have attracted intensive attention since developed. Constructed wetlands are widely used in treatment of domestic sewage, industrial wastewater, and wastewater from mining and petroleum production. However, many problems are found in the practical application of constructed wetland, e. g. they are vulnerable to changes in climatic conditions and temperature, their substrates are easily saturated and plugged, they are readily affected by plant species, they often occupy large areas, and there are other problems including irrational management, non-standard design, and a single function of ecological service. These problems to a certain extent influence the efficiency of constructed wetlands in wastewater treatment, shorten the life of the artificial wetland, and hinder the application of artificial wetland. The review presents correlation analysis and countermeasures for these problems, in order to improve the efficiency of constructed wetland in wastewater treatment, and provide reference for the application and promotion of artificial wetland.

  1. Phytoremediation of industrial effluent containing azo dye by model up-flow constructed wetland

    Institute of Scientific and Technical Information of China (English)

    S.A.Ong; K.Uchiyama; D.Inadama; Y.Ishida; K.Yamagiwa

    2009-01-01

    This study assessed the treatment of azo dye Acid Orange 7(AO7)containing wastewater by laboratory-scale up-flow constructed wetland(UFCW)with and without supplementary aeration.The supplementary aeration could effectively control the ratio of anaerobic and aerobic zones in the UFCW reactor.The results dearly show the supplementary aeration boosted the biodegradation of organic pollutants and mineralization of intermediate aromatic amines formed by AO7 degradation.

  2. Methods of artificial enlargement of the training set for statistical shape models.

    Science.gov (United States)

    Koikkalainen, Juha; Tölli, Tuomas; Lauerma, Kirsi; Antila, Kari; Mattila, Elina; Lilja, Mikko; Lötjönen, Jyrki

    2008-11-01

    Due to the small size of training sets, statistical shape models often over-constrain the deformation in medical image segmentation. Hence, artificial enlargement of the training set has been proposed as a solution for the problem to increase the flexibility of the models. In this paper, different methods were evaluated to artificially enlarge a training set. Furthermore, the objectives were to study the effects of the size of the training set, to estimate the optimal number of deformation modes, to study the effects of different error sources, and to compare different deformation methods. The study was performed for a cardiac shape model consisting of ventricles, atria, and epicardium, and built from magnetic resonance (MR) volume images of 25 subjects. Both shape modeling and image segmentation accuracies were studied. The objectives were reached by utilizing different training sets and datasets, and two deformation methods. The evaluation proved that artificial enlargement of the training set improves both the modeling and segmentation accuracy. All but one enlargement techniques gave statistically significantly (p < 0.05) better segmentation results than the standard method without enlargement. The two best enlargement techniques were the nonrigid movement technique and the technique that combines principal component analysis (PCA) and finite element model (FEM). The optimal number of deformation modes was found to be near 100 modes in our application. The active shape model segmentation gave better segmentation accuracy than the one based on the simulated annealing optimization of the model weights.

  3. MODELLING STUDIES BY APPLICATION OF ARTIFICIAL NEURAL NETWORK USING MATLAB

    Directory of Open Access Journals (Sweden)

    K. S. ARJUN

    2015-11-01

    Full Text Available Four ANN models to estimate Bubble point pressure (Pb, Oil Formation Volume Factor (Bob, Bubble point solution Gas Oil Ratio (Rsob and Stock Tank Vent GOR (RST in the absence of Pressure, Volume and Temperature (PVT analysis, were proposed as a function of readily available field data. The estimated Rsob and RST values from the proposed models can be used as a basic input variable in many PVT correlations in order to estimate other fluid properties such as the Pb and Bob. Another proposed ANN model has the ability to predict and interpolate average reservoir pressure accurately by employing oil, water and gas production rates and number of producers are used as four inputs for the proposed model without the wells having to be closed. Another ANN model proposed is to predict the performance of oil production within water injection reservoirs, which can be utilized to find the most economical scenario of water injection to maximize ultimate oil recovery. It has reasonable accuracy, requires little data and can forecast quickly. ANN approach to solving the identified pipeline damage problem gives satisfactory results as the error between the ANN output and the target is very tolerable. The results conclusively proved with error 0.0027 that it has the ability to accurately predict the pipeline damage probability by employing the model data obtained in this study.

  4. Artificial neural networks modeling gene-environment interaction

    Directory of Open Access Journals (Sweden)

    Günther Frauke

    2012-05-01

    Full Text Available Abstract Background Gene-environment interactions play an important role in the etiological pathway of complex diseases. An appropriate statistical method for handling a wide variety of complex situations involving interactions between variables is still lacking, especially when continuous variables are involved. The aim of this paper is to explore the ability of neural networks to model different structures of gene-environment interactions. A simulation study is set up to compare neural networks with standard logistic regression models. Eight different structures of gene-environment interactions are investigated. These structures are characterized by penetrance functions that are based on sigmoid functions or on combinations of linear and non-linear effects of a continuous environmental factor and a genetic factor with main effect or with a masking effect only. Results In our simulation study, neural networks are more successful in modeling gene-environment interactions than logistic regression models. This outperfomance is especially pronounced when modeling sigmoid penetrance functions, when distinguishing between linear and nonlinear components, and when modeling masking effects of the genetic factor. Conclusion Our study shows that neural networks are a promising approach for analyzing gene-environment interactions. Especially, if no prior knowledge of the correct nature of the relationship between co-variables and response variable is present, neural networks provide a valuable alternative to regression methods that are limited to the analysis of linearly separable data.

  5. China’s wetland change (1990-2000) determined by remote sensing

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    Two wetland maps for the entire China have been produced based on Landsat data acquired around 1990 and 2000. Wetlands in China have been divided into 3 broad categories with 15 sub-categories except rice fields. In 1990, the total wetland area in China was 355208 km2 whereas in 2000 it dropped to 304849 km2 with a net loss of 50360 km2. During an approximate 10-year period, inland wetland reduced from 318326 to 257922 km2, coastal wetland dropped from 14335 to 12015 km2, while artificial wetland increased from 22546 to 34911 km2. The greatest natural wetland loss occurred in Heilongjiang, Inner Mon- golia, and Jilin with a total loss of over 57000 km2 of wetland. In western China, over 13000 km2 of wetlands were newly formed in Xinjiang, Tibet, and Qinghai. About 12000 km2 of artificial wetlands were also added for fish farm and reservoir constructions. The newly formed wetlands in western China were caused primarily by climate warming over that region whereas the newly created artificial wetlands were caused by economic developments. China’s wetland loss is caused mainly by human activities.

  6. A constructed wetland model for synthetic reactive dye wastewater treatment by narrow-leaved cattails (Typha angustifolia Linn.).

    Science.gov (United States)

    Nilratnisakorn, S; Thiravetyan, P; Nakbanpote, W

    2009-01-01

    Textile wastewater is contaminated by reactive dye causing unattractive levels of wastewater color, high pH and high salt content when discharged into public water systems. Decolorization of textile wastewater by plant, phytoremediation, is an alternative, sustainable method which is suitable for long term operation. Narrow-leaved cattails are one species of wetland plant with efficiency for decolorizing and remediating textile wastewater. In addition, chemical oxygen demand (COD) can be lowered and dye residue can be removed. The plant also showed a good salt tolerance even after being exposed to a salt solution for 15 days. The narrow-leaved cattails were set up in a constructed wetland model with a vertical flow system operating from bottom to top for synthetic reactive dye wastewater (SRDW) removal. Narrow-leaved cattails could achieve the removal of SRDW at approximately 0.8 g(SRDW) m(-2) day(-1). Decolorization of SRDW by this plant was approximately 60%. The advantage of this method is that it is suitable for textile wastewater management and improvement of wetland. These plants could lower COD, remove dye, sodium and total dissolved solids (TDS) whereas other biological and chemical methods could not remove TDS and dye in the same time. These results suggested that the spongy cell structure of this plant has the ability to absorb large amounts of water and nutrients. Physico-chemical analysis revealed increasing amounts of sulfur, silicon, iron and calcium in the plant leafs and roots after exposure to wastewater. Proteins or amide groups in the plant might help in textile dye removal. Regarding decolorization, this plant accumulates dye in the intercellular space and still grows in this SRDW condition. Hence, it can be noted here that narrow-leaved cattails are efficient for textile dye wastewater treatment.

  7. 复合型人工湿地及其在小城镇污水处理中的应用%The Compound Artificial Wetland and Its Application in Treating Wastewater in Small Cities and Towns

    Institute of Scientific and Technical Information of China (English)

    何强; 万杰; 翟俊; 梁建军

    2009-01-01

    A technology of wastewater treatment-compound artificial wetland was developed in connection with the characteristics of water pollution in small cities and towns in China. That compound system was composed of vertical baffled wetland filter under anaerobic conditions and lateral subsurface flow wetland bed under facultative conditions. By setting up the inner back-flow system, the nature reoxygenation area and the gravel filler with a particle size of 8~30 mm, the dissolved oxygen(DO) division in the system was achieved with the optimization of the flow pattern and the increase of treatment efficiency. The application results indicated that, under the conditions that the water flow rate was 300~450 m~3/d, COD, NH~4_+ -N and TP concentration of the inffluent were 60~250 mg/L, 4.5~23 mg/L and 1.5~8 mg/L respectively.And these values of the effluent were 20~30 mg/L, 2~4 mg/L and 0.3~0. 5 mg/L respectively at low temperature and could fall below 30 mg/L, 4 mg/L and 0.43 mg/L at normal temperature.%针对中国小城镇的水污染特点,开发了1种复合型人工湿地污水处理技术,该工艺由处于厌氧环境的竖向折流湿地滤池和处于兼氧环境的侧向潜流湿地床组成,通过设置内回流系统、自然复氧区以及粒径为(ρ)8~30 mm的碎石填料,实现了系统内溶解氧的合理分区.并优化了污水流态,显著提高了处理效率.低温和常温条件下的运行结果表明:该工艺在进水流量300~450 m~3/d,COD、NH~4_+-N及TP分别为60~250 mg/L 4.5~23 mg/L、1.5~8 mg/L条件下,低温出水中COD、NH+-N及TP的浓度分别为20~30 mg/L、2~4 mg/L、0.3~0.5 mg/L,常温出水中的浓度分别在30 mg/L、4 mg/L、0.43 mg/L以下.

  8. Proton exchange membrane fuel cells modeling based on artificial neural networks

    Institute of Scientific and Technical Information of China (English)

    Yudong Tian; Xinjian Zhu; Guangyi Cao

    2005-01-01

    To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are analyzed, and artificial neural networks based PEMFC modeling is advanced. The structure, algorithm, training and simulation of PEMFC modeling based on improved BP networks are given out in detail. The computer simulation and conducted experiment verify that this model is fast and accurate, and can be used as a suitable operational model for PEMFC real-time control.

  9. 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.

  10. Modeling, Optimization and simulation of Rotary Furnace using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Dr. R, K. Jain,

    2011-04-01

    Full Text Available This paper deals with modeling and simulation of LDO fired rotary furnace using feed forward modeling method of artificial neural network (ANN.The authors conducted experimental investigations onfuel consumption in a rotary furnace in an industry. It was observed that 6% oxygen enrichment of the air preheated up to 4600C simultaneously with reduction of air volume to 75% of its theoretical requirement lowered the specific fuel consumption to 0.260 lit/kg..The compact heat exchanger with 533 fins was used for preheating the air. Accordingly the emission level was also considerably reduced. The feed forward modeling method of artificial neural network contained in MAT LAB software was used for modeling andoptimization of specific fuel consumption. The percentage variation, between actual experimental data and same data when simulated is +1.730%, and other feasible simulated datas is +6.192%,-3.038%,-5.692%,and+0.115%which is fairly acceptable.

  11. Modeling and Simulation of Road Traffic Noise Using Artificial Neural Network and Regression.

    Science.gov (United States)

    Honarmand, M; Mousavi, S M

    2014-04-01

    Modeling and simulation of noise pollution has been done in a large city, where the population is over 2 millions. Two models of artificial neural network and regression were developed to predict in-city road traffic noise pollution with using the data of noise measurements and vehicle counts at three points of the city for a period of 12 hours. The MATLAB and DATAFIT softwares were used for simulation. The predicted results of noise level were compared with the measured noise levels in three stations. The values of normalized bias, sum of squared errors, mean of squared errors, root mean of squared errors, and squared correlation coefficient calculated for each model show the results of two models are suitable, and the predictions of artificial neural network are closer to the experimental data.

  12. Comparative analysis of regression and artificial neural network models for wind speed prediction

    Science.gov (United States)

    Bilgili, Mehmet; Sahin, Besir

    2010-11-01

    In this study, wind speed was modeled by linear regression (LR), nonlinear regression (NLR) and artificial neural network (ANN) methods. A three-layer feedforward artificial neural network structure was constructed and a backpropagation algorithm was used for the training of ANNs. To get a successful simulation, firstly, the correlation coefficients between all of the meteorological variables (wind speed, ambient temperature, atmospheric pressure, relative humidity and rainfall) were calculated taking two variables in turn for each calculation. All independent variables were added to the simple regression model. Then, the method of stepwise multiple regression was applied for the selection of the “best” regression equation (model). Thus, the best independent variables were selected for the LR and NLR models and also used in the input layer of the ANN. The results obtained by all methods were compared to each other. Finally, the ANN method was found to provide better performance than the LR and NLR methods.

  13. Modeling Consideration Sets and Brand Choice Using Artificial Neural Networks

    NARCIS (Netherlands)

    B.L.K. Vroomen (Björn); Ph.H.B.F. Franses (Philip Hans); J.E.M. van Nierop

    2001-01-01

    textabstractThe concept of consideration sets makes brand choice a two-step process. House-holds first construct a consideration set which not necessarily includes all available brands and conditional on this set they make a final choice. In this paper we put forward a parametric econometric model f

  14. Modelling of Surface Ships using Artificial Neural Networks

    DEFF Research Database (Denmark)

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

    are costly and time consuming - at least in the context of sampling sufficient data for statistical analyses purposes. Furthermore, a modelling of the process of navigating could be used in a vessel autopilot to guide a vessel on a fixed heading (course - keeping) or a new heading (course -changing)....

  15. Mathematical modeling of the growth and development of the mussel Mytilus galloprovincialis on artificial substrates

    Science.gov (United States)

    Vasechkina, E. F.; Kazankova, I. I.

    2014-11-01

    A mathematical model simulating the growth and development of the mussel Mytilus galloprovincialis Lam. on artificial substrates has been constructed. The model is based on experimental data and contains mathematical descriptions of the filtration, respiration, excretion, spawning, and growth of an individual during its ontogenesis from the moment it attaches to a solid substrate to the attainment of a marketable size. The test computations have been compared to the available observation data for mussel farms.

  16. Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters

    OpenAIRE

    Wang, Fei; Mi, Zengqiang; Su, Shi; Zhao, Hongshan

    2012-01-01

    Short-term solar irradiance forecasting (STSIF) is of great significance for the optimal operation and power predication of grid-connected photovoltaic (PV) plants. However, STSIF is very complex to handle due to the random and nonlinear characteristics of solar irradiance under changeable weather conditions. Artificial Neural Network (ANN) is suitable for STSIF modeling and many research works on this topic are presented, but the conciseness and robustness of the existing models still need t...

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. Capacitive MEMS accelerometer wide range modeling using artificial neural network

    Directory of Open Access Journals (Sweden)

    A. Baharodimehr

    2009-08-01

    Full Text Available This paper presents a nonlinear model for a capacitive microelectromechanical accelerometer (MEMA. System parameters ofthe accelerometer are developed using the effect of cubic term of the folded‐flexure spring. To solve this equation, we use theFEA method. The neural network (NN uses the Levenberg‐Marquardt (LM method for training the system to have a moreaccurate response. The designed NN can identify and predict the displacement of the movable mass of accelerometer. Thesimulation results are very promising.

  2. Coastal Wetlands.

    Science.gov (United States)

    Area Cooperative Educational Services, New Haven, CT. Environmental Education Center.

    This material includes student guide sheets, reference materials, and tape script for the audio-tutorial unit on Inland Wetlands. A set of 35mm slides and an audio tape are used with the materials. The material is designed for use with Connecticut schools, but it can be adapted to other localities. The unit materials emphasize the structure,…

  3. Urban wetlands

    NARCIS (Netherlands)

    Van der Salm, N.; Bellmann, C.; Hoeijmakers, S.

    2014-01-01

    This "designers' manual" is made during the TIDO-course AR0533 Innovation & Sustainability. This is a manual meant for designers who are interested in water purifications within the boundaries of a project, presenting constructed wetlands. It is a guide to quickly provide you with project relevant

  4. STELLA software as a tool for modelling phosphorus removal in a constructed wetland employing dewatered alum sludge as main substrate.

    Science.gov (United States)

    Kumar, J L G; Wang, Z Y; Zhao, Y Q; Babatunde, A O; Zhao, X H; Jørgensen, S E

    2011-01-01

    A dynamic simulation model was developed for the removal of soluble reactive phosphorus (SRP) from the vertical flow constructed wetlands (VFCW) using a dynamic software program called STELLA (structural thinking, experiential learning laboratory with animation) 9.1.3 to aid in simulating the environmental nature and succession of relationship between interdependent components and processes in the VFCW system. In particular, the VFCW employed dewatered alum sludge as its main substrate to enhance phosphorus (P) immobilization. Although computer modelling of P in treatment wetland has been well studied especially in recent years, there is still a need to develop simple and realistic models that can be used for investigating the dynamics of SRP in VFCWs. The state variables included in the model are dissolved phosphorus (DISP), plant phosphorus (PLAP), detritus phosphorus (DETP), plant biomass (PLBI) and adsorbed phosphorus (ADSP). The major P transformation processes considered in this study were adsorption, plant and microbial uptake and decomposition. The forcing functions which were considered in the model are temperature, radiation, volume of wastewater, P concentration, contact time, flow rate and the adsorbent (i.e., alum sludge). The model results revealed that up to 72% of the SRP can be removed through adsorption process whereas the uptake by plants is about 20% and the remaining processes such as microbial P utilization and decomposition, accounted for 7% SRP removal based on the mass balance calculations. The results obtained indicate that the model can be used to simulate outflow SRP concentration, and it can also be used to estimate the amount of P removed by individual processes in the VFCW using alum-sludge as a substrate.

  5. 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.

  6. Modeling and optoelectronic realization of an artificial cortex

    Science.gov (United States)

    Pashaie, Ramin

    Cortex, the outermost layer of the cerebrum, is recognized as the most developed part of the brain. It is believed that the higher-level functionality of the brain, the operations such as perception, cognition, and learning of both static and dynamic sensory information, originates from the dynamics of the massively interconnected gray cells of cortex. Because of the compact three-dimensional architecture of this biological computational paradigm, realization of bio-inspired machines that imitate such functionalities, including all the cellular details, is prohibitively difficult even if we consider the available nano-fabrication technologies. Based on this logical deduction, instead of considering each single neuron, an intriguing conjecture is to build aggregate level models that mimic the behavior of a population of neurons with collective emergent properties. In our approach, which is presented in this dissertation, cortex is assumed to be a composition of a sequence of discernable interconnected cortical patches. Each concerned patch is a network of asymmetrically coupled complex processing elements whose dynamics contain not only fixed-point and periodic attractors but also bifurcation and chaos. Dynamics of the complex processing elements, in this dissertation, is mathematically modeled by a slight modification of the time evolution of netlets adapted from computational neuroscience. Regarding this modification, the dynamics of a netlet is approximated by that of a quadratic return map. Studying the previous experimental observations demonstrates that a smart way of coupling such processing units is to couple them through their bifurcation parameters. Putting all pieces of this puzzle together, we model each cortical patch by a network of parametrically coupled quadratic return maps. Our simulations prove the ability of this network to emulate many salient features of cortical information processing, such as clustering, classification, generation of sparse

  7. The prediction of brick wall strengths with artificial neural networks model

    Science.gov (United States)

    Demir, Ali; Kumanlioglu, Ahmet Ali

    2017-01-01

    The aim of this study is to predict with Artificial Neural Networks (ANN) shear strength of brick masonry walls. Shear strength of the walls is determined with diagonal shear tests. It is very difficult to determine strengths of brick masonry walls with experimental procedures. Therefore, an Artificial Neural Networks model is developed with data obtained by investigating many papers from literature and experiments carried out by the authors. Finally, a good degree of coherency is obtained between the experimental and predicted data. The model that is developed makes it possible to easily predict shear strength of the masonry walls. Additionally, this model can be continuously trained with new data and its applicability range can easily be expanded.

  8. Optimization of magnetically driven directional solidification of silicon using artificial neural networks and Gaussian process models

    Science.gov (United States)

    Dropka, Natasha; Holena, Martin

    2017-08-01

    In directional solidification of silicon, the solid-liquid interface shape plays a crucial role for the quality of crystals. The interface shape can be influenced by forced convection using travelling magnetic fields. Up to now, there is no general and explicit methodology to identify the relation and the optimum combination of magnetic and growth parameters e.g., frequency, phase shift, current magnitude and interface deflection in a buoyancy regime. In the present study, 2D CFD modeling was used to generate data for the design and training of artificial neural networks and for Gaussian process modeling. The aim was to quickly assess the complex nonlinear dependences among the parameters and to optimize them for the interface flattening. The first encouraging results are presented and the pros and cons of artificial neural networks and Gaussian process modeling discussed.

  9. Model of Cholera Forecasting Using Artificial Neural Network in Chabahar City, Iran

    Directory of Open Access Journals (Sweden)

    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.

  10. 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.

  11. Generalized in vitro-in vivo relationship (IVIVR model based on artificial neural networks

    Directory of Open Access Journals (Sweden)

    Mendyk A

    2013-03-01

    Full Text Available Aleksander Mendyk,1 Pawel Tuszynski,1 Sebastian Polak,2 Renata Jachowicz1 1Department of Pharmaceutical Technology and Biopharmaceutics, 2Department of Social Pharmacy, Faculty of Pharmacy, Jagiellonian University Medical College, Kraków, Poland Background: The aim of this study was to develop a generalized in vitro-in vivo relationship (IVIVR model based on in vitro dissolution profiles together with quantitative and qualitative composition of dosage formulations as covariates. Such a model would be of substantial aid in the early stages of development of a pharmaceutical formulation, when no in vivo results are yet available and it is impossible to create a classical in vitro-in vivo correlation (IVIVC/IVIVR. Methods: Chemoinformatics software was used to compute the molecular descriptors of drug substances (ie, active pharmaceutical ingredients and excipients. The data were collected from the literature. Artificial neural networks were used as the modeling tool. The training process was carried out using the 10-fold cross-validation technique. Results: The database contained 93 formulations with 307 inputs initially, and was later limited to 28 in a course of sensitivity analysis. The four best models were introduced into the artificial neural network ensemble. Complete in vivo profiles were predicted accurately for 37.6% of the formulations. Conclusion: It has been shown that artificial neural networks can be an effective predictive tool for constructing IVIVR in an integrated generalized model for various formulations. Because IVIVC/IVIVR is classically conducted for 2–4 formulations and with a single active pharmaceutical ingredient, the approach described here is unique in that it incorporates various active pharmaceutical ingredients and dosage forms into a single model. Thus, preliminary IVIVC/IVIVR can be available without in vivo data, which is impossible using current IVIVC/IVIVR procedures. Keywords: artificial neural networks

  12. Mapping Inundation and Changes in Wetland Extent with L-band SAR: A Combined Data and Modeling Approach

    Science.gov (United States)

    Galantowicz, J. F.; Samanta, A.

    2011-12-01

    Accurate mapping of seasonal and inter-annual changes in inundation and wetland extent is a key requisite for the estimation of greenhouse gas (GHG, e.g., methane) emissions from land surfaces to the atmosphere. This task would benefit from the 1- to 3-km spatial resolution L-band synthetic aperture radar (SAR) and 3-day revisit time of NASA's Soil Moisture Active Passive (SMAP) mission, planned for launch in 2014. With a view to utilizing this unique capability, we propose a method for mapping the fraction of area inundated using a combination of semi-empirical models of radar backscatter and L-band SAR data. Inundation exhibits a characteristic radar backscatter that is affected by a set of factors, including roughness of soil and water surfaces, and presence of vegetation cover. Further, the impact of vegetation cover on radar backscatter from underlying soil and/or water surface will depend on biome type. The effects of these factors on both the like-polarized (HH, VV) and cross-polarized (HV) radar backscatter was investigated using semi-empirical models. A key step in devising an inundation fraction retrieval algorithm is to benchmark and calibrate the backscatter simulated with semi-empirical models against SAR data. This task was undertaken using data from the Phased Array L-Band Synthetic Aperture Radar (PALSAR) instrument onboard Japan's Earth Resources Satellite's (JERS, e.g., Fig. 1). This calibration was performed in the following way. First, using a Monte-Carlo type of approach, a large set of random backscatter samples was extracted from different landcover classes, including dry forests and clear-cut areas, inundated forests (wetlands), and open water. Second, mean backscatter was calculated at varying spatial resolutions: 100 m, 500 m, 1 km, 2 km, 3 km and 10 km. Third, the mean model backscatter was set to the mean PALSAR backscatter for each landcover class, but the model dispersion was retained. Finally, using these calibrated values

  13. 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...

  14. Driving forces analysis of reservoir wetland evolution in Beijing during 1984-2010

    Institute of Scientific and Technical Information of China (English)

    GONG Zhaoning; LI Hong; ZHAO Wenji; GONG Huili

    2013-01-01

    The reservoir wetland,which is the largest artificial wetland in Beijing,constitutes one of the important urban ecological infrastructures.Considering two elements of natural environment and socio-economy,this paper established the driving factor indexing system of Beijing reservoir wetland evolution.Natural environment driving factors include precipitation,temperature,entry water and groundwater depth; social economic driving factors include resident population,urbanization rate and per capita GDP.Using multi-temporal Landsat TM images from 1984 to 2010 in Beijing,the spatial extent and the distribution of Beijing reservoir wetlands were extracted,and the change of the wetland area about the three decade years were analyzed.Logistic regression model was used to explore for each of the three periods:from 1984 to 1998,from 1998 to 2004 and from 2004 to 2010.The results showed that the leading driving factors and their influences on reservoir wetland evolution were different for each period.During 1984-1998,two natural environment indices:average annual precipitation and entry water index were the major factors driving the increase in wetland area with the contribution rate of Logistic regression being 5.78 and 3.50,respectively,and caused the wetland growth from total area of 104.93 km2 to 219.96 km2.From 1998 to 2004,as the impact of human activities intensified the main driving factors were the number of residents,groundwater depth and urbanization rate with the contribution rate of Logistic regression 9.41,9.18,and 7.77,respectively,and caused the wetland shrinkage rapidly from the total area of 219.96 km2 to 95.71 km2.During 2004-2010,reservoir wetland evolution was impacted by both natural and socio-economic factors,and the dominant driving factors were urbanization rate and precipitation with the contribution rate of 6.62 and 4.22,respectively,and caused the wetland total area growth slightly to 109.73 km2.

  15. 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...

  16. Modeling of Waves, Hydrodynamics and Sediment Transport for Protection of Wetlands at Braddock Bay, New York

    Science.gov (United States)

    2015-03-01

    maximum wave heights near the backbay shoreline from the Jan 1971 storm for S-0, S-1, S-2, and S-3. Table 4-6 presents the calculated maxi - mum wave...decreases inside the bay. At the high water level, it appears wave overtopping pro- duces more flow in the lee of two structures, but flows are weak at...waves and currents significantly in the central backbay peninsula region preceding the wetlands. The maxi - mum wave height calculated for the Hurricane

  17. 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.

  18. 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

  19. 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

    bed gasifier can be successfully predicted by applying neural networks. ANNs models use in the input layer the biomass composition and few operating parameters, two neurons in the hidden layer and the backpropagation algorithm. The results obtained by these ANNs show high agreement with published......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...

  20. A BOD-DO coupling model for water quality simulation by artificial neural network

    Institute of Scientific and Technical Information of China (English)

    郭劲松; LONG; Tengrui; 等

    2002-01-01

    A one-dimensional BOD-DO coupling model for water quality simulation is presented,which adopts Streeter-Phelps equations and the theory of back-propagation artificial neural network.The water quality data of Yangtze River in the Chongqing region in the year of 1989 are divided into 5 groups and used in the learning and testing courses of this model.The result shows that such model is feasible for water quality simulation and is more accurate than traditional models.

  1. Prediction of rheology of shear thickening fluids using phenomenological and artificial neural network models

    Science.gov (United States)

    Arora, Sanchi; Laha, Animesh; Majumdar, Abhijit; Butola, Bhupendra Singh

    2017-08-01

    Prediction models for the viscosity curve of a shear thickening fluid (STF) over a wide range of shear rate at different temperatures were developed using phenomenological and artificial neural network (ANN) models. STF containing 65% (w/w) silica nanoparticles was prepared using polyethylene glycol (PEG) as dispersion medium, and tested for rheological behavior at different temperatures. The experimental data set was divided into training data and testing data for the model development and validation, respectively. For both the models, the viscosity of STF was estimated for all the zones with good fit between experimental and predicted viscosity, for both training and testing data sets.

  2. Artificial Immune Systems Metaphor for Agent Based Modeling of Crisis Response Operations

    CERN Document Server

    Khalil, Khaled M; Nazmy, Taymour T; Salem, Abdel-Badeeh M

    2010-01-01

    Crisis response requires information intensive efforts utilized for reducing uncertainty, calculating and comparing costs and benefits, and managing resources in a fashion beyond those regularly available to handle routine problems. This paper presents an Artificial Immune Systems (AIS) metaphor for agent based modeling of crisis response operations. The presented model proposes integration of hybrid set of aspects (multi-agent systems, built-in defensive model of AIS, situation management, and intensity-based learning) for crisis response operations. In addition, the proposed response model is applied on the spread of pandemic influenza in Egypt as a case study.

  3. 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

  4. 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...

  5. 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...

  6. 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.

  7. 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.

  8. 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.

  9. Double Glow Plasma Surface Alloying Process Modeling Using Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    Jiang XU; Xishan XIE; Zhong XU

    2003-01-01

    A model is developed for predicting the correlation between processing parameters and the technical target of double glowby applying artificial neural network (ANN). The input parameters of the neural network (NN) are source voltage, workpiecevoltage, working pressure and distance between source electrode and workpiece. The output of the NN model is three importanttechnical targets, namely the gross element content, the thickness of surface alloying layer and the absorption rate (the ratioof the mass loss of source materials to the increasing mass of workpiece) in the processing of double glow plasma surfacealloying. The processing parameters and technical target are then used as a training set for an artificial neural network. Themodel is based on multiplayer feedforward neural network. A very good performance of the neural network is achieved and thecalculated results are in good agreement with the experimental ones.

  10. Decade Review (1999-2009): Artificial Intelligence Techniques in Student Modeling

    Science.gov (United States)

    Drigas, Athanasios S.; Argyri, Katerina; Vrettaros, John

    Artificial Intelligence applications in educational field are getting more and more popular during the last decade (1999-2009) and that is why much relevant research has been conducted. In this paper, we present the most interesting attempts to apply artificial intelligence methods such as fuzzy logic, neural networks, genetic programming and hybrid approaches such as neuro - fuzzy systems and genetic programming neural networks (GPNN) in student modeling. This latest research trend is a part of every Intelligent Tutoring System and aims at generating and updating a student model in order to modify learning content to fit individual needs or to provide reliable assessment and feedback to student's answers. In this paper, we make a brief presentation of methods used to point out their qualities and then we attempt a navigation to the most representative studies sought in the decade of our interest after classifying them according to the principal aim they attempted to serve.

  11. Pilot biomechanical design of biomaterials for artificial nucleus prosthesis using 3D finite-element modeling

    Institute of Scientific and Technical Information of China (English)

    Qijin Huang; Guoquan Liu; Yong Li; Jin Gao; Zhengqiu Gu; Yuanzheng Ma; Haibin Xue

    2004-01-01

    Pilot biomechanical design of biomaterials for artificial nucleus prosthesis was carried out based on the 3D finite-element method. Two 3D models of lumbar intervertebral disc respectively with a real human nucleus and with the nucleus removed were developed and validated using published experimental and clinical data. Then the models with a stainless steel nucleus prosthesis implanted and with polymer nucleus prostheses of various properties implanted were used for the 3D finite-element biomechanical analysis. All the above simulation and analysis were carried out for the L4/L5 disc under a human worst-daily compression load of 2000 N. The results show that the polymer materials with Young's modulus of elasticity E = 0.1-100 MPa and Poisson's ratio v=0.35-0.5 are suitable to produce artificial nucleus prosthesis in view of biomechanical consideration.

  12. Modeling of submerged membrane bioreactor treating cheese whey wastewater by artificial neural network.

    Science.gov (United States)

    Cinar, Ozer; Hasar, Halil; Kinaci, Cumali

    2006-05-17

    A submerged membrane bioreactor receiving cheese whey was modeled by artificial neural network and its performance over a period of 100 days at different solids retention times was evaluated with this robust tool. A cascade-forward network was used to model the membrane bioreactor and normalization was used as a preprocessing method. The network was fed with two subsets of operational data, with two-thirds being used for training and one-third for testing the performance of the artificial neural network. The training procedure for effluent chemical oxygen demand (COD), ammonia, nitrate and total phosphate concentrations was very successful and a perfect match was obtained between the measured and the calculated concentrations. The results of the confirmation (or testing) procedure for effluent ammonia and nitrate concentrations were very successful; however, the results of the confirmation procedure for effluent COD and total phosphate concentrations were only satisfactory.

  13. Modeling and Optimization Technique of a Chilled Water AHU Using Artificial Neural Network Methods

    Science.gov (United States)

    Talib, Rand Issa

    Heating, ventilation, and air conditioning (HVAC) systems are widely used in buildings to provide occupants with conditioned air and acceptable indoor air quality. The chilled water system is one Heating, ventilation, and air conditioning systems are widely used in buildings to provide occupants with conditioned air and acceptable indoor air quality. The design of these systems constitutes a large impact on the energy usage and operating cost of buildings they serve. The ability to accurately predict the performance of these systems is integral to designing more energy efficient and sustainable building systems. In this thesis the modeling of a chilled water air handling units using Artificial Neural Networks model is proposed. The Artificial neural network model was built using four inputs (1) Chilled water temperature (CHWT), (2) Chilled water valve position (CWVLV), (3) Mixed air temperature (MAT), and (4) Supply air flow (SAF). The output of the model is to predict supply air temperature. Moreover, another model was constructed to predict the fan power as a function of the fan air flow and fan speed. The data that were collected from a real building in a span of three months were processed. The ANN model was trained using the measured data and different model structure were then tested with various time delay, feedback time, and number of neurons to determine the best structure. In addition, an optimization method is developed to automate the process of finding the best model structure that can produce the best accurate prediction against the actual data. The Coefficient of variances which was used to determine the error value was recorded to be as low as 1.22 for the best model structure. The obtained results validate the Artificial neural network model created as an accurate tool for predicting the performance of a chilled water air handling unit.

  14. 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.

  15. Analysis of multicriteria models application for selection of an optimal artificial lift method in oil production

    OpenAIRE

    Crnogorac, Miroslav P.; Danilović, Dušan Š.; Karović-Maričić, Vesna D.; Leković, Branko A.

    2016-01-01

    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 multi...

  16. Prediction Model of Antibacterial Activities for Inorganic Antibacterial Agents Based on Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    刘雪峰; 张利; 涂铭旌

    2004-01-01

    Quantitatively evaluation of antibacterial activities of inorganic antibacterial agents is an urgent problem to be solved. Using experimental data by an orthogonal design, a prediction model of the relation between conditions of preparing inorganic antibacterial agents and their antibacterial activities has been developed. This is accomplished by introducing BP artificial neural networks in the study of inorganic antibacterial agents..It provides a theoretical support for the development and research on inorganic antibacterial agents.

  17. 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.

  18. 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...

  19. Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

    Directory of Open Access Journals (Sweden)

    Sumit Goyal

    2012-04-01

    Full Text Available This paper presents the capability of Time–delay artificial neural network models for predicting shelf life of processed cheese. Datasets were divided into two subsets (30 for training and 6 for validation. Models with single and multi layers were developed and compared with each other. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash -
    Sutcliffo Coefficient were used as performance evaluators, Time- delay model predicted the shelf life of processed cheese as 28.25 days, which is very close to experimental shelf life of 30 days.

  20. Handling a Small Dataset Problem in Prediction Model by employ Artificial Data Generation Approach: A Review

    Science.gov (United States)

    Lateh, Masitah Abdul; Kamilah Muda, Azah; Yusof, Zeratul Izzah Mohd; Azilah Muda, Noor; Sanusi Azmi, Mohd

    2017-09-01

    The emerging era of big data for past few years has led to large and complex data which needed faster and better decision making. However, the small dataset problems still arise in a certain area which causes analysis and decision are hard to make. In order to build a prediction model, a large sample is required as a training sample of the model. Small dataset is insufficient to produce an accurate prediction model. This paper will review an artificial data generation approach as one of the solution to solve the small dataset problem.

  1. Disease spread models in wild and feral animal populations: application of artificial life models.

    Science.gov (United States)

    Ward, M P; Laffan, S W; Highfield, L D

    2011-08-01

    The role that wild and feral animal populations might play in the incursion and spread of important transboundary animal diseases, such as foot and mouth disease (FMD), has received less attention than is warranted by the potential impacts. An artificial life model (Sirca) has been used to investigate this issue in studies based on spatially referenced data sets from southern Texas. An incursion of FMD in which either feral pig or deer populations were infected could result in between 698 and 1557 infected cattle and affect an area of between 166 km2 and 455 km2 after a 100-day period. Although outbreak size in deer populations can be predicted bythe size of the local deer population initially infected, the resulting outbreaks in feral pig populations are less predictable. Also, in the case of deer, the size of potential outbreaks might depend on the season when the incursion occurs. The impact of various mitigation strategies on disease spread has also been investigated. The approach used in the studies reviewed here explicitly incorporates the spatial distribution and relationships between animal populations, providing a new framework to explore potential impacts, costs, and control strategies.

  2. 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.

  3. Examining water quality effects of riparian wetland loss and restoration scenarios in a southern ontario watershed.

    Science.gov (United States)

    Yang, Wanhong; Liu, Yongbo; Ou, Chunping; Gabor, Shane

    2016-06-01

    Wetland conservation has two important tasks: The first is to halt wetland loss and the second is to conduct wetland restoration. In order to facilitate these tasks, it is important to understand the environmental degradation from wetland loss and the environmental benefits from wetland restoration. The purpose of the study is to develop SWAT based wetland modelling to examine water quality effects of riparian wetland loss and restoration scenarios in the 323-km(2) Black River watershed in southern Ontario, Canada. The SWAT based wetland modelling was set up, calibrated and validated to fit into watershed conditions. The modelling was then applied to evaluate various scenarios of wetland loss from existing 7590 ha of riparian wetlands (baseline scenario) to 100% loss, and wetland restoration up to the year 1800 condition with 11,237 ha of riparian wetlands (100% restoration). The modelling was further applied to examine 100% riparian wetland loss and restoration in three subareas of the watershed to understand spatial pattern of water quality effects. Modelling results show that in comparing to baseline condition, the sediment, total nitrogen (TN), and total phosphorus (TP) loadings increase by 251.0%, 260.5%, and 890.9% respectively for 100% riparian wetland loss, and decrease by 34.5%, 28.3%, and 37.0% respectively for 100% riparian wetland restoration. Modelling results also show that as riparian wetland loss increases, the corresponding environmental degradation worsens at accelerated rates. In contrast, as riparian wetland restoration increases, the environmental benefits improve but at decelerated rates. Particularly, the water quality effects of riparian wetland loss or restoration show considerable spatial variations. The watershed wetland modelling contributes to inform decisions on riparian wetland conservation or restoration at different rates. The results further demonstrate the importance of targeting priority areas for stopping riparian wetland loss

  4. 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

  5. 有机负荷对潮汐流人工湿地净化农村生活污水的影响%Impact of Organic Pollutant Loading on Effect of Artificial Tidal Flow Wetland Purifying Rural Domestic Sewage

    Institute of Scientific and Technical Information of China (English)

    杜新; 施春红; 马方曙

    2015-01-01

    With increasing discharge of domestic sewage in the rural areas, conventional constructed wetlands gradually fail in reoxygenation capacity, oxygen environments of their beds directly affect pollutants removal efficiency. By simulating the characteristic of intermittent discharge of domestic sewage in the rural areas, a new type of artificial tidal flow wetland ( ATFW) was constructed in lab for an experiment to explore impact of COD loading ( 167�9, 221�9, 610. 3 and 760�0 g·m-2 ·d-1 ) on oxygen environment of the bed and pollution removal efficiency. Results show that concentration of or⁃ganic pollutants was the main factor limiting COD removal efficiency of the tidal flow;COD removal efficiency of the sys⁃tem may reach as high as 95�6%;the ammonia nitrogen ( NH4+⁃N) removal efficiency increased with rising organic pollu⁃tant loading rate from 85.2% to 98�7%;and higher organic pollutant loading favored assimilation of heterotrophic bacteria and enhanced denitrification. The removal of total nitrogen ( TN) followed a trend similar to that of NH4+⁃N with the high⁃est TN removal rate reaching up to 80.3%. Although adsorption was regarded as the primary pathway of phosphorus remov⁃al, which was determined by influent phosphorus concentration, the proportion of total phosphorus (TP) removal through adsorption was low due to the poor adsorption capacity of volcanics. Higher OLR stimulated phosphorus accumulating or⁃ganisms (PAOs) to absorb P, pushing TP removal rate up to 71�0%.%农村生活污水排量逐年增加,传统人工湿地复氧能力较差,床体氧环境直接影响污染物去除效果。通过模拟农村生活污水间歇排放特征,构建新型潮汐流人工湿地小试试验,对比研究了不同COD负荷下(167�9、221�9、610�3和760�0 g·m-2·d-1)床体氧环境和污染物去除效果。结果表明:有机物浓度是潮汐流COD去除效果的主要限制因素,COD去除率最高为95

  6. Optimization of biopharmaceutical downstream processes supported by mechanistic models and artificial neural networks.

    Science.gov (United States)

    Pirrung, Silvia M; van der Wielen, Luuk A M; van Beckhoven, Ruud F W C; van de Sandt, Emile J A X; Eppink, Michel H M; Ottens, Marcel

    2017-01-05

    Downstream process development is a major area of importance within the field of bioengineering. During the design of such a downstream process, important decisions have to be made regarding the type of unit operations as well as their sequence and their operating conditions. Current computational approaches addressing these issues either show a high level of simplification or struggle with computational speed. Therefore, this article presents a new approach that combines detailed mechanistic models and speed-enhancing artificial neural networks. This approach was able to simultaneously optimize a process with three different chromatographic columns toward yield with a minimum purity of 99.9%. The addition of artificial neural networks greatly accelerated this optimization. Due to high computational speed, the approach is easily extendable to include more unit operations. Therefore, it can be of great help in the acceleration of downstream process development. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 2017.

  7. Impact of artificial aeration on nitrogen removal from aquaculture wastewater treated by vertical-flow constructed wetland%曝气对垂直流湿地处理水产养殖废水脱氮的影响

    Institute of Scientific and Technical Information of China (English)

    张世羊; 常军军; 高毛林; 李谷

    2015-01-01

    人工湿地作为一种有效的污水处理技术,现已被逐渐拓展到水产养殖业中。鉴于其与养殖竞争有限土地资源的弊端,如何构建节地高效型湿地成为未来研究的重点。曝气增氧是强化潜流湿地净化效能的重要措施之一,但是关于曝气强度以及净化效率与影响因素的关系仍缺乏深入系统的研究。为此,该文设计构建了7组不同要素组合的垂直流湿地小试系统,同步或分阶段探讨了曝气强化对垂直流湿地脱氮的影响。研究结果表明,无论曝气与否,构建的7组湿地系统于试验运行工况下都存在明显的硝化过程,且空气复氧和植物根系泌氧足以弥补硝化作用耗氧量。曝气增氧进一步强化了湿地内部的矿化和硝化过程;鉴于养殖废水不缺乏碳源(该研究各组湿地进水碳氮比在28.4~30.6之间),湿地内部的反硝化几率增大,导致曝气后总氮的去除效率提高。但是曝气条件下过高的溶解氧又会进一步抑制反硝化过程,从而也会导致系统总氮去除速率的下降。因此,对垂直流湿地而言,曝气强度不是愈高愈好。为了获得更高的脱氮效率,建议可以通过延长水力停留时间或者在垂直流湿地尾部增设水平潜流湿地来补充反硝化过程,进而提高系统对总氮的去除效果。%Due to the serious trend of water pollution across the country, the problem of aquaculture wastewater discharge must be solved appropriately to achieve sustainability. As a novel technology for sewage treatment, constructed wetland (CW) has been gradually expanded to aquaculture. In view of the disadvantages in land dispute with pond aquaculture, how to develop or design a land-saving, high-efficiency CW will be the focus of future study. It is widely accepted that artificial aeration can enhance the purification efficiency of CW’s subsurface flow on wastewater due to its capacity to improve

  8. Changes of Urban Wetland Landscape Pattern and Impacts of Urbanization on Wetland in Wuhan City

    Institute of Scientific and Technical Information of China (English)

    WANG Xuelei; NING Longmei; YU Jing; XIAO Rui; LI Tao

    2008-01-01

    In this study, remote sensing data of Wuhan City, Hubei Province, China in 1996-2001 were selected to ex-tract wetland landscape information. Several landscape indices were used to evaluate the changes of landscape patternwithin the five years, including patch number, patch density, patch fractal dimension, landscape diversity, dominance,evenness, and fragmentation indexes. Then, transformation probabilities of wetland landscapes into non-wetland land-scapes were calculated based on Markov Model, and on these grounds the relationship between changes of wetlandlandscape pattern and urban construction was analyzed. The results showed that fragmentation degree of all wetlandtypes increased, lake area declined, and dominance of natural wetland decreased. The reasons for these results weremainly because of urban construction. According to the features of abundant wetland in Wuhan City, we suggested thatprotection of wetland landscape should cooperate with urban construction, which means wetland should become im-portant part of urban landscape.

  9. 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.

  10. 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

    is by active truncated models. In these models only the very top part of the system is represented by a physical model whereas the behavior of the part below the truncation is calculated by numerical models and accounted for in the physical model by active actuators applying relevant forces to the physical...... model. Hence, in principal it is possible to achieve reliable experimental data for much larger water depths than what the actual depth of the test basin would suggest. However, since the computations must be faster than real time, as the numerical simulations and the physical experiment run...... 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...

  11. 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.

  12. MODEL IMPROVEMENT AND EXPERI-MENT VALIDATION OF PNEUMATIC ARTIFICIAL MUSCLES

    Institute of Scientific and Technical Information of China (English)

    Zhou Aiguo; Shi Guanglin; Zhong Tingxiu

    2004-01-01

    According to the deficiency of the present model of pneumatic artificial muscles (PAM), a serial model is built up based on the PAM's essential working principle with the elastic theory, it is validated by the quasi-static and dynamic experiment results, which are gained from two experiment systems.The experiment results and the simulation results illustrate that the serial model has made a great success compared with Chou's model, which can describe the force characteristics of PAM more precisely.A compensation item considering the braid's elasticity and the coulomb damp is attached to the serial model based on the analysis of the experiment results.The dynamic experiment proves that the viscous damp of the PAM could be ignored in order to simplify the model of PAM.Finally, an improved serial model of PAM is obtained.

  13. 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

  14. 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...

  15. 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

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

    NARCIS (Netherlands)

    Saaltink, R.M.; 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

  17. The Cartridge Theory: a description of the functioning of horizontal subsurface flow constructed wetlands for wastewater treatment, based on modelling results.

    Science.gov (United States)

    Samsó, Roger; García, Joan

    2014-03-01

    Despite the fact that horizontal subsurface flow constructed wetlands have been in operation for several decades now, there is still no clear understanding of some of their most basic internal functioning patterns. To fill this knowledge gap, on this paper we present what we call "The Cartridge Theory". This theory was derived from simulation results obtained with the BIO_PORE model and explains the functioning of urban wastewater treatment wetlands based on the interaction between bacterial communities and the accumulated solids leading to clogging. In this paper we start by discussing some changes applied to the biokinetic model implemented in BIO_PORE (CWM1) so that the growth of bacterial communities is consistent with a well-known population dynamics models. This discussion, combined with simulation results for a pilot wetland system, led to the introduction of "The Cartridge Theory", which states that the granular media of horizontal subsurface flow wetlands can be assimilated to a generic cartridge which is progressively consumed (clogged) with inert solids from inlet to outlet. Simulations also revealed that bacterial communities are poorly distributed within the system and that their location is not static but changes over time, moving towards the outlet as a consequence of the progressive clogging of the granular media. According to these findings, the life-span of constructed wetlands corresponds to the time when bacterial communities are pushed as much towards the outlet that their biomass is not anymore sufficient to remove the desirable proportion of the influent pollutants. Copyright © 2013 Elsevier B.V. All rights reserved.

  18. Artificial neural network model of constitutive relations for shock-prestrained copper

    Institute of Scientific and Technical Information of China (English)

    杨扬; 朱远志; 李正华; 张新明; 杨立斌; 陈志永

    2001-01-01

    Data from the deformation on Split-Hopkinson Bar were used for constructing an artificial neural network model. When putting the thermodynamic parameters of the metals into the trained network model, the corresponding yielding stress can be predicted. The results show that the systematic error is small when the objective function is 0.5, the number of the nodes in the hidden layer is 6 and the learning rate is about 0.1, and the accuracy of the rate-error is less than 3%.

  19. 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.

  20. 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

  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. 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

  3. Estimating temporal trend in the presence of spatial complexity: A Bayesian hierarchical model for a wetland plant population undergoing restoration

    Science.gov (United States)

    Rodhouse, T.J.; Irvine, K.M.; Vierling, K.T.; Vierling, L.A.

    2011-01-01

    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.

  4. Development and validation of a global dynamical wetlands extent scheme

    Directory of Open Access Journals (Sweden)

    T. Stacke

    2012-01-01

    Full Text Available In this study we present the development of the dynamical wetland extent scheme (DWES and its validation against present day wetland observations. The DWES is a simple, global scale hydrological scheme that solves the water balance of wetlands and estimates their extent dynamically. The extent depends on the balance of water flows in the wetlands and the slope distribution within the grid cells. In contrast to most models, the DWES is not directly calibrated against wetland extent observations. Instead, wetland affected river discharge data are used to optimize global parameters of the model. The DWES is not a complete hydrological model by itself but implemented into the Max Planck Institute – Hydrology Model (MPI-HM. However, it can be transferred into other models as well.

    For present climate, the model validation reveals a good agreement between the occurrence of simulated and observed wetlands on the global scale. The best result is achieved for the northern hemisphere where not only the wetland distribution pattern but also their extent is simulated reasonably well by the DWES. However, the wetland fraction in the tropical parts of South America and Central Africa is strongly overestimated. The simulated extent dynamics correlate well with monthly inundation variations obtained from satellite for most locations. Also, the simulated river discharge is affected by wetlands resulting in a delay and mitigation of peak flows. Compared to simulations without wetlands, we find locally increased evaporation and decreased river flow into the oceans due to the implemented wetland processes.

    In summary, the validation analysis demonstrates the DWES' ability to simulate the global distribution of wetlands and their seasonal variations. Thus, the dynamical wetland extent scheme can provide hydrological boundary conditions for wetland related studies. In future applications, the DWES should be implemented into an earth system model

  5. Artificial Neural Network Modeling of Microstructure During C-Mn and HSLA Plate Rolling

    Institute of Scientific and Technical Information of China (English)

    TAN Wen; LIU Zhen-yu; WU Di; WANG Guo-dong

    2009-01-01

    An artificial neural network (ANN) model for predicting transformed mierostrueture in conventional roll-ing process and thermomechanieal controlled process (TMCP) is proposed. The model uses austenite grain size and retained strain, which can be calculated by using microstrueture evolution models, together with a measured cooling rate and chemical compositions as inputs and the ferrite grain size and ferrite fraction as outputs. The predicted re-sults show that the model can predict the transformed microstructure which is in good agreement with the measured one, and it is better than the empirical equations. Also, the effect of the alloying elements on transformed products has been analyzed by using the model. The tendency is the same as that in the reported articles. The model can be used further for the optimization of processing parameters, mierostructure and properties in TMCP.

  6. Improving peak flow estimates in artificial neural network river flow models

    Science.gov (United States)

    Sudheer, K. P.; Nayak, P. C.; Ramasastri, K. S.

    2003-02-01

    In this paper, the concern of accuracy in peak estimation by the artificial neural network (ANN) river flow models is discussed and a suitable statistical procedure to get better estimates from these models is presented. The possible cause for underestimation of peak flow values has been attributed to the local variations in the function being mapped due to varying skewness in the data series, and theoretical considerations of the network functioning confirm this. It is envisaged that an appropriate data transformation will reduce the local variations in the function being mapped, and thus any ANN model built on the transformed series should perform better. This heuristic is illustrated and confirmed by many case studies and the results suggest that the model performance is significantly improved by data transformation. The model built on transformed data outperforms the model built on raw data in terms of various statistical performance indices. The peak estimates are improved significantly by data transformation.

  7. Hybrid Hot Strip Rolling Force Prediction using a Bayesian Trained Artificial Neural Network and Analytical Models

    Directory of Open Access Journals (Sweden)

    Abdelkrim Moussaoui

    2006-01-01

    Full Text Available The authors discuss the combination of an Artificial Neural Network (ANN with analytical models to improve the performance of the prediction model of finishing rolling force in hot strip rolling mill process. The suggested model was implemented using Bayesian Evidence based training algorithm. It was found that the Bayesian Evidence based approach provided a superior and smoother fit to the real rolling mill data. Completely independent set of real rolling data were used to evaluate the capacity of the fitted ANN model to predict the unseen regions of data. As a result, test rolls obtained by the suggested hybrid model have shown high prediction quality comparatively to the usual empirical prediction models.

  8. 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.

  9. Wetland eco-engineering: 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.

    2016-09-01

    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 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 6-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 and yellowing of foliage. The N : P ratios of the plant tissue were low, and these were affected not by hampered uptake of N but by enhanced uptake of P. Subsequent 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 the use of Fe-tolerant species 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 situated sediment and the feedback mechanisms between plant and soil prior to finalizing the design of the eco-engineering project.

  10. 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.

  11. 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.

  12. 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.

  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. Predicting coastal flooding and wetland loss

    Science.gov (United States)

    Doyle, Thomas W.

    1997-01-01

    The southeastern coastal region encompasses vast areas of wetland habitat important to wildlife and other economically valuable natural resources. Located on the interface between sea and land, these wetland habitats are affected by both sea-level rise and hurricanes, and possibly by hydroperiod associated with regional climatic shifts. Increased sea level is expected to accompany global warming because of higher sea temperatures and ice melt. To help determine the effects of sea-level rise on these wetlands, USGS scientists created computer models of coastal flooding and wetland loss.

  15. Radioiodine concentrated in a wetland.

    Science.gov (United States)

    Kaplan, Daniel I; Zhang, Saijin; Roberts, Kimberly A; Schwehr, Kathy; Xu, Chen; Creeley, Danielle; Ho, Yi-Fang; Li, Hsiu-Ping; Yeager, Chris M; Santschi, Peter H

    2014-05-01

    Most subsurface environmental radioactivity contamination is expected to eventually resurface in riparian zones, or wetlands. There are a number of extremely sharp biogeochemical interfaces in wetlands that could alter radionuclide speciation and promote accumulation. The objective of this study was to determine if a wetland concentrated (129)I emanating from a former waste disposal basin located on the Savannah River Site (SRS) in South Carolina, USA. Additionally, studies were conducted to evaluate the role of sediment organic matter in immobilizing the radioiodine. Groundwater samples were collected along a 0.7-km transect away from the seepage basin and in the downstream wetlands. The samples were analyzed for (129)I speciation (iodide (I(-)), iodate (IO3(-)), and organo-I). Groundwater (129)I concentrations in many locations in the wetlands (as high as 59.9 Bq L(-1)(129)I) were greatly elevated with respect to the source term (5.9 Bq L(-1)(129)I). (129)I concentration profiles in sediment cores were closely correlated to organic matter concentrations (r(2) = 0.992; n = 5). While the sediment organic matter promoted the uptake of (129)I to the wetland sediment, it also promoted the formation of a soluble organic fraction: 74% of the wetland groundwater (129)I could pass through a 1 kDa (wetlands may be highly effective at immobilizing aqueous (129)I, they may also promote the formation of a low-molecular-weight organic species that does not partition to sediments. This study provides a rare example of radioactivity concentrations increasing rather than decreasing as it migrates from a point source and brings into question assumptions in risk models regarding continuous dilution of released contaminants.

  16. Implementing an Agent Based Artificial Stock Market Model in JADE – An Illustration

    Directory of Open Access Journals (Sweden)

    PN Kumar

    2013-06-01

    Full Text Available Agent-based approach to economic and financial analysis is a suitable research methodolgy for developing and understanding the complex patterns and phenomena that are observed in economic systems. In agent-based financial market models, prices can be endogenously formed by the system itself as the result of interaction of market participants. By using agents for the study, heterogeneous, boundedly rational, and adaptive behaviour of market participants can be analysed and its impact assessed. The collective behaviour of such groups is determined by the interaction of individual behaviours distributed across the group. This being the scenario prevailing in stock markets, agent based models are suitable for the study. Through this paper, we have attempted to illustrate a detailed implementation of multi agents in an artificial stock market invoking the agent-based methodology on Java Agent Development (JADE environment, a platform to develop multi-agent systems. The Extended Glosten and Milgrom Model, an agent based artificial stock market model, has been chosen to depict the multi-agent environment model in JADE.

  17. Modelling bioclogging in variably saturated porous media and the interactions between surface/subsurface flows: Application to Constructed Wetlands.

    Science.gov (United States)

    Samsó, Roger; García, Joan; Molle, Pascal; Forquet, Nicolas

    2016-01-01

    Horizontal subsurface Flow Constructed Wetlands (HF CWs) are biofilters planted with aquatic macrophytes within which wastewater is treated mostly through contact with bacterial biofilms. The high concentrations of organic carbon and nutrients being transported leads to high bacterial biomass production, which decreases the flow capacity of the porous material (bioclogging). In severe bioclogging scenarios, overland flow may take place, reducing overall treatment performance. In this work we developed a mathematical model using COMSOL Multiphysics™ and MATLAB(®) to simulate bioclogging effects in HF CWs. Variably saturated subsurface flow and overland flow were described using the Richards equation. To simplify the inherent complexity of the processes involved in bioclogging development, only one bacterial group was considered, and its growth was described using a Monod equation. Bioclogging effects on the hydrodynamics were taken into account by using a conceptual model that affects the value of Mualem's unsaturated relative permeability. Simulation results with and without bioclogging were compared to showcase the impact of this process on the overall functioning of CWs. The two scenarios rendered visually different bacteria distributions, flow and transport patterns, showing the necessity of including bioclogging effects on CWs models. This work represents one of the few studies available on bioclogging in variably saturated conditions, and the presented model allows simulating the interaction between overland and subsurface flow occurring in most HF CWs. Hence, this work gets us a step closer to being able to describe CWs functioning in an integrated way using mathematical models. Copyright © 2015 Elsevier Ltd. All rights reserved.

  18. Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp.

    Science.gov (United States)

    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. PMID:26368924

  19. 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.

  20. A conceptual and computational model of moral decision making in human and artificial agents.

    Science.gov (United States)

    Wallach, Wendell; Franklin, Stan; Allen, Colin

    2010-07-01

    Recently, there has been a resurgence of interest in general, comprehensive models of human cognition. Such models aim to explain higher-order cognitive faculties, such as deliberation and planning. Given a computational representation, the validity of these models can be tested in computer simulations such as software agents or embodied robots. The push to implement computational models of this kind has created the field of artificial general intelligence (AGI). Moral decision making is arguably one of the most challenging tasks for computational approaches to higher-order cognition. The need for increasingly autonomous artificial agents to factor moral considerations into their choices and actions has given rise to another new field of inquiry variously known as Machine Morality, Machine Ethics, Roboethics, or Friendly AI. In this study, we discuss how LIDA, an AGI model of human cognition, can be adapted to model both affective and rational features of moral decision making. Using the LIDA model, we will demonstrate how moral decisions can be made in many domains using the same mechanisms that enable general decision making. Comprehensive models of human cognition typically aim for compatibility with recent research in the cognitive and neural sciences. Global workspace theory, proposed by the neuropsychologist Bernard Baars (1988), is a highly regarded model of human cognition that is currently being computationally instantiated in several software implementations. LIDA (Franklin, Baars, Ramamurthy, & Ventura, 2005) is one such computational implementation. LIDA is both a set of computational tools and an underlying model of human cognition, which provides mechanisms that are capable of explaining how an agent's selection of its next action arises from bottom-up collection of sensory data and top-down processes for making sense of its current situation. We will describe how the LIDA model helps integrate emotions into the human decision-making process, and we

  1. Artificial Neural Network Turbulent Modeling for Predicting the Pressure Drop of Nanofluid

    Directory of Open Access Journals (Sweden)

    M. S. Youssef

    2013-10-01

    Full Text Available An Artificial Neural Network (ANN model was developed to predict the pressure drop of titanium dioxide-water (TiO2-water. The model was developed based on experimentally measured data. Experimental measurements of fully developed turbulent flow in pipe at different particle volumetric concentrations, nanoparticle diameters, nanofluid temperature and Reynolds number were used to construct the proposed model. The ANN model was validated by comparing the predicted results with the experimental measured data at different experimental conditions. It was shown that, the present ANN model performed well in predicting the pressure drop of TiO2-water nanofluid under different flow conditions with a high degree of accuracy.

  2. Connectionist models of artificial grammar learning: what type of knowledge is acquired?

    Science.gov (United States)

    Kinder, Annette; Lotz, Anja

    2009-09-01

    Two experiments are presented that test the predictions of two associative learning models of Artificial Grammar Learning. The two models are the simple recurrent network (SRN) and the competitive chunking (CC) model. The two experiments investigate acquisition of different types of knowledge in this task: knowledge of frequency and novelty of stimulus fragments (Experiment 1) and knowledge of letter positions, of small fragments, and of large fragments up to entire strings (Experiment 2). The results show that participants acquired all types of knowledge. Simulation studies demonstrate that the CC model explains the acquisition of all types of fragment knowledge but fails to account for the acquisition of positional knowledge. The SRN model, by contrast, accounts for the entire pattern of results found in the two experiments.

  3. A Hybrid Fresh Apple Export Volume Forecasting Model Based on Time Series and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Lihua Yang

    2015-04-01

    Full Text Available Export volume forecasting of fresh fruits is a complex task due to the large number of factors affecting the demand. In order to guide the fruit growers’ sales, decreasing the cultivating cost and increasing their incomes, a hybrid fresh apple export volume forecasting model is proposed. Using the actual data of fresh apple export volume, the Seasonal Decomposition (SD model of time series and Radial Basis Function (RBF model of artificial neural network are built. The predictive results are compared among the three forecasting model based on the criterion of Mean Absolute Percentage Error (MAPE. The result indicates that the proposed combined forecasting model is effective because it can improve the prediction accuracy of fresh apple export volumes.

  4. Explaining the internal behaviour of artificial neural network river flow models

    Science.gov (United States)

    Sudheer, K. P.; Jain, Ashu

    2004-03-01

    A novel method of visualizing and understanding the internal functional behaviour of an artificial neural network (ANN) river flow model is presented. The method hypothesizes that an ANN is able to map a function similar to the flow duration curve while modelling the river flow. A mathematical analysis of the hypothesis is presented, and a case study of an ANN river flow model confirms its significance. The proposed approach is also useful within other models that improve the performance of an ANN. The reasons why these models improve a raw ANN can be clearly understood using this approach. While the field of ANN knowledge-extraction is one that continues to attract considerable interest, it is anticipated that the current approach will initiate further research and make ANNs more useful to the hydrologic community.

  5. Implementation of recurrent artificial neural networks for nonlinear dynamic modeling in biomedical applications.

    Science.gov (United States)

    Stošovic, Miona V Andrejevic; Litovski, Vanco B

    2013-11-01

    Simulation is indispensable during the design of many biomedical prostheses that are based on fundamental electrical and electronic actions. However, simulation necessitates the use of adequate models. The main difficulties related to the modeling of such devices are their nonlinearity and dynamic behavior. Here we report the application of recurrent artificial neural networks for modeling of a nonlinear, two-terminal circuit equivalent to a specific implantable hearing device. The method is general in the sense that any nonlinear dynamic two-terminal device or circuit may be modeled in the same way. The model generated was successfully used for simulation and optimization of a driver (operational amplifier)-transducer ensemble. This confirms our claim that in addition to the proper design and optimization of the hearing actuator, optimization in the electronic domain, at the electronic driver circuit-to-actuator interface, should take place in order to achieve best performance of the complete hearing aid.

  6. 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.

  7. 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

  8. Modeling the cooling performance of vortex tube using a genetic algorithm-based artificial neural network

    Directory of Open Access Journals (Sweden)

    Pouraria Hassan

    2016-01-01

    Full Text Available In this study, artificial neural networks (ANNs have been used to model the effects of four important parameters consist of the ratio of the length to diameter(L/D, the ratio of the cold outlet diameter to the tube diameter(d/D, inlet pressure(P, and cold mass fraction (Y on the cooling performance of counter flow vortex tube. In this approach, experimental data have been used to train and validate the neural network model with MATLAB software. Also, genetic algorithm (GA has been used to find the optimal network architecture. In this model, temperature drop at the cold outlet has been considered as the cooling performance of the vortex tube. Based on experimental data, cooling performance of the vortex tube has been predicted by four inlet parameters (L/D, d/D, P, Y. The results of this study indicate that the genetic algorithm-based artificial neural network model is capable of predicting the cooling performance of vortex tube in a wide operating range and with satisfactory precision.

  9. 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

  10. 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.

  11. Spatiotemporal groundwater level modeling using hybrid artificial intelligence-meshless method

    Science.gov (United States)

    Nourani, Vahid; Mousavi, Shahram

    2016-05-01

    Uncertainties of the field parameters, noise of the observed data and unknown boundary conditions are the main factors involved in the groundwater level (GL) time series which limit the modeling and simulation of GL. This paper presents a hybrid artificial intelligence-meshless model for spatiotemporal GL modeling. In this way firstly time series of GL observed in different piezometers were de-noised using threshold-based wavelet method and the impact of de-noised and noisy data was compared in temporal GL modeling by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). In the second step, both ANN and ANFIS models were calibrated and verified using GL data of each piezometer, rainfall and runoff considering various input scenarios to predict the GL at one month ahead. In the final step, the simulated GLs in the second step of modeling were considered as interior conditions for the multiquadric radial basis function (RBF) based solve of governing partial differential equation of groundwater flow to estimate GL at any desired point within the plain where there is not any observation. In order to evaluate and compare the GL pattern at different time scales, the cross-wavelet coherence was also applied to GL time series of piezometers. The results showed that the threshold-based wavelet de-noising approach can enhance the performance of the modeling up to 13.4%. Also it was found that the accuracy of ANFIS-RBF model is more reliable than ANN-RBF model in both calibration and validation steps.

  12. The Evolution of a Malignancy Risk Prediction Model for Thyroid Nodules Using the Artificial Neural Network

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    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

  13. An Artificial Neural Network Model for the Wholesale Company Order's Cycle Management

    Directory of Open Access Journals (Sweden)

    Tereza Sustrova

    2016-06-01

    Full Text Available The purpose of this article is to verify the possibility of using artificial neural networks (ANN in business management processes, primarily in the area of supply chain management. The author has designed several neural network models featuring different architectures to optimize the level of the company’s inventory. The results of the research show that ANN can be used for managing a company’s order cycle and lead to reduced levels of goods purchased and storage costs. Optimal neural networks show suitable results for subsequent prediction of the amount of items to be ordered and for achieving reduced inventory purchase and keeping costs down.

  14. A 3D model describing the initial structure of an artificial hydrological catchment

    Science.gov (United States)

    Maurer, T.; Schneider, A.; Buczko, U.; Gerke, H. H.

    2009-04-01

    The initial development stages of artificially constructed hydrologic catchments are characterized by the absence of vegetation, soil organic matter and soil horizons. This results in increased surface runoff and favors erosion processes that dominate the initial phase. Hydraulic conditions on artificial catchments thus are governed by rapidly changing surface structures as well as by the primary internal structural framework. Contemporary hydrological modeling does not consider any dynamic change of relevant structural features but rather assumes a stable, invariant landscape. The objective of this study was the digital visualization and quantitative description of the initial state and its early structural dynamics, exemplified for the small artificial hydrological catchment "Huehnerwasser" near Cottbus, Germany. Photogrammetric surveys of surface and internal structural units (clay basis liner) during the construction phase provided spatially and temporally resolved data for digital elevation models (DEM). Interpolated physical and chemical soil properties obtained at a borehole grid (e.g., texture) are used for the visualization of spatial distribution of relevant (hydraulic) parameters. The data are merged in a database and visualized in the 3D-GIS application GoCAD. The specific technological construction processes determines the internal structure of the artificial catchment. Resulting differences in bulk density and texture are supposed to have considerable impact on hydraulic properties. A structure generator program was implemented to reproduce the initial structure of the sediment layer as closely as possible. Results of the digital structure generation are checked with non-invasive geophysical measurements, on-site bore holes data and off-site 2D vertical spoil exploration. The accuracy of structure generator results will be compared with predictions of different interpolation methods. Thus, the structure model will serve as a basis for deriving the 3D

  15. Subpixel Inundation Mapping Using Landsat-8 OLI and UAV Data for a Wetland Region on the Zoige Plateau, China

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    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.

  16. 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.

  17. Establishment of constitutive relationship model for 2519 aluminum alloy based on BP artificial neural network

    Institute of Scientific and Technical Information of China (English)

    LIN Qi-quan; PENG Da-shu; ZHU Yuan-zhi

    2005-01-01

    An isothermal compressive experiment using Gleeble 1500 thermal simulator was studied to acquire flow stress at different deformation temperatures, strains and strain rates. The artificial neural networks with the error back propagation(BP) algorithm was used to establish constitutive model of 2519 aluminum alloy based on the experiment data. The model results show that the systematical error is small(δ=3.3%) when the value of objective function is 0.2, the number of nodes in the hidden layer is 5 and the learning rate is 0.1. Flow stresses of the material under various thermodynamic conditions are predicted by the neural network model, and the predicted results correspond with the experimental results. A knowledge-based constitutive relation model is developed.

  18. 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.

  19. Modeling and prediction of retardance in citric acid coated ferrofluid using artificial neural network

    Science.gov (United States)

    Lin, Jing-Fung; Sheu, Jer-Jia

    2016-06-01

    Citric acid coated (citrate-stabilized) magnetite (Fe3O4) magnetic nanoparticles have been conducted and applied in the biomedical fields. Using Taguchi-based measured retardances as the training data, an artificial neural network (ANN) model was developed for the prediction of retardance in citric acid (CA) coated ferrofluid (FF). According to the ANN simulation results in the training stage, the correlation coefficient between predicted retardances and measured retardances was found to be as high as 0.9999998. Based on the well-trained ANN model, the predicted retardance at excellent program from Taguchi method showed less error of 2.17% compared with a multiple regression (MR) analysis of statistical significance. Meanwhile, the parameter analysis at excellent program by the ANN model had the guiding significance to find out a possible program for the maximum retardance. It was concluded that the proposed ANN model had high ability for the prediction of retardance in CA coated FF.

  20. Application of artificial neural engineering and regression models for forecasting shelf life of instant coffee drink

    Directory of Open Access Journals (Sweden)

    Sumit Goyal

    2011-07-01

    Full Text Available Coffee as beverage is prepared from the roasted seeds (beans of the coffee plant. Coffee is the second most important product in the international market in terms of volume trade and the most important in terms of value. Artificial neural engineering and regression models were developed to predict shelf life of instant coffee drink. Colour and appearance, flavour, viscosity and sediment were used as input parameters. Overall acceptability was used as output parameter. The dataset consisted of experimentally developed 50 observations. The dataset was divided into two disjoint subsets, namely, training set containing 40 observations (80% of total observations and test set comprising of 10 observations (20% of total observations. The network was trained with 500 epochs. Neural network toolbox under Matlab 7.0 software was used for training the models. From the investigation it was revealed that multiple linear regression model was superior over radial basis model for forecasting shelf life of instant coffee drink.