WorldWideScience

Sample records for model predicting environmental

  1. Predictions of models for environmental radiological assessment

    International Nuclear Information System (INIS)

    Peres, Sueli da Silva; Lauria, Dejanira da Costa; Mahler, Claudio Fernando

    2011-01-01

    In the field of environmental impact assessment, models are used for estimating source term, environmental dispersion and transfer of radionuclides, exposure pathway, radiation dose and the risk for human beings Although it is recognized that the specific information of local data are important to improve the quality of the dose assessment results, in fact obtaining it can be very difficult and expensive. Sources of uncertainties are numerous, among which we can cite: the subjectivity of modelers, exposure scenarios and pathways, used codes and general parameters. The various models available utilize different mathematical approaches with different complexities that can result in different predictions. Thus, for the same inputs different models can produce very different outputs. This paper presents briefly the main advances in the field of environmental radiological assessment that aim to improve the reliability of the models used in the assessment of environmental radiological impact. The intercomparison exercise of model supplied incompatible results for 137 Cs and 60 Co, enhancing the need for developing reference methodologies for environmental radiological assessment that allow to confront dose estimations in a common comparison base. The results of the intercomparison exercise are present briefly. (author)

  2. PEEX Modelling Platform for Seamless Environmental Prediction

    Science.gov (United States)

    Baklanov, Alexander; Mahura, Alexander; Arnold, Stephen; Makkonen, Risto; Petäjä, Tuukka; Kerminen, Veli-Matti; Lappalainen, Hanna K.; Ezau, Igor; Nuterman, Roman; Zhang, Wen; Penenko, Alexey; Gordov, Evgeny; Zilitinkevich, Sergej; Kulmala, Markku

    2017-04-01

    The Pan-Eurasian EXperiment (PEEX) is a multidisciplinary, multi-scale research programme stared in 2012 and aimed at resolving the major uncertainties in Earth System Science and global sustainability issues concerning the Arctic and boreal Northern Eurasian regions and in China. Such challenges include climate change, air quality, biodiversity loss, chemicalization, food supply, and the use of natural resources by mining, industry, energy production and transport. The research infrastructure introduces the current state of the art modeling platform and observation systems in the Pan-Eurasian region and presents the future baselines for the coherent and coordinated research infrastructures in the PEEX domain. The PEEX modeling Platform is characterized by a complex seamless integrated Earth System Modeling (ESM) approach, in combination with specific models of different processes and elements of the system, acting on different temporal and spatial scales. The ensemble approach is taken to the integration of modeling results from different models, participants and countries. PEEX utilizes the full potential of a hierarchy of models: scenario analysis, inverse modeling, and modeling based on measurement needs and processes. The models are validated and constrained by available in-situ and remote sensing data of various spatial and temporal scales using data assimilation and top-down modeling. The analyses of the anticipated large volumes of data produced by available models and sensors will be supported by a dedicated virtual research environment developed for these purposes.

  3. Evaluating the reliability of predictions made using environmental transfer models

    International Nuclear Information System (INIS)

    1989-01-01

    The development and application of mathematical models for predicting the consequences of releases of radionuclides into the environment from normal operations in the nuclear fuel cycle and in hypothetical accident conditions has increased dramatically in the last two decades. This Safety Practice publication has been prepared to provide guidance on the available methods for evaluating the reliability of environmental transfer model predictions. It provides a practical introduction of the subject and a particular emphasis has been given to worked examples in the text. It is intended to supplement existing IAEA publications on environmental assessment methodology. 60 refs, 17 figs, 12 tabs

  4. Predicting People's Environmental Behaviour: Theory of Planned Behaviour and Model of Responsible Environmental Behaviour

    Science.gov (United States)

    Chao, Yu-Long

    2012-01-01

    Using different measures of self-reported and other-reported environmental behaviour (EB), two important theoretical models explaining EB--Hines, Hungerford and Tomera's model of responsible environmental behaviour (REB) and Ajzen's theory of planned behaviour (TPB)--were compared regarding the fit between model and data, predictive ability,…

  5. Predictive Model for Environmental Assessment in Additive Manufacturing Process

    OpenAIRE

    Le Bourhis , Florent; Kerbrat , Olivier; Dembinski , Lucas; Hascoët , Jean-Yves; Mognol , Pascal

    2014-01-01

    International audience; Additive Manufacturing is an innovative way to produce parts. However its environmental impact is unknown. To ensure the development of additive manufacturing processes it seems important to develop the concept of DFSAM (Design for Sustainable Additive Manufacturing). In fact, one of the objectives of environmental sustainable manufacturing is to minimize the whole flux consumption (electricity, material and fluids) during manufacturing step. To achieve this goal, it i...

  6. Comparison of marine dispersion model predictions with environmental radionuclide concentrations

    International Nuclear Information System (INIS)

    Johnson, C.E.; McKay, W.A.

    1988-01-01

    The comparison of marine dispersion model results with measurements is an essential part of model development and testing. The results from two residual flow models are compared with seawater concentrations, and in one case with concentrations measured in marine molluscs. For areas with short turnover times, seawater concentrations respond rapidly to variations in discharge rate and marine currents. These variations are difficult to model, and comparison with concentrations in marine animals provides an alternative and complementary technique for model validation with the advantages that the measurements reflect the mean conditions and frequently form a useful time series. (author)

  7. Cybernetic modeling of adaptive prediction of environmental changes by microorganisms.

    Science.gov (United States)

    Mandli, Aravinda R; Modak, Jayant M

    2014-02-01

    Microorganisms exhibit varied regulatory strategies such as direct regulation, symmetric anticipatory regulation, asymmetric anticipatory regulation, etc. Current mathematical modeling frameworks for the growth of microorganisms either do not incorporate regulation or assume that the microorganisms utilize the direct regulation strategy. In the present study, we extend the cybernetic modeling framework to account for asymmetric anticipatory regulation strategy. The extended model accurately captures various experimental observations. We use the developed model to explore the fitness advantage provided by the asymmetric anticipatory regulation strategy and observe that the optimal extent of asymmetric regulation depends on the selective pressure that the microorganisms experience. We also explore the importance of timing the response in anticipatory regulation and find that there is an optimal time, dependent on the extent of asymmetric regulation, at which microorganisms should respond anticipatorily to maximize their fitness. We then discuss the advantages offered by the cybernetic modeling framework over other modeling frameworks in modeling the asymmetric anticipatory regulation strategy. Copyright © 2013. Published by Elsevier Inc.

  8. Integrating Environmental Optics into Multidisciplinary, Predictive Models of Ocean Dynamics

    Science.gov (United States)

    2011-09-30

    Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 2 New modeling system – The photosynthetic pigment chlorophyll a plays a pivotal role in...directly supported. This research program was an extension of a long collaboration with Marlon Lewis coordinated through the NSERC/Satlantic Industrial ...the C-MORE summer course in Microbial Oceanography at the University of Hawaii (Cullen 2011). RESULTS Analysis of an extensive set of optical

  9. Group contribution modelling for the prediction of safety-related and environmental properties

    DEFF Research Database (Denmark)

    Frutiger, Jerome; Abildskov, Jens; Sin, Gürkan

    We present a new set of property prediction models based on group contributions to predict major safety-related and environmental properties for organic compounds. The predicted list of properties includes lower and upper flammability limits, heat of combustion, auto ignition temperature, global...... warming potential and ozone depletion potential. Process safety studies and environmental assessments rely on accurate property data. Safety data such as flammability limits, heat of combustion or auto ignition temperature play an important role in quantifying the risk of fire and explosions among others....... Global warming potential and ozone depletion potential became a standard to analyze the environmental impact of processes and products. In the early stage of process development and analysis, experimental values are often not available due to cost or time constraints. In this case property prediction...

  10. What role can simulation model predictions play in environmental decisions: carbon dioxide as an example

    International Nuclear Information System (INIS)

    Emanuel, W.R.

    1979-01-01

    Frequently, when an environmental issue requiring quantitative analysis surfaces, the development of a model synthesizing all aspects of the problem and applicable at each stage of the decision process is proposed. A more desirable alternative is to generate models specifically designed to meet the requirements of each level in decision making and which can be adapted in response to the changing status of the environmental issue. Various models of the global carbon cycle constructed to predict levels of CO 2 in the atmosphere as a result of man's activities are described to illustrate this point. In summary, the progression of models developed to analyze the global carbon cycle in resolving the CO 2 /climate issue indicates the changing character of models depending on the immediate role they play in environmental decision making. The dominant and successful role served by models in the carbon cycle problem points to the desirability of this flexible approach

  11. Evaluation of selected predictive models and parameters for the environmental transport and dosimetry of radionuclides

    International Nuclear Information System (INIS)

    Miller, C.W.; Dunning, D.E. Jr.; Etnier, E.L.; Hoffman, F.O.; Little, C.A.; Meyer, H.R.; Shaeffer, D.L.; Till, J.E.

    1979-07-01

    Evaluations of selected predictive models and parameters used in the assessment of the environmental transport and dosimetry of radionuclides are summarized. Mator sections of this report include a validation of the Gaussian plume disperson model, comparison of the output of a model for the transport of 131 I from vegetation to milk with field data, validation of a model for the fraction of aerosols intercepted by vegetation, an evaluation of dose conversion factors for 232 Th, an evaluation of considering the effect of age dependency on population dose estimates, and a summary of validation results for hydrologic transport models

  12. Evaluation of selected predictive models and parameters for the environmental transport and dosimetry of radionuclides

    Energy Technology Data Exchange (ETDEWEB)

    Miller, C.W.; Dunning, D.E. Jr.; Etnier, E.L.; Hoffman, F.O.; Little, C.A.; Meyer, H.R.; Shaeffer, D.L.; Till, J.E.

    1979-07-01

    Evaluations of selected predictive models and parameters used in the assessment of the environmental transport and dosimetry of radionuclides are summarized. Mator sections of this report include a validation of the Gaussian plume disperson model, comparison of the output of a model for the transport of /sup 131/I from vegetation to milk with field data, validation of a model for the fraction of aerosols intercepted by vegetation, an evaluation of dose conversion factors for /sup 232/Th, an evaluation of considering the effect of age dependency on population dose estimates, and a summary of validation results for hydrologic transport models.

  13. Estimation of uncertainties in predictions of environmental transfer models: evaluation of methods and application to CHERPAC

    International Nuclear Information System (INIS)

    Koch, J.; Peterson, S-R.

    1995-10-01

    Models used to simulate environmental transfer of radionuclides typically include many parameters, the values of which are uncertain. An estimation of the uncertainty associated with the predictions is therefore essential. Difference methods to quantify the uncertainty in the prediction parameter uncertainties are reviewed. A statistical approach using random sampling techniques is recommended for complex models with many uncertain parameters. In this approach, the probability density function of the model output is obtained from multiple realizations of the model according to a multivariate random sample of the different input parameters. Sampling efficiency can be improved by using a stratified scheme (Latin Hypercube Sampling). Sample size can also be restricted when statistical tolerance limits needs to be estimated. Methods to rank parameters according to their contribution to uncertainty in the model prediction are also reviewed. Recommended are measures of sensitivity, correlation and regression coefficients that can be calculated on values of input and output variables generated during the propagation of uncertainties through the model. A parameter uncertainty analysis is performed for the CHERPAC food chain model which estimates subjective confidence limits and intervals on the predictions at a 95% confidence level. A sensitivity analysis is also carried out using partial rank correlation coefficients. This identified and ranks the parameters which are the main contributors to uncertainty in the predictions, thereby guiding further research efforts. (author). 44 refs., 2 tabs., 4 figs

  14. Environmental modeling, technology, and communication for land falling tropical cyclone/hurricane prediction.

    Science.gov (United States)

    Tuluri, Francis; Reddy, R Suseela; Anjaneyulu, Y; Colonias, John; Tchounwou, Paul

    2010-05-01

    Katrina (a tropical cyclone/hurricane) began to strengthen reaching a Category 5 storm on 28th August, 2005 and its winds reached peak intensity of 175 mph and pressure levels as low as 902 mb. Katrina eventually weakened to a category 3 storm and made a landfall in Plaquemines Parish, Louisiana, Gulf of Mexico, south of Buras on 29th August 2005. We investigate the time series intensity change of the hurricane Katrina using environmental modeling and technology tools to develop an early and advanced warning and prediction system. Environmental Mesoscale Model (Weather Research Forecast, WRF) simulations are used for prediction of intensity change and track of the hurricane Katrina. The model is run on a doubly nested domain centered over the central Gulf of Mexico, with grid spacing of 90 km and 30 km for 6 h periods, from August 28th to August 30th. The model results are in good agreement with the observations suggesting that the model is capable of simulating the surface features, intensity change and track and precipitation associated with hurricane Katrina. We computed the maximum vertical velocities (W(max)) using Convective Available Kinetic Energy (CAPE) obtained at the equilibrium level (EL), from atmospheric soundings over the Gulf Coast stations during the hurricane land falling for the period August 21-30, 2005. The large vertical atmospheric motions associated with the land falling hurricane Katrina produced severe weather including thunderstorms and tornadoes 2-3 days before landfall. The environmental modeling simulations in combination with sounding data show that the tools may be used as an advanced prediction and communication system (APCS) for land falling tropical cyclones/hurricanes.

  15. Environmental Modeling, Technology, and Communication for Land Falling Tropical Cyclone/Hurricane Prediction

    Directory of Open Access Journals (Sweden)

    Paul Tchounwou

    2010-04-01

    Full Text Available Katrina (a tropical cyclone/hurricane began to strengthen reaching a Category 5 storm on 28th August, 2005 and its winds reached peak intensity of 175 mph and pressure levels as low as 902 mb. Katrina eventually weakened to a category 3 storm and made a landfall in Plaquemines Parish, Louisiana, Gulf of Mexico, south of Buras on 29th August 2005. We investigate the time series intensity change of the hurricane Katrina using environmental modeling and technology tools to develop an early and advanced warning and prediction system. Environmental Mesoscale Model (Weather Research Forecast, WRF simulations are used for prediction of intensity change and track of the hurricane Katrina. The model is run on a doubly nested domain centered over the central Gulf of Mexico, with grid spacing of 90 km and 30 km for 6 h periods, from August 28th to August 30th. The model results are in good agreement with the observations suggesting that the model is capable of simulating the surface features, intensity change and track and precipitation associated with hurricane Katrina. We computed the maximum vertical velocities (Wmax using Convective Available Kinetic Energy (CAPE obtained at the equilibrium level (EL, from atmospheric soundings over the Gulf Coast stations during the hurricane land falling for the period August 21–30, 2005. The large vertical atmospheric motions associated with the land falling hurricane Katrina produced severe weather including thunderstorms and tornadoes 2–3 days before landfall. The environmental modeling simulations in combination with sounding data show that the tools may be used as an advanced prediction and communication system (APCS for land falling tropical cyclones/hurricanes.

  16. Generalized Concentration Addition Modeling Predicts Mixture Effects of Environmental PPARγ Agonists.

    Science.gov (United States)

    Watt, James; Webster, Thomas F; Schlezinger, Jennifer J

    2016-09-01

    The vast array of potential environmental toxicant combinations necessitates the development of efficient strategies for predicting toxic effects of mixtures. Current practices emphasize the use of concentration addition to predict joint effects of endocrine disrupting chemicals in coexposures. Generalized concentration addition (GCA) is one such method for predicting joint effects of coexposures to chemicals and has the advantage of allowing for mixture components to have differences in efficacy (ie, dose-response curve maxima). Peroxisome proliferator-activated receptor gamma (PPARγ) is a nuclear receptor that plays a central role in regulating lipid homeostasis, insulin sensitivity, and bone quality and is the target of an increasing number of environmental toxicants. Here, we tested the applicability of GCA in predicting mixture effects of therapeutic (rosiglitazone and nonthiazolidinedione partial agonist) and environmental PPARγ ligands (phthalate compounds identified using EPA's ToxCast database). Transcriptional activation of human PPARγ1 by individual compounds and mixtures was assessed using a peroxisome proliferator response element-driven luciferase reporter. Using individual dose-response parameters and GCA, we generated predictions of PPARγ activation by the mixtures, and we compared these predictions with the empirical data. At high concentrations, GCA provided a better estimation of the experimental response compared with 3 alternative models: toxic equivalency factor, effect summation and independent action. These alternatives provided reasonable fits to the data at low concentrations in this system. These experiments support the implementation of GCA in mixtures analysis with endocrine disrupting compounds and establish PPARγ as an important target for further studies of chemical mixtures. © The Author 2016. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e

  17. Spatial characterization and prediction of Neanderthal sites based on environmental information and stochastic modelling

    Science.gov (United States)

    Maerker, Michael; Bolus, Michael

    2014-05-01

    We present a unique spatial dataset of Neanderthal sites in Europe that was used to train a set of stochastic models to reveal the correlations between the site locations and environmental indices. In order to assess the relations between the Neanderthal sites and environmental variables as described above we applied a boosted regression tree approach (TREENET) a statistical mechanics approach (MAXENT) and support vector machines. The stochastic models employ a learning algorithm to identify a model that best fits the relationship between the attribute set (predictor variables (environmental variables) and the classified response variable which is in this case the types of Neanderthal sites. A quantitative evaluation of model performance was done by determining the suitability of the model for the geo-archaeological applications and by helping to identify those aspects of the methodology that need improvements. The models' predictive performances were assessed by constructing the Receiver Operating Characteristics (ROC) curves for each Neanderthal class, both for training and test data. In a ROC curve the Sensitivity is plotted over the False Positive Rate (1-Specificity) for all possible cut-off points. The quality of a ROC curve is quantified by the measure of the parameter area under the ROC curve. The dependent variable or target variable in this study are the locations of Neanderthal sites described by latitude and longitude. The information on the site location was collected from literature and own research. All sites were checked for site accuracy using high resolution maps and google earth. The study illustrates that the models show a distinct ranking in model performance with TREENET outperforming the other approaches. Moreover Pre-Neanderthals, Early Neanderthals and Classic Neanderthals show a specific spatial distribution. However, all models show a wide correspondence in the selection of the most important predictor variables generally showing less

  18. Environmental Modeling

    Science.gov (United States)

    EPA's modeling community is working to gain insights into certain parts of a physical, biological, economic, or social system by conducting environmental assessments for Agency decision making to complex environmental issues.

  19. A Modelling Study for Predicting Life of Downhole Tubes Considering Service Environmental Parameters and Stress

    Directory of Open Access Journals (Sweden)

    Tianliang Zhao

    2016-09-01

    Full Text Available A modelling effort was made to try to predict the life of downhole tubes or casings, synthetically considering the effect of service influencing factors on corrosion rate. Based on the discussed corrosion mechanism and corrosion processes of downhole tubes, a mathematic model was established. For downhole tubes, the influencing factors are environmental parameters and stress, which vary with service duration. Stress and the environmental parameters including water content, partial pressure of H2S and CO2, pH value, total pressure and temperature, were considered to be time-dependent. Based on the model, life-span of an L80 downhole tube in oilfield Halfaya, an oilfield in Iraq, was predicted. The results show that life-span of the L80 downhole tube in Halfaya is 247 months (approximately 20 years under initial stress of 0.1 yield strength and 641 months (approximately 53 years under no initial stress, which indicates that an initial stress of 0.1 yield strength will reduce the life-span by more than half.

  20. A Modelling Study for Predicting Life of Downhole Tubes Considering Service Environmental Parameters and Stress

    Science.gov (United States)

    Zhao, Tianliang; Liu, Zhiyong; Du, Cuiwei; Hu, Jianpeng; Li, Xiaogang

    2016-01-01

    A modelling effort was made to try to predict the life of downhole tubes or casings, synthetically considering the effect of service influencing factors on corrosion rate. Based on the discussed corrosion mechanism and corrosion processes of downhole tubes, a mathematic model was established. For downhole tubes, the influencing factors are environmental parameters and stress, which vary with service duration. Stress and the environmental parameters including water content, partial pressure of H2S and CO2, pH value, total pressure and temperature, were considered to be time-dependent. Based on the model, life-span of an L80 downhole tube in oilfield Halfaya, an oilfield in Iraq, was predicted. The results show that life-span of the L80 downhole tube in Halfaya is 247 months (approximately 20 years) under initial stress of 0.1 yield strength and 641 months (approximately 53 years) under no initial stress, which indicates that an initial stress of 0.1 yield strength will reduce the life-span by more than half. PMID:28773872

  1. General fugacity-based model to predict the environmental fate of multiple chemical species.

    Science.gov (United States)

    Cahill, Thomas M; Cousins, Ian; Mackay, Donald

    2003-03-01

    A general multimedia environmental fate model is presented that is capable of simulating the fate of up to four interconverting chemical species. It is an extension of the existing equilibrium criterion (EQC) fugacity model, which is limited to single-species assessments. It is suggested that multispecies chemical assessments are warranted when a degradation product of a released chemical is either more toxic or more persistent than the parent chemical or where there is cycling between species, as occurs with association, disassociation, or ionization. The model is illustratively applied to three chemicals, namely chlorpyrifos, pentachlorophenol, and perfluorooctane sulfonate, for which multispecies assessments are advisable. The model results compare favorably with field data for chlorpyrifos and pentachlorophenol, while the perfluorooctane sulfonate simulation is more speculative due to uncertainty in input parameters and the paucity of field data to validate the predictions. The model thus provides a tool for assessing the environmental fate and behavior of a group of chemicals that hitherto have not been addressed by evaluative models such as EQC.

  2. [Simulation and prediction of water environmental carrying capacity in Liaoning Province based on system dynamics model].

    Science.gov (United States)

    Wang, Jian; Li, Xue-liang; Li, Fa-yun; Bao, Hong-xu

    2009-09-01

    By the methods of system dynamics, a water environmental carrying capacity (WECC) model was constructed, and the dynamic trend of the WECC in Liaoning Province was simulated by using this model, in combining with analytical hierarchy process (AHP) and the vector norm method. It was predicted that under the conditions of maintaining present development schemes, the WECC in this province in 2000-2050 would be decreased year after year. Only increasing water resources supply while not implementing scientific and rational management of water environment could not improve the regional WECC, and the integration of searching for new and saving present water resources with controlling wastewater pollution and reducing sewage discharge would be the only effective way to improve the WECC and the coordinated development of economy, society, and environment in Liaoning.

  3. Predicting biological parameters of estuarine benthic communities using models based on environmental data

    Directory of Open Access Journals (Sweden)

    José Souto Rosa-Filho

    2004-08-01

    Full Text Available This study aimed to predict the biological parameters (species composition, abundance, richness, diversity and evenness of benthic assemblages in southern Brazil estuaries using models based on environmental data (sediment characteristics, salinity, air and water temperature and depth. Samples were collected seasonally from five estuaries between the winter of 1996 and the summer of 1998. At each estuary, samples were taken in unpolluted areas with similar characteristics related to presence or absence of vegetation, depth and distance from the mouth. In order to obtain predictive models, two methods were used, the first one based on Multiple Discriminant Analysis (MDA, and the second based on Multiple Linear Regression (MLR. Models using MDA had better results than those based on linear regression. The best results using MLR were obtained for diversity and richness. It could be concluded that the use predictions models based on environmental data would be very useful in environmental monitoring studies in estuaries.Este trabalho objetivou predizer parâmetros da estrutura de associações macrobentônicas (composição específica, abundância, riqueza, diversidade e equitatividade em estuários do Sul do Brasil, utilizando modelos baseados em dados ambientais (características dos sedimentos, salinidade, temperaturas do ar e da água, e profundidade. As amostragens foram realizadas sazonalmente em cinco estuários entre o inverno de 1996 e o verão de 1998. Em cada estuário as amostras foram coletadas em áreas não poluídas, com características semelhantes quanto a presença ou ausência de vegetação, profundidade e distância da desenbocadura. Para a obtenção dos modelos de predição, foram utilizados dois métodos: o primeiro baseado em Análise Discriminante Múltipla (ADM e o segundo em Regressão Linear Múltipla (RLM. Os modelos baseados em ADM apresentaram resultados melhores do que os baseados em regressão linear. Os melhores

  4. A mixed modeling approach to predict the effect of environmental modification on species distributions.

    Directory of Open Access Journals (Sweden)

    Francesco Cozzoli

    Full Text Available Human infrastructures can modify ecosystems, thereby affecting the occurrence and spatial distribution of organisms, as well as ecosystem functionality. Sustainable development requires the ability to predict responses of species to anthropogenic pressures. We investigated the large scale, long term effect of important human alterations of benthic habitats with an integrated approach combining engineering and ecological modelling. We focused our analysis on the Oosterschelde basin (The Netherlands, which was partially embanked by a storm surge barrier (Oosterscheldekering, 1986. We made use of 1 a prognostic (numerical environmental (hydrodynamic model and 2 a novel application of quantile regression to Species Distribution Modeling (SDM to simulate both the realized and potential (habitat suitability abundance of four macrozoobenthic species: Scoloplos armiger, Peringia ulvae, Cerastoderma edule and Lanice conchilega. The analysis shows that part of the fluctuations in macrozoobenthic biomass stocks during the last decades is related to the effect of the coastal defense infrastructures on the basin morphology and hydrodynamics. The methodological framework we propose is particularly suitable for the analysis of large abundance datasets combined with high-resolution environmental data. Our analysis provides useful information on future changes in ecosystem functionality induced by human activities.

  5. Predictive analytics of environmental adaptability in multi-omic network models.

    Science.gov (United States)

    Angione, Claudio; Lió, Pietro

    2015-10-20

    Bacterial phenotypic traits and lifestyles in response to diverse environmental conditions depend on changes in the internal molecular environment. However, predicting bacterial adaptability is still difficult outside of laboratory controlled conditions. Many molecular levels can contribute to the adaptation to a changing environment: pathway structure, codon usage, metabolism. To measure adaptability to changing environmental conditions and over time, we develop a multi-omic model of Escherichia coli that accounts for metabolism, gene expression and codon usage at both transcription and translation levels. After the integration of multiple omics into the model, we propose a multiobjective optimization algorithm to find the allowable and optimal metabolic phenotypes through concurrent maximization or minimization of multiple metabolic markers. In the condition space, we propose Pareto hypervolume and spectral analysis as estimators of short term multi-omic (transcriptomic and metabolic) evolution, thus enabling comparative analysis of metabolic conditions. We therefore compare, evaluate and cluster different experimental conditions, models and bacterial strains according to their metabolic response in a multidimensional objective space, rather than in the original space of microarray data. We finally validate our methods on a phenomics dataset of growth conditions. Our framework, named METRADE, is freely available as a MATLAB toolbox.

  6. Risk-Predicting Model for Incident of Essential Hypertension Based on Environmental and Genetic Factors with Support Vector Machine.

    Science.gov (United States)

    Pei, Zhiyong; Liu, Jielin; Liu, Manjiao; Zhou, Wenchao; Yan, Pengcheng; Wen, Shaojun; Chen, Yubao

    2018-03-01

    Essential hypertension (EH) has become a major chronic disease around the world. To build a risk-predicting model for EH can help to interpose people's lifestyle and dietary habit to decrease the risk of getting EH. In this study, we constructed a EH risk-predicting model considering both environmental and genetic factors with support vector machine (SVM). The data were collected through Epidemiological investigation questionnaire from Beijing Chinese Han population. After data cleaning, we finally selected 9 environmental factors and 12 genetic factors to construct the predicting model based on 1200 samples, including 559 essential hypertension patients and 641 controls. Using radial basis kernel function, predictive accuracy via SVM with function with only environmental factor and only genetic factor were 72.8 and 54.4%, respectively; after considering both environmental and genetic factor the accuracy improved to 76.3%. Using the model via SVM with Laplacian function, the accuracy with only environmental factor and only genetic factor were 76.9 and 57.7%, respectively; after combining environmental and genetic factor, the accuracy improved to 80.1%. The predictive accuracy of SVM model constructed based on Laplacian function was higher than radial basis kernel function, as well as sensitivity and specificity, which were 63.3 and 86.7%, respectively. In conclusion, the model based on SVM with Laplacian kernel function had better performance in predicting risk of hypertension. And SVM model considering both environmental and genetic factors had better performance than the model with environmental or genetic factors only.

  7. Biological and environmental surface interactions of nanomaterials: characterization, modeling, and prediction.

    Science.gov (United States)

    Chen, Ran; Riviere, Jim E

    2017-05-01

    The understanding of nano-bio interactions is deemed essential in the design, application, and safe handling of nanomaterials. Proper characterization of the intrinsic physicochemical properties, including their size, surface charge, shape, and functionalization, is needed to consider the fate or impact of nanomaterials in biological and environmental systems. The characterizations of their interactions with surrounding chemical species are often hindered by the complexity of biological or environmental systems, and the drastically different surface physicochemical properties among a large population of nanomaterials. The complexity of these interactions is also due to the diverse ligands of different chemical properties present in most biomacromolecules, and multiple conformations they can assume at different conditions to minimize their conformational free energy. Often these interactions are collectively determined by multiple physical or chemical forces, including electrostatic forces, hydrogen bonding, and hydrophobic forces, and calls for multidimensional characterization strategies, both experimentally and computationally. Through these characterizations, the understanding of the roles surface physicochemical properties of nanomaterials and their surface interactions with biomacromolecules can play in their applications in biomedical and environmental fields can be obtained. To quantitatively decipher these physicochemical surface interactions, computational methods, including physical, statistical, and pharmacokinetic models, can be used for either analyses of large amounts of experimental characterization data, or theoretical prediction of the interactions, and consequent biological behavior in the body after administration. These computational methods include molecular dynamics simulation, structure-activity relationship models such as biological surface adsorption index, and physiologically-based pharmacokinetic models. WIREs Nanomed Nanobiotechnol 2017

  8. Qualitative and quantitative guidelines for the comparison of environmental model predictions

    International Nuclear Information System (INIS)

    Scott, M.

    1995-03-01

    The question of how to assess or compare predictions from a number of models is one of concern in the validation of models, in understanding the effects of different models and model parameterizations on model output, and ultimately in assessing model reliability. Comparison of model predictions with observed data is the basic tool of model validation while comparison of predictions amongst different models provides one measure of model credibility. The guidance provided here is intended to provide qualitative and quantitative approaches (including graphical and statistical techniques) to such comparisons for use within the BIOMOVS II project. It is hoped that others may find it useful. It contains little technical information on the actual methods but several references are provided for the interested reader. The guidelines are illustrated on data from the VAMP CB scenario. Unfortunately, these data do not permit all of the possible approaches to be demonstrated since predicted uncertainties were not provided. The questions considered are concerned with a) intercomparison of model predictions and b) comparison of model predictions with the observed data. A series of examples illustrating some of the different types of data structure and some possible analyses have been constructed. A bibliography of references on model validation is provided. It is important to note that the results of the various techniques discussed here, whether qualitative or quantitative, should not be considered in isolation. Overall model performance must also include an evaluation of model structure and formulation, i.e. conceptual model uncertainties, and results for performance measures must be interpreted in this context. Consider a number of models which are used to provide predictions of a number of quantities at a number of time points. In the case of the VAMP CB scenario, the results include predictions of total deposition of Cs-137 and time dependent concentrations in various

  9. Cultural Resource Predictive Modeling

    Science.gov (United States)

    2017-10-01

    refining formal, inductive predictive models is the quality of the archaeological and environmental data. To build models efficiently, relevant...geomorphology, and historic information . Lessons Learned: The original model was focused on the identification of prehistoric resources. This...system but uses predictive modeling informally . For example, there is no probability for buried archaeological deposits on the Burton Mesa, but there is

  10. Indoor environmental quality (IEQ) and building energy optimization through model predictive control (MPC)

    Science.gov (United States)

    Woldekidan, Korbaga

    This dissertation aims at developing a novel and systematic approach to apply Model Predictive Control (MPC) to improve energy efficiency and indoor environmental quality in office buildings. Model predictive control is one of the advanced optimal control approaches that use models to predict the behavior of the process beyond the current time to optimize the system operation at the present time. In building system, MPC helps to exploit buildings' thermal storage capacity and to use the information on future disturbances like weather and internal heat gains to estimate optimal control inputs ahead of time. In this research the major challenges of applying MPC to building systems are addressed. A systematic framework has been developed for ease of implementation. New methods are proposed to develop simple and yet reasonably accurate models that can minimize the MPC development effort as well as computational time. The developed MPC is used to control a detailed building model represented by whole building performance simulation tool, EnergyPlus. A co-simulation strategy is used to communicate the MPC control developed in Matlab platform with the case building model in EnergyPlus. The co-simulation tool used (MLE+) also has the ability to talk to actual building management systems that support the BACnet communication protocol which makes it easy to implement the developed MPC control in actual buildings. A building that features an integrated lighting and window control and HVAC system with a dedicated outdoor air system and ceiling radiant panels was used as a case building. Though this study is specifically focused on the case building, the framework developed can be applied to any building type. The performance of the developed MPC was compared against a baseline control strategy using Proportional Integral and Derivative (PID) control. Various conventional and advanced thermal comfort as well as ventilation strategies were considered for the comparison. These

  11. Development and Application of In Vitro Models for Screening Drugs and Environmental Chemicals that Predict Toxicity in Animals and Humans

    Science.gov (United States)

    Development and Application of In Vitro Models for Screening Drugs and Environmental Chemicals that Predict Toxicity in Animals and Humans (Presented by James McKim, Ph.D., DABT, Founder and Chief Science Officer, CeeTox) (5/25/2012)

  12. Predicting Environmental Suitability for a Rare and Threatened Species (Lao Newt, Laotriton laoensis) Using Validated Species Distribution Models

    Science.gov (United States)

    Chunco, Amanda J.; Phimmachak, Somphouthone; Sivongxay, Niane; Stuart, Bryan L.

    2013-01-01

    The Lao newt (Laotriton laoensis) is a recently described species currently known only from northern Laos. Little is known about the species, but it is threatened as a result of overharvesting. We integrated field survey results with climate and altitude data to predict the geographic distribution of this species using the niche modeling program Maxent, and we validated these predictions by using interviews with local residents to confirm model predictions of presence and absence. The results of the validated Maxent models were then used to characterize the environmental conditions of areas predicted suitable for L. laoensis. Finally, we overlaid the resulting model with a map of current national protected areas in Laos to determine whether or not any land predicted to be suitable for this species is coincident with a national protected area. We found that both area under the curve (AUC) values and interview data provided strong support for the predictive power of these models, and we suggest that interview data could be used more widely in species distribution niche modeling. Our results further indicated that this species is mostly likely geographically restricted to high altitude regions (i.e., over 1,000 m elevation) in northern Laos and that only a minute fraction of suitable habitat is currently protected. This work thus emphasizes that increased protection efforts, including listing this species as endangered and the establishment of protected areas in the region predicted to be suitable for L. laoensis, are urgently needed. PMID:23555808

  13. Predicting environmental suitability for a rare and threatened species (Lao newt, Laotriton laoensis using validated species distribution models.

    Directory of Open Access Journals (Sweden)

    Amanda J Chunco

    Full Text Available The Lao newt (Laotriton laoensis is a recently described species currently known only from northern Laos. Little is known about the species, but it is threatened as a result of overharvesting. We integrated field survey results with climate and altitude data to predict the geographic distribution of this species using the niche modeling program Maxent, and we validated these predictions by using interviews with local residents to confirm model predictions of presence and absence. The results of the validated Maxent models were then used to characterize the environmental conditions of areas predicted suitable for L. laoensis. Finally, we overlaid the resulting model with a map of current national protected areas in Laos to determine whether or not any land predicted to be suitable for this species is coincident with a national protected area. We found that both area under the curve (AUC values and interview data provided strong support for the predictive power of these models, and we suggest that interview data could be used more widely in species distribution niche modeling. Our results further indicated that this species is mostly likely geographically restricted to high altitude regions (i.e., over 1,000 m elevation in northern Laos and that only a minute fraction of suitable habitat is currently protected. This work thus emphasizes that increased protection efforts, including listing this species as endangered and the establishment of protected areas in the region predicted to be suitable for L. laoensis, are urgently needed.

  14. Predicting the resilience and recovery of aquatic systems: A framework for model evolution within environmental observatories

    Science.gov (United States)

    Hipsey, Matthew R.; Hamilton, David P.; Hanson, Paul C.; Carey, Cayelan C.; Coletti, Janaine Z.; Read, Jordan S.; Ibelings, Bas W.; Valesini, Fiona J.; Brookes, Justin D.

    2015-09-01

    Maintaining the health of aquatic systems is an essential component of sustainable catchment management, however, degradation of water quality and aquatic habitat continues to challenge scientists and policy-makers. To support management and restoration efforts aquatic system models are required that are able to capture the often complex trajectories that these systems display in response to multiple stressors. This paper explores the abilities and limitations of current model approaches in meeting this challenge, and outlines a strategy based on integration of flexible model libraries and data from observation networks, within a learning framework, as a means to improve the accuracy and scope of model predictions. The framework is comprised of a data assimilation component that utilizes diverse data streams from sensor networks, and a second component whereby model structural evolution can occur once the model is assessed against theoretically relevant metrics of system function. Given the scale and transdisciplinary nature of the prediction challenge, network science initiatives are identified as a means to develop and integrate diverse model libraries and workflows, and to obtain consensus on diagnostic approaches to model assessment that can guide model adaptation. We outline how such a framework can help us explore the theory of how aquatic systems respond to change by bridging bottom-up and top-down lines of enquiry, and, in doing so, also advance the role of prediction in aquatic ecosystem management.

  15. Predicting the resilience and recovery of aquatic systems: a framework for model evolution within environmental observatories

    Science.gov (United States)

    Hipsey, Matthew R.; Hamilton, David P.; Hanson, Paul C.; Carey, Cayelan C.; Coletti, Janaine Z; Read, Jordan S.; Ibelings, Bas W; Valensini, Fiona J; Brookes, Justin D

    2015-01-01

    Maintaining the health of aquatic systems is an essential component of sustainable catchmentmanagement, however, degradation of water quality and aquatic habitat continues to challenge scientistsand policy-makers. To support management and restoration efforts aquatic system models are requiredthat are able to capture the often complex trajectories that these systems display in response to multiplestressors. This paper explores the abilities and limitations of current model approaches in meeting this chal-lenge, and outlines a strategy based on integration of flexible model libraries and data from observationnetworks, within a learning framework, as a means to improve the accuracy and scope of model predictions.The framework is comprised of a data assimilation component that utilizes diverse data streams from sensornetworks, and a second component whereby model structural evolution can occur once the model isassessed against theoretically relevant metrics of system function. Given the scale and transdisciplinarynature of the prediction challenge, network science initiatives are identified as a means to develop and inte-grate diverse model libraries and workflows, and to obtain consensus on diagnostic approaches to modelassessment that can guide model adaptation. We outline how such a framework can help us explore thetheory of how aquatic systems respond to change by bridging bottom-up and top-down lines of enquiry,and, in doing so, also advance the role of prediction in aquatic ecosystem management.

  16. A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals

    Directory of Open Access Journals (Sweden)

    Huixiao Hong

    2016-03-01

    Full Text Available Endocrine disruptors such as polychlorinated biphenyls (PCBs, diethylstilbestrol (DES and dichlorodiphenyltrichloroethane (DDT are agents that interfere with the endocrine system and cause adverse health effects. Huge public health concern about endocrine disruptors has arisen. One of the mechanisms of endocrine disruption is through binding of endocrine disruptors with the hormone receptors in the target cells. Entrance of endocrine disruptors into target cells is the precondition of endocrine disruption. The binding capability of a chemical with proteins in the blood affects its entrance into the target cells and, thus, is very informative for the assessment of potential endocrine disruption of chemicals. α-fetoprotein is one of the major serum proteins that binds to a variety of chemicals such as estrogens. To better facilitate assessment of endocrine disruption of environmental chemicals, we developed a model for α-fetoprotein binding activity prediction using the novel pattern recognition method (Decision Forest and the molecular descriptors calculated from two-dimensional structures by Mold2 software. The predictive capability of the model has been evaluated through internal validation using 125 training chemicals (average balanced accuracy of 69% and external validations using 22 chemicals (balanced accuracy of 71%. Prediction confidence analysis revealed the model performed much better at high prediction confidence. Our results indicate that the model is useful (when predictions are in high confidence in endocrine disruption risk assessment of environmental chemicals though improvement by increasing number of training chemicals is needed.

  17. Prediction of China's coal production-environmental pollution based on a hybrid genetic algorithm-system dynamics model

    International Nuclear Information System (INIS)

    Yu Shiwei; Wei Yiming

    2012-01-01

    This paper proposes a hybrid model based on genetic algorithm (GA) and system dynamics (SD) for coal production–environmental pollution load in China. GA has been utilized in the optimization of the parameters of the SD model to reduce implementation subjectivity. The chain of “Economic development–coal demand–coal production–environmental pollution load” of China in 2030 was predicted, and scenarios were analyzed. Results show that: (1) GA performs well in optimizing the parameters of the SD model objectively and in simulating the historical data; (2) The demand for coal energy continuously increases, although the coal intensity has actually decreased because of China's persistent economic development. Furthermore, instead of reaching a turning point by 2030, the environmental pollution load continuously increases each year even under the scenario where coal intensity decreased by 20% and investment in pollution abatement increased by 20%; (3) For abating the amount of “three types of wastes”, reducing the coal intensity is more effective than reducing the polluted production per tonne of coal and increasing investment in pollution control. - Highlights: ► We propos a GA-SD model for China's coal production-pollution prediction. ► Genetic algorithm (GA) can objectively and accurately optimize parameters of system dynamics (SD) model. ► Environmental pollution in China is projected to grow in our scenarios by 2030. ► The mechanism of reducing waste production per tonne of coal mining is more effective than others.

  18. Calcareous Bio-Concretions in the Northern Adriatic Sea: Habitat Types, Environmental Factors that Influence Habitat Distributions, and Predictive Modeling.

    Science.gov (United States)

    Falace, Annalisa; Kaleb, Sara; Curiel, Daniele; Miotti, Chiara; Galli, Giovanni; Querin, Stefano; Ballesteros, Enric; Solidoro, Cosimo; Bandelj, Vinko

    2015-01-01

    Habitat classifications provide guidelines for mapping and comparing marine resources across geographic regions. Calcareous bio-concretions and their associated biota have not been exhaustively categorized. Furthermore, for management and conservation purposes, species and habitat mapping is critical. Recently, several developments have occurred in the field of predictive habitat modeling, and multiple methods are available. In this study, we defined the habitats constituting northern Adriatic biogenic reefs and created a predictive habitat distribution model. We used an updated dataset of the epibenthic assemblages to define the habitats, which we verified using the fuzzy k-means (FKM) clustering method. Redundancy analysis was employed to model the relationships between the environmental descriptors and the FKM membership grades. Predictive modelling was carried out to map habitats across the basin. Habitat A (opportunistic macroalgae, encrusting Porifera, bioeroders) characterizes reefs closest to the coastline, which are affected by coastal currents and river inputs. Habitat B is distinguished by massive Porifera, erect Tunicata, and non-calcareous encrusting algae (Peyssonnelia spp.). Habitat C (non-articulated coralline, Polycitor adriaticus) is predicted in deeper areas. The onshore-offshore gradient explains the variability of the assemblages because of the influence of coastal freshwater, which is the main driver of nutrient dynamics. This model supports the interpretation of Habitat A and C as the extremes of a gradient that characterizes the epibenthic assemblages, while Habitat B demonstrates intermediate characteristics. Areas of transition are a natural feature of the marine environment and may include a mixture of habitats and species. The habitats proposed are easy to identify in the field, are related to different environmental features, and may be suitable for application in studies focused on other geographic areas. The habitat model outputs

  19. Calcareous Bio-Concretions in the Northern Adriatic Sea: Habitat Types, Environmental Factors that Influence Habitat Distributions, and Predictive Modeling.

    Directory of Open Access Journals (Sweden)

    Annalisa Falace

    Full Text Available Habitat classifications provide guidelines for mapping and comparing marine resources across geographic regions. Calcareous bio-concretions and their associated biota have not been exhaustively categorized. Furthermore, for management and conservation purposes, species and habitat mapping is critical. Recently, several developments have occurred in the field of predictive habitat modeling, and multiple methods are available. In this study, we defined the habitats constituting northern Adriatic biogenic reefs and created a predictive habitat distribution model. We used an updated dataset of the epibenthic assemblages to define the habitats, which we verified using the fuzzy k-means (FKM clustering method. Redundancy analysis was employed to model the relationships between the environmental descriptors and the FKM membership grades. Predictive modelling was carried out to map habitats across the basin. Habitat A (opportunistic macroalgae, encrusting Porifera, bioeroders characterizes reefs closest to the coastline, which are affected by coastal currents and river inputs. Habitat B is distinguished by massive Porifera, erect Tunicata, and non-calcareous encrusting algae (Peyssonnelia spp.. Habitat C (non-articulated coralline, Polycitor adriaticus is predicted in deeper areas. The onshore-offshore gradient explains the variability of the assemblages because of the influence of coastal freshwater, which is the main driver of nutrient dynamics. This model supports the interpretation of Habitat A and C as the extremes of a gradient that characterizes the epibenthic assemblages, while Habitat B demonstrates intermediate characteristics. Areas of transition are a natural feature of the marine environment and may include a mixture of habitats and species. The habitats proposed are easy to identify in the field, are related to different environmental features, and may be suitable for application in studies focused on other geographic areas. The habitat

  20. Predicted and actual indoor environmental quality: Verification of occupants' behaviour models in residential buildings

    DEFF Research Database (Denmark)

    Andersen, Rune Korsholm; Fabi, Valentina; Corgnati, Stefano P.

    2016-01-01

    Occupants' interactions with the building envelope and building systems can have a large impact on the indoor environment and energy consumption in a building. As a consequence, any realistic forecast of building performance must include realistic models of the occupants' interactions...... with the building controls (windows, thermostats, solar shading etc.). During the last decade, studies about stochastic models of occupants' behaviour in relation to control of the indoor environment have been published. Often the overall aim of these models is to enable more reliable predictions of building...... performance using building energy performance simulations (BEPS). However, the validity of these models has only been sparsely tested. In this paper, stochastic models of occupants' behaviour from literature were tested against measurements in five apartments. In a monitoring campaign, measurements of indoor...

  1. Predicting environmental restoration activities through static simulation

    International Nuclear Information System (INIS)

    Ross, T.L.; King, D.A.; Wilkins, M.L.; Forward, M.F.

    1994-12-01

    This paper discusses a static simulation model that predicts several performance measures of environmental restoration activities over different remedial strategies. Basic model operation consists of manipulating and processing waste streams via selecting and applying remedial technologies according to the strategy. Performance measure prediction is possible for contaminated soil, solid waste, surface water, groundwater, storage tank, and facility sites. Simulations are performed for the U.S. Department of Energy in support of its Programmatic Environmental Impact Statement

  2. An overview of the IAEA Safety Series on procedures for evaluating the reliability of predictions made by environmental transfer models

    International Nuclear Information System (INIS)

    Hoffman, F.W.; Hofer, E.

    1987-10-01

    The International Atomic Energy Agency is preparing a Safety Series publication on practical approaches for evaluating the reliability of the predictions made by environmental radiological assessment models. This publication identifies factors that affect the reliability of these predictions and discusses methods for quantifying uncertainty. Emphasis is placed on understanding the quantity of interest specified by the assessment question and distinguishing between stochastic variability and lack of knowledge about either the true value or the true distribution of values for quantity of interest. Among the many approaches discussed, model testing using independent data sets (model validation) is considered the best method for evaluating the accuracy in model predictions. Analytical and numerical methods for propagating the uncertainties in model parameters are presented and the strengths and weaknesses of model intercomparison exercises are also discussed. It is recognized that subjective judgment is employed throughout the entire modelling process, and quantitative reliability statements must be subjectively obtained when models are applied to different situations from those under which they have been tested. (6 refs.)

  3. A Predictive Model for Time-to-Flowering in the Common Bean Based on QTL and Environmental Variables

    Directory of Open Access Journals (Sweden)

    Mehul S. Bhakta

    2017-12-01

    Full Text Available The common bean is a tropical facultative short-day legume that is now grown in tropical and temperate zones. This observation underscores how domestication and modern breeding can change the adaptive phenology of a species. A key adaptive trait is the optimal timing of the transition from the vegetative to the reproductive stage. This trait is responsive to genetically controlled signal transduction pathways and local climatic cues. A comprehensive characterization of this trait can be started by assessing the quantitative contribution of the genetic and environmental factors, and their interactions. This study aimed to locate significant QTL (G and environmental (E factors controlling time-to-flower in the common bean, and to identify and measure G × E interactions. Phenotypic data were collected from a biparental [Andean × Mesoamerican] recombinant inbred population (F11:14, 188 genotypes grown at five environmentally distinct sites. QTL analysis using a dense linkage map revealed 12 QTL, five of which showed significant interactions with the environment. Dissection of G × E interactions using a linear mixed-effect model revealed that temperature, solar radiation, and photoperiod play major roles in controlling common bean flowering time directly, and indirectly by modifying the effect of certain QTL. The model predicts flowering time across five sites with an adjusted r-square of 0.89 and root-mean square error of 2.52 d. The model provides the means to disentangle the environmental dependencies of complex traits, and presents an opportunity to identify in silico QTL allele combinations that could yield desired phenotypes under different climatic conditions.

  4. Evaluation of environmental impact predictions

    International Nuclear Information System (INIS)

    Cunningham, P.A.; Adams, S.M.; Kumar, K.D.

    1977-01-01

    An analysis and evaluation of the ecological monitoring program at the Surry Nuclear Power Plant showed that predictions of potential environmental impact made in the Final Environmental Statement (FES), which were based on generally accepted ecological principles, were not completely substantiated by environmental monitoring data. The Surry Nuclear Power Plant (Units 1 and 2) was chosen for study because of the facility's relatively continuous operating history and the availability of environmental data adequate for analysis. Preoperational and operational fish monitoring data were used to assess the validity of the FES prediction that fish would congregate in the thermal plume during winter months and would avoid the plume during summer months. Analysis of monitoring data showed that fish catch per unit effort (CPE) was generally high in the thermal plume during winter months; however, the highest fish catches occurred in the plume during the summer. Possible explanations for differences between the FES prediction and results observed in analysis of monitoring data are discussed, and general recommendations are outlined for improving impact assessment predictions

  5. Report: Physics Constrained Stochastic Statistical Models for Extended Range Environmental Prediction

    Science.gov (United States)

    2013-09-30

    picture of ENSO-driven autoregressive models for North Pacific SST variability, providing evidence that intermittent processes, such as variability of...intermittent aspects (i) and (ii) are achieved by developing a simple stochastic parameterization for the unresolved details of synoptic -scale...stochastic parameterization of synoptic scale activity to build a stochastic skeleton model for the MJO; this is the first low order model of the MJO which

  6. Physics Constrained Stochastic-Statistical Models for Extended Range Environmental Prediction

    Science.gov (United States)

    2014-09-30

    incorporated: multiple variables (wind, geopotential height, water vapor , and, as a proxy for convective activity, outgoing longwave radiation); multiple...a possible limitation in the water vapor formulation that will require further attention in the future. Finally, a simple interpretation is given...observational data and climate model data (Stechmann, Majda). 5. Identification of outgoing longwave radiation (OLR) satellite data as a measure of

  7. Methodology for predictive modeling of environmental transport and health effects for waste sites at the Savannah River Plant: Environmental information document

    International Nuclear Information System (INIS)

    Stephensen, D.E.; King, C.M.; Looney, B.B.; Grant, M.W.

    1987-03-01

    This document provides information on the methods used to predict chemical transport and the associated health risk for various postulated closure activities at waste sites. The document was prepared as background documentation for the Department of Energy's proposed Environmental Impact Statement (EIS) on waste management activities for groundwater protection at the Savannah River Plant (SRP). The various mathematical formulations used in the environmental transport analysis, the exposure assessment, and the health risk assessment used in the analysis of all foreseeable scenarios as defined by the National Environmental Policy Act (CFR, 1986) are presented in this document. The scenarios do not necessarily represent actual environmental conditions for every SRP waste site. This document was prepared in support of the National Environmental Policy Act process, but does not by itself satisfy federal or state regulatory requirements. 29 refs., 11 figs

  8. A coupled carbon and plant hydraulic model to predict ecosystem carbon and water flux responses to disturbance and environmental change

    Science.gov (United States)

    Mackay, D. S.; Ewers, B. E.; Roberts, D. E.; McDowell, N. G.; Pendall, E.; Frank, J. M.; Reed, D. E.; Massman, W. J.; Mitra, B.

    2011-12-01

    Changing climate drivers including temperature, humidity, precipitation, and carbon dioxide (CO2) concentrations directly control land surface exchanges of CO2 and water. In a profound way these responses are modulated by disturbances that are driven by or exacerbated by climate change. Predicting these changes is challenging given that the feedbacks between environmental controls, disturbances, and fluxes are complex. Flux data in areas of bark beetle outbreaks in the western U.S.A. show differential declines in carbon and water flux in response to the occlusion of xylem by associated fungi. For example, bark beetle infestation at the GLEES AmeriFlux site manifested in a decline in summer water use efficiency to 60% in the year after peak infestation compared to previous years, and no recovery of carbon uptake following a period of high vapor pressure deficit. This points to complex feedbacks between disturbance and differential ecosystem reaction and relaxation responses. Theory based on plant hydraulics and extending to include links to carbon storage and exhaustion has potential for explaining these dynamics with simple, yet rigorous models. In this spirit we developed a coupled model that combines an existing model of canopy water and carbon flow, TREES [e.g., Loranty et al., 2010], with the Sperry et al., [1998] plant hydraulic model. The new model simultaneously solves carbon uptake and losses along with plant hydraulics, and allows for testing specific hypotheses on feedbacks between xylem dysfunction, stomatal and non-stomatal controls on photosynthesis and carbon allocation, and autotrophic and heterotrophic respiration. These are constrained through gas exchange, root vulnerability to cavitation, sap flux, and eddy covariance data in a novel model complexity-testing framework. Our analysis focuses on an ecosystem gradient spanning sagebrush to subalpine forests. Our modeling results support hypotheses on feedbacks between hydraulic dysfunction and 1) non

  9. Environmental Modeling Center

    Data.gov (United States)

    Federal Laboratory Consortium — The Environmental Modeling Center provides the computational tools to perform geostatistical analysis, to model ground water and atmospheric releases for comparison...

  10. The potential use of Chernobyl fallout data to test and evaluate the predictions of environmental radiological assessment models

    Energy Technology Data Exchange (ETDEWEB)

    Richmond, C.R.; Hoffman, F.O.; Blaylock, B.G.; Eckerman, K.F.; Lesslie, P.A.; Miller, C.W.; Ng, Y.C.; Till, J.E.

    1988-06-01

    The objectives of the Model Validation Committee were to collaborate with US and foreign scientists to collect, manage, and evaluate data for identifying critical research issues and data needs to support an integrated assessment of the Chernobyl nuclear accident; test environmental transport, human dosimetric, and health effects models against measured data to determine their efficacy in guiding decisions on protective actions and in estimating exposures to populations and individuals following a nuclear accident; and apply Chernobyl data to quantifications of key processes governing the environmental transport, fate and effects of radionuclides and other trace substances. 55 refs.

  11. The potential use of Chernobyl fallout data to test and evaluate the predictions of environmental radiological assessment models

    International Nuclear Information System (INIS)

    Richmond, C.R.; Hoffman, F.O.; Blaylock, B.G.; Eckerman, K.F.; Lesslie, P.A.; Miller, C.W.; Ng, Y.C.; Till, J.E.

    1988-06-01

    The objectives of the Model Validation Committee were to collaborate with US and foreign scientists to collect, manage, and evaluate data for identifying critical research issues and data needs to support an integrated assessment of the Chernobyl nuclear accident; test environmental transport, human dosimetric, and health effects models against measured data to determine their efficacy in guiding decisions on protective actions and in estimating exposures to populations and individuals following a nuclear accident; and apply Chernobyl data to quantifications of key processes governing the environmental transport, fate and effects of radionuclides and other trace substances. 55 refs

  12. Model description and evaluation of model performance, scenario S. Multiple pathways assessment of the IAEA/CEC co-ordinated research programme on validation of environmental model predictions (VAMP)

    Energy Technology Data Exchange (ETDEWEB)

    Suolanen, V. [VTT Energy, Espoo (Finland). Nuclear Energy

    1996-12-01

    A modelling approach was used to predict doses from a large area deposition of {sup 137}Cs over southern and central Finland. The assumed deposition profile and quantity were both similar to those resulting from the Chernobyl accident. In the study, doses via terrestrial and aquatic environments have been analyzed. Additionally, the intermediate results of the study, such as concentrations in various foodstuffs and the resulting body burdents, were presented. The contributions of ingestion, inhalation and external doses to the total dose were estimated in the study. The considered deposition scenario formed a modelling exercise in the IAEA coordinated research programme on Validation of Environmental Model Predictions, the VAMP project. (21 refs.).

  13. Model description and evaluation of model performance, scenario S. Multiple pathways assessment of the IAEA/CEC co-ordinated research programme on validation of environmental model predictions (VAMP)

    International Nuclear Information System (INIS)

    Suolanen, V.

    1996-12-01

    A modelling approach was used to predict doses from a large area deposition of 137 Cs over southern and central Finland. The assumed deposition profile and quantity were both similar to those resulting from the Chernobyl accident. In the study, doses via terrestrial and aquatic environments have been analyzed. Additionally, the intermediate results of the study, such as concentrations in various foodstuffs and the resulting body burdents, were presented. The contributions of ingestion, inhalation and external doses to the total dose were estimated in the study. The considered deposition scenario formed a modelling exercise in the IAEA coordinated research programme on Validation of Environmental Model Predictions, the VAMP project. (21 refs.)

  14. Predictive modeling of complications.

    Science.gov (United States)

    Osorio, Joseph A; Scheer, Justin K; Ames, Christopher P

    2016-09-01

    Predictive analytic algorithms are designed to identify patterns in the data that allow for accurate predictions without the need for a hypothesis. Therefore, predictive modeling can provide detailed and patient-specific information that can be readily applied when discussing the risks of surgery with a patient. There are few studies using predictive modeling techniques in the adult spine surgery literature. These types of studies represent the beginning of the use of predictive analytics in spine surgery outcomes. We will discuss the advancements in the field of spine surgery with respect to predictive analytics, the controversies surrounding the technique, and the future directions.

  15. Predicting soil, water and air concentrations of environmental contaminants locally and regionally; multimedia transport and transformation models

    International Nuclear Information System (INIS)

    McKone, T.E.; Daniels, J.I.

    1991-01-01

    Environmental scientists recognize that the environment functions as a complex, interconnected system. A realistic risk-management strategy for many contaminants requires a comprehensive and integrated assessment of local and regional transport and transformation processes. In response to this need, we have developed multimedia models that simulate the movement and transformation of chemicals as they spread through air, water, biota, soils, sediments, surface water and ground water. Each component of the environment is treated as a homogeneous subsystem that can exchange water, nutrients, and chemical contaminants with other adjacent compartments. In this paper, we illustrate the use of multimedia models and measurements as tools for screening the potential risks of contaminants released to air and deposited onto soil and plants. The contaminant list includes the volatile organic compounds (VOCs) tetrachloroethylene (PCE) and trichloroethylene (TCE), the semi-volatile organic compound benzo(a)pyrene, and the radionuclides tritium and uranium-238. We examine how chemical properties effect both the ultimate route and quantity of human and ecosystem contact and identify sensitivities and uncertainties in the model results. We consider the advantages of multimedia models relative to environmental monitoring data. (au)

  16. Testing and application of simple semi-analytical models for soil temperature estimation and prediction in environmental assessments.

    Science.gov (United States)

    Tsiros, Ioannis X; Droulia, Fotoula; Thoma, Eleni; Psiloglou, Basil

    2017-07-29

    The ability of various semi-analytical models to predict soil temperature profiles in an experimental plot during a 16-year monitoring study for soil depths up to 120 cm is evaluated. The models are developed from an analytical model by replacing the steady-state soil temperature with easily obtained hourly and daily average temperature values. Such values include the hourly air temperature, the daily average air temperature, the hourly soil temperature of selected soil depths from three daily observations, the daily average of the soil temperature profile and the hourly soil temperature for the bottom depth. The performance evaluation results show that, in principle, all models exhibit high correlation (R 2 values in the range 0.85-0.97), indicating a very good agreement between measured and predicted values. In addition, error statistics reveal that the best performance in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) is the model based on the daily average of the soil temperature profile with MAE values in the range of 0-0.4°C and RMSE values in the range of 0.1-1.5°C.

  17. Modeling environmental policy

    International Nuclear Information System (INIS)

    Martin, W.E.; McDonald, L.A.

    1997-01-01

    The eight book chapters demonstrate the link between the physical models of the environment and the policy analysis in support of policy making. Each chapter addresses an environmental policy issue using a quantitative modeling approach. The volume addresses three general areas of environmental policy - non-point source pollution in the agricultural sector, pollution generated in the extractive industries, and transboundary pollutants from burning fossil fuels. The book concludes by discussing the modeling efforts and the use of mathematical models in general. Chapters are entitled: modeling environmental policy: an introduction; modeling nonpoint source pollution in an integrated system (agri-ecological); modeling environmental and trade policy linkages: the case of EU and US agriculture; modeling ecosystem constraints in the Clean Water Act: a case study in Clearwater National Forest (subject to discharge from metal mining waste); costs and benefits of coke oven emission controls; modeling equilibria and risk under global environmental constraints (discussing energy and environmental interrelations); relative contribution of the enhanced greenhouse effect on the coastal changes in Louisiana; and the use of mathematical models in policy evaluations: comments. The paper on coke area emission controls has been abstracted separately for the IEA Coal Research CD-ROM

  18. Quantifying the ability of environmental parameters to predict soil texture fractions using regression-tree model with GIS and LIDAR data

    DEFF Research Database (Denmark)

    Greve, Mogens Humlekrog; Bou Kheir, Rania; Greve, Mette Balslev

    2012-01-01

    Soil texture is an important soil characteristic that drives crop production and field management, and is the basis for environmental monitoring (including soil quality and sustainability, hydrological and ecological processes, and climate change simulations). The combination of coarse sand, fine...... the most important parameters that can be used as weighted input data in soil environmental prediction models. Seven primary terrain parameters (elevation, slope gradient, slope aspect, plan curvature, profile curvature, flow direction, flow accumulation) and one compound topographic index (CTI) were...... and flow direction/accumulation did not interfere in the building of the soil texture regression trees and associated relationships. The latter can be extrapolated to other areas sharing similar geo-environmental conditions....

  19. Integration of nitrogen dynamics into the Noah-MP land surface model v1.1 for climate and environmental predictions

    International Nuclear Information System (INIS)

    Cai, X.; Zhang, X.

    2016-01-01

    Climate and terrestrial biosphere models consider nitrogen an important factor in limiting plant carbon uptake, while operational environmental models view nitrogen as the leading pollutant causing eutrophication in water bodies. The community Noah land surface model with multi-parameterization options (Noah-MP) is unique in that it is the next-generation land surface model for the Weather Research and Forecasting meteorological model and for the operational weather/climate models in the National Centers for Environmental Prediction. Here in this study, we add a capability to Noah-MP to simulate nitrogen dynamics by coupling the Fixation and Uptake of Nitrogen (FUN) plant model and the Soil and Water Assessment Tool (SWAT) soil nitrogen dynamics. This model development incorporates FUN's state-of-the-art concept of carbon cost theory and SWAT's strength in representing the impacts of agricultural management on the nitrogen cycle. Parameterizations for direct root and mycorrhizal-associated nitrogen uptake, leaf retranslocation, and symbiotic biological nitrogen fixation are employed from FUN, while parameterizations for nitrogen mineralization, nitrification, immobilization, volatilization, atmospheric deposition, and leaching are based on SWAT. The coupled model is then evaluated at the Kellogg Biological Station – a Long Term Ecological Research site within the US Corn Belt. Results show that the model performs well in capturing the major nitrogen state/flux variables (e.g., soil nitrate and nitrate leaching). Furthermore, the addition of nitrogen dynamics improves the modeling of net primary productivity and evapotranspiration. The model improvement is expected to advance the capability of Noah-MP to simultaneously predict weather and water quality in fully coupled Earth system models.

  20. Uncertainty analysis of environmental models

    International Nuclear Information System (INIS)

    Monte, L.

    1990-01-01

    In the present paper an evaluation of the output uncertainty of an environmental model for assessing the transfer of 137 Cs and 131 I in the human food chain are carried out on the basis of a statistical analysis of data reported by the literature. The uncertainty analysis offers the oppotunity of obtaining some remarkable information about the uncertainty of models predicting the migration of non radioactive substances in the environment mainly in relation to the dry and wet deposition

  1. Archaeological predictive model set.

    Science.gov (United States)

    2015-03-01

    This report is the documentation for Task 7 of the Statewide Archaeological Predictive Model Set. The goal of this project is to : develop a set of statewide predictive models to assist the planning of transportation projects. PennDOT is developing t...

  2. Predicting tick presence by environmental risk mapping

    Directory of Open Access Journals (Sweden)

    Arno eSwart

    2014-11-01

    Full Text Available Public health statistics recorded an increasing trend in the incidence of tick bites and erythema migrans in the Netherlands. We investigated whether the disease incidence could be predicted by a spatially explicit categorization model, based on environmental factors and a training set of tick absence-presence data. Presence and absence of Ixodes ricinus were determined by the blanket-dragging method at numerous sites spread over the Netherlands. The probability of tick presence on a 1 km by 1 km square grid was estimated from the field data using a satellite-based methodology. Expert elicitation was conducted to provide a Bayesian prior per landscape type. We applied a linear model to test a correlation between incidence of erythema migrans consultations by general practitioners in the Netherlands and the estimated probability of tick presence. Ticks were present at 252 distinct sampling coordinates and absent at 425. Tick presence was estimated for 54% percent of the total land cover. Our model has predictive power for tick presence in the Netherlands, tick bite incidence per municipality correlated significantly with the average probability of tick presence per grid. The estimated intercept of the linear model was positive and significant. This indicates that a significant fraction of the tick bite consultations could be attributed to the Ixodes ricinus population outside the resident municipality.

  3. Mathematical models for accurate prediction of atmospheric visibility with particular reference to the seasonal and environmental patterns in Hong Kong.

    Science.gov (United States)

    Mui, K W; Wong, L T; Chung, L Y

    2009-11-01

    Atmospheric visibility impairment has gained increasing concern as it is associated with the existence of a number of aerosols as well as common air pollutants and produces unfavorable conditions for observation, dispersion, and transportation. This study analyzed the atmospheric visibility data measured in urban and suburban Hong Kong (two selected stations) with respect to time-matched mass concentrations of common air pollutants including nitrogen dioxide (NO(2)), nitrogen monoxide (NO), respirable suspended particulates (PM(10)), sulfur dioxide (SO(2)), carbon monoxide (CO), and meteorological parameters including air temperature, relative humidity, and wind speed. No significant difference in atmospheric visibility was reported between the two measurement locations (p > or = 0.6, t test); and good atmospheric visibility was observed more frequently in summer and autumn than in winter and spring (p atmospheric visibility increased with temperature but decreased with the concentrations of SO(2), CO, PM(10), NO, and NO(2). The results showed that atmospheric visibility was season dependent and would have significant correlations with temperature, the mass concentrations of PM(10) and NO(2), and the air pollution index API (correlation coefficients mid R: R mid R: > or = 0.7, p atmospheric visibility were thus proposed. By comparison, the proposed visibility prediction models were more accurate than some existing regional models. In addition to improving visibility prediction accuracy, this study would be useful for understanding the context of low atmospheric visibility, exploring possible remedial measures, and evaluating the impact of air pollution and atmospheric visibility impairment in this region.

  4. Scenario prediction of emerging coastal city using CA modeling under different environmental conditions: a case study of Lingang New City, China.

    Science.gov (United States)

    Feng, Yongjiu; Liu, Yan

    2016-09-01

    The world's coastal regions are experiencing rapid urbanization coupled with increased risk of ecological damage and storm surge related to global climate and sea level rising. This urban development issue is particularly important in China, where many emerging coastal cities are being developed. Lingang New City, southeast of Shanghai, is an excellent example of a coastal city that is increasingly vulnerable to environmental change. Sustainable urban development requires planning that classifies and allocates coastal lands using objective procedures that incorporate changing environmental conditions. In this paper, we applied cellular automata (CA) modeling based on self-adaptive genetic algorithm (SAGA) to predict future scenarios and explore sustainable urban development options for Lingang. The CA model was calibrated using the 2005 initial status, 2015 final status, and a set of spatial variables. We implemented specific ecological and environmental conditions as spatial constraints for the model and predicted four 2030 scenarios: (a) an urban planning-oriented Plan Scenario; (b) an ecosystem protection-oriented Eco Scenario; (c) a storm surge-affected Storm Scenario; and (d) a scenario incorporating both ecosystem protection and the effects of storm surge, called the Ecostorm Scenario. The Plan Scenario has been taken as the baseline, with the Lingang urban area increasing from 45.8 km(2) in 2015 to 66.8 km(2) in 2030, accounting for 23.9 % of the entire study area. The simulated urban land size of the Plan Scenario in 2030 was taken as the target to accommodate the projected population increase in this city, which was then applied in the remaining three development scenarios. We used CA modeling to reallocate the urban cells to other unconstrained areas in response to changing spatial constraints. Our predictions should be helpful not only in assessing and adjusting the urban planning schemes for Lingang but also for evaluating urban planning in coastal

  5. Wind power prediction models

    Science.gov (United States)

    Levy, R.; Mcginness, H.

    1976-01-01

    Investigations were performed to predict the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction was derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave desert. In addition to a model for power prediction over relatively long periods of time, an interim simulation model that produces sample wind speeds is described. The interim model furnishes uncorrelated sample speeds at hourly intervals that reproduce the statistical wind distribution at Goldstone. A stochastic simulation model to provide speed samples representative of both the statistical speed distributions and correlations is also discussed.

  6. Predicting soil, water, and air concentrations of environmental contaminants locally and regionally: Multimedia transport and transformation models

    International Nuclear Information System (INIS)

    McKone, T.E.; Daniels, J.I.

    1991-10-01

    Environmental scientists recognize that the environment functions as a complex, interconnected system. A realistic risk-management strategy for many contaminants requires a comprehensive and integrated assessment of local and regional transport and transformation processes. In response to this need, we have developed multimedia models that simulate the movement and transformation of chemicals as they spread through air, water, biota, soils, sediments, surface water, and ground water. Each component of the environment is treated as a homogeneous subsystem that can exchange water, nutrients, and chemical contaminants with other adjacent compartments. In this paper, we illustrate the use of multimedia models and measurements as tools for screening the potential risks of contaminants released to air and deposited onto soil and plants. The contaminant list includes the volatile organic compounds (VOCs) tetrachloroethylene (PCE) and trichloroethylene (TCE), the semi-volatile organic compound benzo(a)pyrene, and the radionuclides tritium and uranium-238. We examine how chemical properties effect both the ultimate route and quantity of human and ecosystem contact and identify sensitivities and uncertainties in the model results

  7. Zephyr - the prediction models

    DEFF Research Database (Denmark)

    Nielsen, Torben Skov; Madsen, Henrik; Nielsen, Henrik Aalborg

    2001-01-01

    utilities as partners and users. The new models are evaluated for five wind farms in Denmark as well as one wind farm in Spain. It is shown that the predictions based on conditional parametric models are superior to the predictions obatined by state-of-the-art parametric models.......This paper briefly describes new models and methods for predicationg the wind power output from wind farms. The system is being developed in a project which has the research organization Risø and the department of Informatics and Mathematical Modelling (IMM) as the modelling team and all the Danish...

  8. Jensen's Inequality Predicts Effects of Environmental Variation

    Science.gov (United States)

    Jonathan J. Ruel; Matthew P. Ayres

    1999-01-01

    Many biologists now recognize that environmental variance can exert important effects on patterns and processes in nature that are independent of average conditions. Jenson's inequality is a mathematical proof that is seldom mentioned in the ecological literature but which provides a powerful tool for predicting some direct effects of environmental variance in...

  9. Mathematical Model Developed for Environmental Samples: Prediction of GC/MS Dioxin TEQ from XDS-CALUX Bioassay Data

    Science.gov (United States)

    Brown, David J.; Orelien, Jean; Gordon, John D.; Chu, Andrew C.; Chu, Michael D.; Nakamura, Masafumi; Handa, Hiroshi; Kayama, Fujio; Denison, Michael S.; Clark, George C.

    2010-01-01

    Remediation of hazardous waste sites requires efficient and cost-effective methods to assess the extent of contamination by toxic substances including dioxin-like chemicals. Traditionally, dioxin-like contamination has been assessed by gas chromatography/high-resolution mass spectrometry (GC/MS) analysis for specific polychlorinated dibenzo-p-dioxins, dibenzofurans, and biphenyl congeners. Toxic equivalency factors for these congeners are then used to estimate the overall dioxin toxic equivalency (TEQ) of complex mixtures found in samples. The XDS-CALUX bioassay estimates contamination by dioxin-like chemicals in a sample extract by measuring expression of a sensitive reporter gene in genetically engineered cells. The output of the XDS-CALUX assay is a CALUX-TEQ value, calibrated based on TCDD standards. Soil samples taken from a variety of hazardous waste sites were measured using the XDS-CALUX bioassay and GC/MS. TEQ and CALUX-TEQ from these methods were compared, and a mathematical model was developed describing the relationship between these two data sets: log(TEQ) = 0.654 × log(CALUX-TEQ) + 0.058-(log(CALUX-TEQ))2. Applying this equation to these samples showed that predicted and GC/MS measured TEQ values strongly correlate (R2 = 0.876) and that TEQ values predicted from CALUX-TEQ were on average nearly identical to the GC/MS-TEQ. The ability of XDS-CALUX bioassay data to predict GC/MS-derived TEQ data should make this procedure useful in risk assessment and management decisions. PMID:17626436

  10. Inverse and Predictive Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Syracuse, Ellen Marie [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-09-27

    The LANL Seismo-Acoustic team has a strong capability in developing data-driven models that accurately predict a variety of observations. These models range from the simple – one-dimensional models that are constrained by a single dataset and can be used for quick and efficient predictions – to the complex – multidimensional models that are constrained by several types of data and result in more accurate predictions. Team members typically build models of geophysical characteristics of Earth and source distributions at scales of 1 to 1000s of km, the techniques used are applicable for other types of physical characteristics at an even greater range of scales. The following cases provide a snapshot of some of the modeling work done by the Seismo- Acoustic team at LANL.

  11. Prediction of Cytochrome P450 Profiles of Environmental Chemicals with QSAR Models Built from Drug-like Molecules

    Science.gov (United States)

    The human cytochrome P450 (CYP450) enzyme family is involved in the biotransformation of many environmental chemicals. As part of the U.S. Tox21 effort, we profiled the CYP450 activity of ~2800 chemicals predominantly of environmental concern against CYP1A2, CYP2C19, CYP2C9, CYP2...

  12. Transparency of Environmental Computer Models

    NARCIS (Netherlands)

    Vos, de M.G.; Top, J.L.; van Hage, W.R.; Schreiber, A.Th.

    2013-01-01

    Environmental computer models are considered essential tools in supporting environmental decision making, but their main value is that they allow a better understanding of our complex environment. Despite numerous attempts to promote good modelling practice, transparency of current environmental

  13. Environmental assessment model for shallow land disposal of low-level radioactive wastes: interim report. [PRESTO (Prediction of Radiation Effects from Shallow Trench Operations)

    Energy Technology Data Exchange (ETDEWEB)

    Little, C.A.; Fields, D.E.; Emerson, C.J.; Hiromoto, G.

    1981-09-01

    PRESTO (Prediction of Radiation Effects from Shallow Trench Operations) is a computer code developed under US Environmental Protection Agency (EPA) funding to evaluate possible health effects from shallow land burial trenches. The model is intended to be generic and to assess radionuclide transport, ensuing exposure, and health impact to a static local population for a 1000-y period following the end of burial operations. Human exposure scenarios considered by the model include normal releases (including leaching and operational spillage), human intrusion, and site farming or reclamation. Pathways and processes of transit from the trench to an individual or population include: groundwater transport, overland flow, erosion, surface water dilution, resuspension, atmospheric transport, deposition, inhalation, and ingestion of contaminated beef, milk, crops, and water. Both population doses and individual doses are calculated as well as doses to the intruder and farmer.

  14. How well do cognitive and environmental variables predict active commuting?

    Directory of Open Access Journals (Sweden)

    Godin Gaston

    2009-03-01

    Full Text Available Abstract Background In recent years, there has been growing interest in theoretical studies integrating cognitions and environmental variables in the prediction of behaviour related to the obesity epidemic. This is the approach adopted in the present study in reference to the theory of planned behaviour. More precisely, the aim of this study was to determine the contribution of cognitive and environmental variables in the prediction of active commuting to get to and from work or school. Methods A prospective study was carried out with 130 undergraduate and graduate students (93 females; 37 males. Environmental, cognitive and socio-demographic variables were evaluated at baseline by questionnaire. Two weeks later, active commuting (walking/bicycling to get to and from work or school was self-reported by questionnaire. Hierarchical multiple regression analyses were performed to predict intention and behaviour. Results The model predicting behaviour based on cognitive variables explained more variance than the model based on environmental variables (37.4% versus 26.8%; Z = 3.86, p p p Conclusion The results showed that cognitive variables play a more important role than environmental variables in predicting and explaining active commuting. When environmental variables were significant, they were mediated by cognitive variables. Therefore, individual cognitions should remain one of the main focuses of interventions promoting active commuting among undergraduate and graduate students.

  15. Modelling of the transfer of radiocaesium from deposition to lake ecosystems. Report of the VAMP aquatic working group. Part of the IAEA/CEC co-ordinated research programme on the validation of environmental model predictions (VAMP)

    International Nuclear Information System (INIS)

    2000-03-01

    The environmental impact of releases of radionuclides from nuclear installations can be predicted using assessment models. For such assessments information on their reliability must be provided. Ideally models should be developed and tested using actual data on the transfer of the nuclides which are site specific for the environment being modelled. In the past, generic data have often been taken from environmental contamination that resulted from the fallout from the nuclear weapons testing in the 1950s and 1960s or from laboratory experiments. However, it has always been recognized that there may be differences in the physico-chemical form of the radionuclides from these sources as compared to those that could be released from nuclear installations. Furthermore, weapons fallout was spread over time; it did not provide a single pulse which is generally used in testing models that predict time dependence. On the other hand, the Chernobyl accident resulted in a single pulse, which was detected and measured in a variety of environments throughout Europe. The acquisition of these new data sets justified the establishment of an international programme aimed at collating data from different IAEA Member States and at co-ordinating work on new model testing studies. The IAEA established a Co-ordinated Research Programme (CRP) on 'Validation of Environmental Model Predictions' (VAMP). The principal objectives of the VAMP Co-ordinated Research Programme were: (a) To facilitate the validation of assessment models for radionuclide transfer in the terrestrial, aquatic and urban environments. It is envisaged that this will be achieved by acquiring suitable sets of environmental data from the results of the national research and monitoring programmes established following the Chernobyl release. (b) To guide, if necessary, environmental research and monitoring efforts to acquire data for the validation of models used to assess the most significant radiological exposure pathways

  16. Testing a Model of Environmental Risk and Protective Factors to Predict Middle and High School Students' Academic Success

    Science.gov (United States)

    Peters, S. Colby; Woolley, Michael E.

    2015-01-01

    Data from the School Success Profile generated by 19,228 middle and high school students were organized into three broad categories of risk and protective factors--control, support, and challenge--to examine the relative and combined power of aggregate scale scores in each category so as to predict academic success. It was hypothesized that higher…

  17. The estimation of soil parameters using observations on crop biophysical variables and the crop model STICS improve the predictions of agro environmental variables.

    Science.gov (United States)

    Varella, H.-V.

    2009-04-01

    Dynamic crop models are very useful to predict the behavior of crops in their environment and are widely used in a lot of agro-environmental work. These models have many parameters and their spatial application require a good knowledge of these parameters, especially of the soil parameters. These parameters can be estimated from soil analysis at different points but this is very costly and requires a lot of experimental work. Nevertheless, observations on crops provided by new techniques like remote sensing or yield monitoring, is a possibility for estimating soil parameters through the inversion of crop models. In this work, the STICS crop model is studied for the wheat and the sugar beet and it includes more than 200 parameters. After a previous work based on a large experimental database for calibrate parameters related to the characteristics of the crop, a global sensitivity analysis of the observed variables (leaf area index LAI and absorbed nitrogen QN provided by remote sensing data, and yield at harvest provided by yield monitoring) to the soil parameters is made, in order to determine which of them have to be estimated. This study was made in different climatic and agronomic conditions and it reveals that 7 soil parameters (4 related to the water and 3 related to the nitrogen) have a clearly influence on the variance of the observed variables and have to be therefore estimated. For estimating these 7 soil parameters, a Bayesian data assimilation method is chosen (because of available prior information on these parameters) named Importance Sampling by using observations, on wheat and sugar beet crop, of LAI and QN at various dates and yield at harvest acquired on different climatic and agronomic conditions. The quality of parameter estimation is then determined by comparing the result of parameter estimation with only prior information and the result with the posterior information provided by the Bayesian data assimilation method. The result of the

  18. Uncertainty quantification for environmental models

    Science.gov (United States)

    Hill, Mary C.; Lu, Dan; Kavetski, Dmitri; Clark, Martyn P.; Ye, Ming

    2012-01-01

    Environmental models are used to evaluate the fate of fertilizers in agricultural settings (including soil denitrification), the degradation of hydrocarbons at spill sites, and water supply for people and ecosystems in small to large basins and cities—to mention but a few applications of these models. They also play a role in understanding and diagnosing potential environmental impacts of global climate change. The models are typically mildly to extremely nonlinear. The persistent demand for enhanced dynamics and resolution to improve model realism [17] means that lengthy individual model execution times will remain common, notwithstanding continued enhancements in computer power. In addition, high-dimensional parameter spaces are often defined, which increases the number of model runs required to quantify uncertainty [2]. Some environmental modeling projects have access to extensive funding and computational resources; many do not. The many recent studies of uncertainty quantification in environmental model predictions have focused on uncertainties related to data error and sparsity of data, expert judgment expressed mathematically through prior information, poorly known parameter values, and model structure (see, for example, [1,7,9,10,13,18]). Approaches for quantifying uncertainty include frequentist (potentially with prior information [7,9]), Bayesian [13,18,19], and likelihood-based. A few of the numerous methods, including some sensitivity and inverse methods with consequences for understanding and quantifying uncertainty, are as follows: Bayesian hierarchical modeling and Bayesian model averaging; single-objective optimization with error-based weighting [7] and multi-objective optimization [3]; methods based on local derivatives [2,7,10]; screening methods like OAT (one at a time) and the method of Morris [14]; FAST (Fourier amplitude sensitivity testing) [14]; the Sobol' method [14]; randomized maximum likelihood [10]; Markov chain Monte Carlo (MCMC) [10

  19. National Centers for Environmental Prediction (NCEP) Regional Ocean Forecast System (ROFS) model output from 1997-01-01 to 2007-09-05

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Regional Ocean Forecast System (ROFS) was developed jointly by the Ocean Modeling Branch of the National Weather Service's Environmental Modeling Center, the...

  20. A landscape model for predicting potential natural vegetation of the Olympic Peninsula USA using boundary equations and newly developed environmental variables.

    Science.gov (United States)

    Jan A. Henderson; Robin D. Lesher; David H. Peter; Chris D. Ringo

    2011-01-01

    A gradient-analysis-based model and grid-based map are presented that use the potential vegetation zone as the object of the model. Several new variables are presented that describe the environmental gradients of the landscape at different scales. Boundary algorithms are conceptualized, and then defined, that describe the environmental boundaries between vegetation...

  1. Predicting on-site environmental impacts of municipal engineering works

    International Nuclear Information System (INIS)

    Gangolells, Marta; Casals, Miquel; Forcada, Núria; Macarulla, Marcel

    2014-01-01

    The research findings fill a gap in the body of knowledge by presenting an effective way to evaluate the significance of on-site environmental impacts of municipal engineering works prior to the construction stage. First, 42 on-site environmental impacts of municipal engineering works were identified by means of a process-oriented approach. Then, 46 indicators and their corresponding significance limits were determined on the basis of a statistical analysis of 25 new-build and remodelling municipal engineering projects. In order to ensure the objectivity of the assessment process, direct and indirect indicators were always based on quantitative data from the municipal engineering project documents. Finally, two case studies were analysed and found to illustrate the practical use of the proposed model. The model highlights the significant environmental impacts of a particular municipal engineering project prior to the construction stage. Consequently, preventive actions can be planned and implemented during on-site activities. The results of the model also allow a comparison of proposed municipal engineering projects and alternatives with respect to the overall on-site environmental impact and the absolute importance of a particular environmental aspect. These findings are useful within the framework of the environmental impact assessment process, as they help to improve the identification and evaluation of on-site environmental aspects of municipal engineering works. The findings may also be of use to construction companies that are willing to implement an environmental management system or simply wish to improve on-site environmental performance in municipal engineering projects. -- Highlights: • We present a model to predict the environmental impacts of municipal engineering works. • It highlights significant on-site environmental impacts prior to the construction stage. • Findings are useful within the environmental impact assessment process. • They also

  2. Predictive Surface Complexation Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Sverjensky, Dimitri A. [Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Earth and Planetary Sciences

    2016-11-29

    Surface complexation plays an important role in the equilibria and kinetics of processes controlling the compositions of soilwaters and groundwaters, the fate of contaminants in groundwaters, and the subsurface storage of CO2 and nuclear waste. Over the last several decades, many dozens of individual experimental studies have addressed aspects of surface complexation that have contributed to an increased understanding of its role in natural systems. However, there has been no previous attempt to develop a model of surface complexation that can be used to link all the experimental studies in order to place them on a predictive basis. Overall, my research has successfully integrated the results of the work of many experimentalists published over several decades. For the first time in studies of the geochemistry of the mineral-water interface, a practical predictive capability for modeling has become available. The predictive correlations developed in my research now enable extrapolations of experimental studies to provide estimates of surface chemistry for systems not yet studied experimentally and for natural and anthropogenically perturbed systems.

  3. Environmental Light and Its Relationship with Electromagnetic Resonances of Biomolecular Interactions, as Predicted by the Resonant Recognition Model

    Directory of Open Access Journals (Sweden)

    Irena Cosic

    2016-06-01

    Full Text Available The meaning and influence of light to biomolecular interactions, and consequently to health, has been analyzed using the Resonant Recognition Model (RRM. The RRM proposes that biological processes/interactions are based on electromagnetic resonances between interacting biomolecules at specific electromagnetic frequencies within the infra-red, visible and ultra-violet frequency ranges, where each interaction can be identified by the certain frequency critical for resonant activation of specific biological activities of proteins and DNA. We found that: (1 the various biological interactions could be grouped according to their resonant frequency into super families of these functions, enabling simpler analyses of these interactions and consequently analyses of influence of electromagnetic frequencies to health; (2 the RRM spectrum of all analyzed biological functions/interactions is the same as the spectrum of the sun light on the Earth, which is in accordance with fact that life is sustained by the sun light; (3 the water is transparent to RRM frequencies, enabling proteins and DNA to interact without loss of energy; (4 the spectrum of some artificial sources of light, as opposed to the sun light, do not cover the whole RRM spectrum, causing concerns for disturbance to some biological functions and consequently we speculate that it can influence health.

  4. Environmental Measurements and Modeling

    Science.gov (United States)

    Environmental measurement is any data collection activity involving the assessment of chemical, physical, or biological factors in the environment which affect human health. Learn more about these programs and tools that aid in environmental decisions

  5. Candidate Prediction Models and Methods

    DEFF Research Database (Denmark)

    Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik

    2005-01-01

    This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...

  6. Melanoma risk prediction models

    Directory of Open Access Journals (Sweden)

    Nikolić Jelena

    2014-01-01

    Full Text Available Background/Aim. The lack of effective therapy for advanced stages of melanoma emphasizes the importance of preventive measures and screenings of population at risk. Identifying individuals at high risk should allow targeted screenings and follow-up involving those who would benefit most. The aim of this study was to identify most significant factors for melanoma prediction in our population and to create prognostic models for identification and differentiation of individuals at risk. Methods. This case-control study included 697 participants (341 patients and 356 controls that underwent extensive interview and skin examination in order to check risk factors for melanoma. Pairwise univariate statistical comparison was used for the coarse selection of the most significant risk factors. These factors were fed into logistic regression (LR and alternating decision trees (ADT prognostic models that were assessed for their usefulness in identification of patients at risk to develop melanoma. Validation of the LR model was done by Hosmer and Lemeshow test, whereas the ADT was validated by 10-fold cross-validation. The achieved sensitivity, specificity, accuracy and AUC for both models were calculated. The melanoma risk score (MRS based on the outcome of the LR model was presented. Results. The LR model showed that the following risk factors were associated with melanoma: sunbeds (OR = 4.018; 95% CI 1.724- 9.366 for those that sometimes used sunbeds, solar damage of the skin (OR = 8.274; 95% CI 2.661-25.730 for those with severe solar damage, hair color (OR = 3.222; 95% CI 1.984-5.231 for light brown/blond hair, the number of common naevi (over 100 naevi had OR = 3.57; 95% CI 1.427-8.931, the number of dysplastic naevi (from 1 to 10 dysplastic naevi OR was 2.672; 95% CI 1.572-4.540; for more than 10 naevi OR was 6.487; 95%; CI 1.993-21.119, Fitzpatricks phototype and the presence of congenital naevi. Red hair, phototype I and large congenital naevi were

  7. Hybrid approaches to physiologic modeling and prediction

    Science.gov (United States)

    Olengü, Nicholas O.; Reifman, Jaques

    2005-05-01

    This paper explores how the accuracy of a first-principles physiological model can be enhanced by integrating data-driven, "black-box" models with the original model to form a "hybrid" model system. Both linear (autoregressive) and nonlinear (neural network) data-driven techniques are separately combined with a first-principles model to predict human body core temperature. Rectal core temperature data from nine volunteers, subject to four 30/10-minute cycles of moderate exercise/rest regimen in both CONTROL and HUMID environmental conditions, are used to develop and test the approach. The results show significant improvements in prediction accuracy, with average improvements of up to 30% for prediction horizons of 20 minutes. The models developed from one subject's data are also used in the prediction of another subject's core temperature. Initial results for this approach for a 20-minute horizon show no significant improvement over the first-principles model by itself.

  8. Predicting Responses to Contemporary Environmental Change Using Evolutionary Response Architectures.

    Science.gov (United States)

    Bay, Rachael A; Rose, Noah; Barrett, Rowan; Bernatchez, Louis; Ghalambor, Cameron K; Lasky, Jesse R; Brem, Rachel B; Palumbi, Stephen R; Ralph, Peter

    2017-05-01

    Rapid environmental change currently presents a major threat to global biodiversity and ecosystem functions, and understanding impacts on individual populations is critical to creating reliable predictions and mitigation plans. One emerging tool for this goal is high-throughput sequencing technology, which can now be used to scan the genome for signs of environmental selection in any species and any system. This explosion of data provides a powerful new window into the molecular mechanisms of adaptation, and although there has been some success in using genomic data to predict responses to selection in fields such as agriculture, thus far genomic data are rarely integrated into predictive frameworks of future adaptation in natural populations. Here, we review both theoretical and empirical studies of adaptation to rapid environmental change, focusing on areas where genomic data are poised to contribute to our ability to estimate species and population persistence and adaptation. We advocate for the need to study and model evolutionary response architectures, which integrate spatial information, fitness estimates, and plasticity with genetic architecture. Understanding how these factors contribute to adaptive responses is essential in efforts to predict the responses of species and ecosystems to future environmental change.

  9. Predictions by the multimedia environmental fate model SimpleBox compared to field data: Intermedia concentration ratios of two phthalate esters

    NARCIS (Netherlands)

    Struijs J; Peijnenburg WJGM; ECO

    2003-01-01

    The multimedia environmental fate model SimpleBox is applied to compute steady-state concentration ratios with the aim to harmonize environmetal quality objectives of air, water, sediment and soil. In 1995 the Dutch Health Council recommended validation of the model. Several activities were

  10. Characterising performance of environmental models

    NARCIS (Netherlands)

    Bennett, N.D.; Croke, B.F.W.; Guariso, G.; Guillaume, J.H.A.; Hamilton, S.H.; Jakeman, A.J.; Marsili-Libelli, S.; Newham, L.T.H.; Norton, J.; Perrin, C.; Pierce, S.; Robson, B.; Seppelt, R.; Voinov, A.; Fath, B.D.; Andreassian, V.

    2013-01-01

    In order to use environmental models effectively for management and decision-making, it is vital to establish an appropriate level of confidence in their performance. This paper reviews techniques available across various fields for characterising the performance of environmental models with focus

  11. Confidence scores for prediction models

    DEFF Research Database (Denmark)

    Gerds, Thomas Alexander; van de Wiel, MA

    2011-01-01

    In medical statistics, many alternative strategies are available for building a prediction model based on training data. Prediction models are routinely compared by means of their prediction performance in independent validation data. If only one data set is available for training and validation......, then rival strategies can still be compared based on repeated bootstraps of the same data. Often, however, the overall performance of rival strategies is similar and it is thus difficult to decide for one model. Here, we investigate the variability of the prediction models that results when the same...... to distinguish rival prediction models with similar prediction performances. Furthermore, on the subject level a confidence score may provide useful supplementary information for new patients who want to base a medical decision on predicted risk. The ideas are illustrated and discussed using data from cancer...

  12. UK Environmental Prediction - integration and evaluation at the convective scale

    Science.gov (United States)

    Fallmann, Joachim; Lewis, Huw; Castillo, Juan Manuel; Pearson, David; Harris, Chris; Saulter, Andy; Bricheno, Lucy; Blyth, Eleanor

    2016-04-01

    Traditionally, the simulation of regional ocean, wave and atmosphere components of the Earth System have been considered separately, with some information on other components provided by means of boundary or forcing conditions. More recently, the potential value of a more integrated approach, as required for global climate and Earth System prediction, for regional short-term applications has begun to gain increasing research effort. In the UK, this activity is motivated by an understanding that accurate prediction and warning of the impacts of severe weather requires an integrated approach to forecasting. The substantial impacts on individuals, businesses and infrastructure of such events indicate a pressing need to understand better the value that might be delivered through more integrated environmental prediction. To address this need, the Met Office, NERC Centre for Ecology & Hydrology and NERC National Oceanography Centre have begun to develop the foundations of a coupled high resolution probabilistic forecast system for the UK at km-scale. This links together existing model components of the atmosphere, coastal ocean, land surface and hydrology. Our initial focus has been on a 2-year Prototype project to demonstrate the UK coupled prediction concept in research mode. This presentation will provide an update on UK environmental prediction activities. We will present the results from the initial implementation of an atmosphere-land-ocean coupled system, including a new eddy-permitting resolution ocean component, and discuss progress and initial results from further development to integrate wave interactions in this relatively high resolution system. We will discuss future directions and opportunities for collaboration in environmental prediction, and the challenges to realise the potential of integrated regional coupled forecasting for improving predictions and applications.

  13. Modelling environmental dynamics. Advances in goematic solutions

    Energy Technology Data Exchange (ETDEWEB)

    Paegelow, Martin [Toulouse-2 Univ., 31 (France). GEODE UMR 5602 CNRS; Camacho Olmedo, Maria Teresa (eds.) [Granada Univ (Spain). Dpto. de Analisis Geografico Regional y Geografia Fisica

    2008-07-01

    Modelling environmental dynamics is critical to understanding and predicting the evolution of the environment in response to the large number of influences including urbanisation, climate change and deforestation. Simulation and modelling provide support for decision making in environmental management. The first chapter introduces terminology and provides an overview of methodological modelling approaches which may be applied to environmental and complex dynamics. Based on this introduction this book illustrates various models applied to a large variety of themes: deforestation in tropical regions, fire risk, natural reforestation in European mountains, agriculture, biodiversity, urbanism, climate change and land management for decision support, etc. These case studies, provided by a large international spectrum of researchers and presented in a uniform structure, focus particularly on methods and model validation so that this book is not only aimed at researchers and graduates but also at professionals. (orig.)

  14. Model evaluation methodology applicable to environmental assessment models

    International Nuclear Information System (INIS)

    Shaeffer, D.L.

    1979-08-01

    A model evaluation methodology is presented to provide a systematic framework within which the adequacy of environmental assessment models might be examined. The necessity for such a tool is motivated by the widespread use of models for predicting the environmental consequences of various human activities and by the reliance on these model predictions for deciding whether a particular activity requires the deployment of costly control measures. Consequently, the uncertainty associated with prediction must be established for the use of such models. The methodology presented here consists of six major tasks: model examination, algorithm examination, data evaluation, sensitivity analyses, validation studies, and code comparison. This methodology is presented in the form of a flowchart to show the logical interrelatedness of the various tasks. Emphasis has been placed on identifying those parameters which are most important in determining the predictive outputs of a model. Importance has been attached to the process of collecting quality data. A method has been developed for analyzing multiplicative chain models when the input parameters are statistically independent and lognormally distributed. Latin hypercube sampling has been offered as a promising candidate for doing sensitivity analyses. Several different ways of viewing the validity of a model have been presented. Criteria are presented for selecting models for environmental assessment purposes

  15. Uncertainty associated with selected environmental transport models

    International Nuclear Information System (INIS)

    Little, C.A.; Miller, C.W.

    1979-11-01

    A description is given of the capabilities of several models to predict accurately either pollutant concentrations in environmental media or radiological dose to human organs. The models are discussed in three sections: aquatic or surface water transport models, atmospheric transport models, and terrestrial and aquatic food chain models. Using data published primarily by model users, model predictions are compared to observations. This procedure is infeasible for food chain models and, therefore, the uncertainty embodied in the models input parameters, rather than the model output, is estimated. Aquatic transport models are divided into one-dimensional, longitudinal-vertical, and longitudinal-horizontal models. Several conclusions were made about the ability of the Gaussian plume atmospheric dispersion model to predict accurately downwind air concentrations from releases under several sets of conditions. It is concluded that no validation study has been conducted to test the predictions of either aquatic or terrestrial food chain models. Using the aquatic pathway from water to fish to an adult for 137 Cs as an example, a 95% one-tailed confidence limit interval for the predicted exposure is calculated by examining the distributions of the input parameters. Such an interval is found to be 16 times the value of the median exposure. A similar one-tailed limit for the air-grass-cow-milk-thyroid for 131 I and infants was 5.6 times the median dose. Of the three model types discussed in this report,the aquatic transport models appear to do the best job of predicting observed concentrations. However, this conclusion is based on many fewer aquatic validation data than were availaable for atmospheric model validation

  16. Performance Evaluation of FAO Model for Prediction of Yield Production, Soil Water and Solute Balance under Environmental Stresses (Case Study Winter Wheat

    Directory of Open Access Journals (Sweden)

    V. Rezaverdinejad

    2014-11-01

    Full Text Available In this study, the FAO agro-hydrological model was investigated and evaluated to predict of yield production, soil water and solute balance by winter wheat field data under water and salt stresses. For this purpose, a field experimental was conducted with three salinity levels of irrigation water include: S1, S2 and S3 corresponding to 1.4, 4.5 and 9.6 dS/m, respectively, and four irrigation depth levels include: I1, I2, I3 and I4 corresponding to 50, 75, 100 and 125% of crop water requirement, respectively, for two varieties of winter wheat: Roshan and Ghods, with three replications in an experimental farm of Birjand University for 1384-85 period. Based on results, the mean relative error of the model in yield prediction for Roshan and Ghods were obtained 9.2 and 26.1%, respectively. The maximum error of yield prediction in both of the Roshan and Ghods varieties, were obtained for S1I1, S2I1 and S3I1 treatments. The relative error of Roshan yield prediction for S1I1, S2I1 and S3I1 were calculated 20.0, 28.1 and 26.6%, respectively and for Ghods variety were calculated 61, 94.5 and 99.9%, respectively, that indicated a significant over estimate error under higher water stress. The mean relative error of model for all treatments, in prediction of soil water depletion and electrical conductivity of soil saturation extract, were calculated 7.1 and 5.8%, respectively, that indicated proper accuracy of model in prediction of soil water content and soil salinity.

  17. Bootstrap prediction and Bayesian prediction under misspecified models

    OpenAIRE

    Fushiki, Tadayoshi

    2005-01-01

    We consider a statistical prediction problem under misspecified models. In a sense, Bayesian prediction is an optimal prediction method when an assumed model is true. Bootstrap prediction is obtained by applying Breiman's `bagging' method to a plug-in prediction. Bootstrap prediction can be considered to be an approximation to the Bayesian prediction under the assumption that the model is true. However, in applications, there are frequently deviations from the assumed model. In this paper, bo...

  18. Assessing the radiological impact of past nuclear activities and events. Part of the IAEA/CEC co-ordinated research programme on the validation of environmental model predictions (VAMP)

    International Nuclear Information System (INIS)

    1994-07-01

    The report is a compilation of papers presented during the July 1993 Special Plenary Session of the VAMP (Validation of Environmental Model Predictions). The papers are grouped in 4 chapters: Assessment in the vicinity of nuclear weapons test sites (4 papers), Assessment in the vicinity of nuclear weapons production facilities (2 papers), Post-Chernobyl dose assessment studies (4 papers) and Assessment in the vicinity of dumped radioactive waste (1 paper). A separate abstract was prepared for each paper. Refs, figs and tabs

  19. Prediction models in complex terrain

    DEFF Research Database (Denmark)

    Marti, I.; Nielsen, Torben Skov; Madsen, Henrik

    2001-01-01

    The objective of the work is to investigatethe performance of HIRLAM in complex terrain when used as input to energy production forecasting models, and to develop a statistical model to adapt HIRLAM prediction to the wind farm. The features of the terrain, specially the topography, influence...... the performance of HIRLAM in particular with respect to wind predictions. To estimate the performance of the model two spatial resolutions (0,5 Deg. and 0.2 Deg.) and different sets of HIRLAM variables were used to predict wind speed and energy production. The predictions of energy production for the wind farms...... are calculated using on-line measurements of power production as well as HIRLAM predictions as input thus taking advantage of the auto-correlation, which is present in the power production for shorter pediction horizons. Statistical models are used to discribe the relationship between observed energy production...

  20. Combination of equilibrium models and hybrid life cycle-input–output analysis to predict the environmental impacts of energy policy scenarios

    International Nuclear Information System (INIS)

    Igos, Elorri; Rugani, Benedetto; Rege, Sameer; Benetto, Enrico; Drouet, Laurent; Zachary, Daniel S.

    2015-01-01

    Highlights: • The environmental impacts of two energy policy scenarios in Luxembourg are assessed. • Computable General Equilibrium (CGE) and Partial Equilibrium (PE) models are used. • Results from coupling of CGE and PE are integrated in hybrid Life Cycle Assessment. • Impacts due to energy related production and imports are likely to grow over time. • Carbon mitigation policies seem to not substantially decrease the impacts’ trend. - Abstract: Nowadays, many countries adopt an active agenda to mitigate the impact of greenhouse gas emissions by moving towards less polluting energy generation technologies. The environmental costs, directly or indirectly generated to achieve such a challenging objective, remain however largely underexplored. Until now, research has focused either on pure economic approaches such as Computable General Equilibrium (CGE) and partial equilibrium (PE) models, or on (physical) energy supply scenarios. These latter could be used to evaluate the environmental impacts of various energy saving or cleaner technologies via Life Cycle Assessment (LCA) methodology. These modelling efforts have, however, been pursued in isolation, without exploring the possible complementarities and synergies. In this study, we have undertaken a practical combination of these approaches into a common framework: on the one hand, by coupling a CGE with a PE model, and, on the other hand, by linking the outcomes from the coupling with a hybrid input–output−process based life cycle inventory. The methodological framework aimed at assessing the environmental consequences of two energy policy scenarios in Luxembourg between 2010 and 2025. The study highlights the potential of coupling CGE and PE models but also the related methodological difficulties (e.g. small number of available technologies in Luxembourg, intrinsic limitations of the two approaches, etc.). The assessment shows both environmental synergies and trade-offs due to the implementation of

  1. The UKC2 regional coupled environmental prediction system

    Science.gov (United States)

    Lewis, Huw W.; Castillo Sanchez, Juan Manuel; Graham, Jennifer; Saulter, Andrew; Bornemann, Jorge; Arnold, Alex; Fallmann, Joachim; Harris, Chris; Pearson, David; Ramsdale, Steven; Martínez-de la Torre, Alberto; Bricheno, Lucy; Blyth, Eleanor; Bell, Victoria A.; Davies, Helen; Marthews, Toby R.; O'Neill, Clare; Rumbold, Heather; O'Dea, Enda; Brereton, Ashley; Guihou, Karen; Hines, Adrian; Butenschon, Momme; Dadson, Simon J.; Palmer, Tamzin; Holt, Jason; Reynard, Nick; Best, Martin; Edwards, John; Siddorn, John

    2018-01-01

    It is hypothesized that more accurate prediction and warning of natural hazards, such as of the impacts of severe weather mediated through various components of the environment, require a more integrated Earth System approach to forecasting. This hypothesis can be explored using regional coupled prediction systems, in which the known interactions and feedbacks between different physical and biogeochemical components of the environment across sky, sea and land can be simulated. Such systems are becoming increasingly common research tools. This paper describes the development of the UKC2 regional coupled research system, which has been delivered under the UK Environmental Prediction Prototype project. This provides the first implementation of an atmosphere-land-ocean-wave modelling system focussed on the United Kingdom and surrounding seas at km-scale resolution. The UKC2 coupled system incorporates models of the atmosphere (Met Office Unified Model), land surface with river routing (JULES), shelf-sea ocean (NEMO) and ocean waves (WAVEWATCH III). These components are coupled, via OASIS3-MCT libraries, at unprecedentedly high resolution across the UK within a north-western European regional domain. A research framework has been established to explore the representation of feedback processes in coupled and uncoupled modes, providing a new research tool for UK environmental science. This paper documents the technical design and implementation of UKC2, along with the associated evaluation framework. An analysis of new results comparing the output of the coupled UKC2 system with relevant forced control simulations for six contrasting case studies of 5-day duration is presented. Results demonstrate that performance can be achieved with the UKC2 system that is at least comparable to its component control simulations. For some cases, improvements in air temperature, sea surface temperature, wind speed, significant wave height and mean wave period highlight the potential

  2. The UKC2 regional coupled environmental prediction system

    Directory of Open Access Journals (Sweden)

    H. W. Lewis

    2018-01-01

    Full Text Available It is hypothesized that more accurate prediction and warning of natural hazards, such as of the impacts of severe weather mediated through various components of the environment, require a more integrated Earth System approach to forecasting. This hypothesis can be explored using regional coupled prediction systems, in which the known interactions and feedbacks between different physical and biogeochemical components of the environment across sky, sea and land can be simulated. Such systems are becoming increasingly common research tools. This paper describes the development of the UKC2 regional coupled research system, which has been delivered under the UK Environmental Prediction Prototype project. This provides the first implementation of an atmosphere–land–ocean–wave modelling system focussed on the United Kingdom and surrounding seas at km-scale resolution. The UKC2 coupled system incorporates models of the atmosphere (Met Office Unified Model, land surface with river routing (JULES, shelf-sea ocean (NEMO and ocean waves (WAVEWATCH III. These components are coupled, via OASIS3-MCT libraries, at unprecedentedly high resolution across the UK within a north-western European regional domain. A research framework has been established to explore the representation of feedback processes in coupled and uncoupled modes, providing a new research tool for UK environmental science. This paper documents the technical design and implementation of UKC2, along with the associated evaluation framework. An analysis of new results comparing the output of the coupled UKC2 system with relevant forced control simulations for six contrasting case studies of 5-day duration is presented. Results demonstrate that performance can be achieved with the UKC2 system that is at least comparable to its component control simulations. For some cases, improvements in air temperature, sea surface temperature, wind speed, significant wave height and mean wave period

  3. MODEL PREDICTIVE CONTROL FUNDAMENTALS

    African Journals Online (AJOL)

    2012-07-02

    Jul 2, 2012 ... Linear MPC. 1. Uses linear model: ˙x = Ax + Bu. 2. Quadratic cost function: F = xT Qx + uT Ru. 3. Linear constraints: Hx + Gu < 0. 4. Quadratic program. Nonlinear MPC. 1. Nonlinear model: ˙x = f(x, u). 2. Cost function can be nonquadratic: F = (x, u). 3. Nonlinear constraints: h(x, u) < 0. 4. Nonlinear program.

  4. Integrated Environmental Assessment Modelling

    Energy Technology Data Exchange (ETDEWEB)

    Guardanz, R.; Gimeno, B. S.; Bermejo, V.; Elvira, S.; Martin, F.; Palacios, M.; Rodriguez, E.; Donaire, I. [Ciemat, Madrid (Spain)

    2000-07-01

    This report describes the results of the Spanish participation in the project Coupling CORINAIR data to cost-effect emission reduction strategies based on critical threshold. (EU/LIFE97/ENV/FIN/336). The subproject has focused on three tasks. Develop tools to improve knowledge on the spatial and temporal details of emissions of air pollutants in Spain. Exploit existing experimental information on plant response to air pollutants in temperate ecosystem and Integrate these findings in a modelling framework that can asses with more accuracy the impact of air pollutants to temperate ecosystems. The results obtained during the execution of this project have significantly improved the models of the impact of alternative emission control strategies on ecosystems and crops in the Iberian Peninsula. (Author) 375 refs.

  5. Modelling bankruptcy prediction models in Slovak companies

    Directory of Open Access Journals (Sweden)

    Kovacova Maria

    2017-01-01

    Full Text Available An intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression and early artificial intelligence models (e.g. artificial neural networks, there is a trend for transition to machine learning models (support vector machines, bagging, boosting, and random forest to predict bankruptcy one year prior to the event. Comparing the performance of this with unconventional approach with results obtained by discriminant analysis, logistic regression, and neural networks application, it has been found that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. On the other side the prediction accuracy of old and well known bankruptcy prediction models is quiet high. Therefore, we aim to analyse these in some way old models on the dataset of Slovak companies to validate their prediction ability in specific conditions. Furthermore, these models will be modelled according to new trends by calculating the influence of elimination of selected variables on the overall prediction ability of these models.

  6. Melanoma Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  7. Predictive models of moth development

    Science.gov (United States)

    Degree-day models link ambient temperature to insect life-stages, making such models valuable tools in integrated pest management. These models increase management efficacy by predicting pest phenology. In Wisconsin, the top insect pest of cranberry production is the cranberry fruitworm, Acrobasis v...

  8. Predictive Models and Computational Embryology

    Science.gov (United States)

    EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

  9. Modeling interfacial liquid layers on environmental ices

    Directory of Open Access Journals (Sweden)

    M. H. Kuo

    2011-09-01

    Full Text Available Interfacial layers on ice significantly influence air-ice chemical interactions. In solute-containing aqueous systems, a liquid brine may form upon freezing due to the exclusion of impurities from the ice crystal lattice coupled with freezing point depression in the concentrated brine. The brine may be segregated to the air-ice interface where it creates a surface layer, in micropockets, or at grain boundaries or triple junctions.

    We present a model for brines and their associated liquid layers in environmental ice systems that is valid over a wide range of temperatures and solute concentrations. The model is derived from fundamental equlibrium thermodynamics and takes into account nonideal solution behavior in the brine, partitioning of the solute into the ice matrix, and equilibration between the brine and the gas phase for volatile solutes. We find that these phenomena are important to consider when modeling brines in environmental ices, especially at low temperatures. We demonstrate its application for environmentally important volatile and nonvolatile solutes including NaCl, HCl, and HNO3. The model is compared to existing models and experimental data from literature where available. We also identify environmentally relevant regimes where brine is not predicted to exist, but the QLL may significantly impact air-ice chemical interactions. This model can be used to improve the representation of air-ice chemical interactions in polar atmospheric chemistry models.

  10. Methods and techniques for prediction of environmental impact

    International Nuclear Information System (INIS)

    1992-04-01

    Environmental impact assessment (EIA) is the procedure that helps decision makers understand the environmental implications of their decisions. The prediction of environmental effects or impact is an extremely important part of the EIA procedure and improvements in existing capabilities are needed. Considerable attention is paid within environmental impact assessment and in handbooks on EIA to methods for identifying and evaluating environmental impacts. However, little attention is given to the issue distribution of information on impact prediction methods. The quantitative or qualitative methods for the prediction of environmental impacts appear to be the two basic approaches for incorporating environmental concerns into the decision-making process. Depending on the nature of the proposed activity and the environment likely to be affected, a combination of both quantitative and qualitative methods is used. Within environmental impact assessment, the accuracy of methods for the prediction of environmental impacts is of major importance while it provides for sound and well-balanced decision making. Pertinent and effective action to deal with the problems of environmental protection and the rational use of natural resources and sustainable development is only possible given objective methods and techniques for the prediction of environmental impact. Therefore, the Senior Advisers to ECE Governments on Environmental and Water Problems, decided to set up a task force, with the USSR as lead country, on methods and techniques for the prediction of environmental impacts in order to undertake a study to review and analyse existing methodological approaches and to elaborate recommendations to ECE Governments. The work of the task force was completed in 1990 and the resulting report, with all relevant background material, was approved by the Senior Advisers to ECE Governments on Environmental and Water Problems in 1991. The present report reflects the situation, state of

  11. Predicting effects of environmental change on a migratory herbivore

    Science.gov (United States)

    Stillman, R A; Wood, K A; Gilkerson, Whelan; Elkinton, E; Black, J. M.; Ward, David H.; Petrie, M.

    2015-01-01

    for which birds were disturbed. We discuss the consequences of these predictions for Black Brant conservation. A wide range of migratory species responses are expected in response to environmental change. Process-based models are potential tools to predict such responses and understand the mechanisms which underpin them.

  12. Advancing Environmental Prediction Capabilities for the Polar Regions and Beyond during The Year of Polar Prediction

    Science.gov (United States)

    Werner, Kirstin; Goessling, Helge; Hoke, Winfried; Kirchhoff, Katharina; Jung, Thomas

    2017-04-01

    Environmental changes in polar regions open up new opportunities for economic and societal operations such as vessel traffic related to scientific, fishery and tourism activities, and in the case of the Arctic also enhanced resource development. The availability of current and accurate weather and environmental information and forecasts will therefore play an increasingly important role in aiding risk reduction and safety management around the poles. The Year of Polar Prediction (YOPP) has been established by the World Meteorological Organization's World Weather Research Programme as the key activity of the ten-year Polar Prediction Project (PPP; see more on www.polarprediction.net). YOPP is an internationally coordinated initiative to significantly advance our environmental prediction capabilities for the polar regions and beyond, supporting improved weather and climate services. Scheduled to take place from mid-2017 to mid-2019, the YOPP core phase covers an extended period of intensive observing, modelling, prediction, verification, user-engagement and education activities in the Arctic and Antarctic, on a wide range of time scales from hours to seasons. The Year of Polar Prediction will entail periods of enhanced observational and modelling campaigns in both polar regions. With the purpose to close the gaps in the conventional polar observing systems in regions where the observation network is sparse, routine observations will be enhanced during Special Observing Periods for an extended period of time (several weeks) during YOPP. This will allow carrying out subsequent forecasting system experiments aimed at optimizing observing systems in the polar regions and providing insight into the impact of better polar observations on forecast skills in lower latitudes. With various activities and the involvement of a wide range of stakeholders, YOPP will contribute to the knowledge base needed to managing the opportunities and risks that come with polar climate change.

  13. Communication models in environmental health.

    Science.gov (United States)

    Guidotti, Tee L

    2013-01-01

    Communication models common in environmental health are not well represented in the literature on health communication. Risk communication is a systematic approach to conveying essential information about a specific environmental issue and a framework for thinking about community risk and the alternatives for dealing with it. Crisis communication is intended to provide essential information to people facing an emergency in order to mitigate its effects and to enable them to make appropriate decisions, and it is primarily used in emergency management. Corporate communication is intended to achieve a change in attitude or perception of an organization, and its role in environmental health is usually public relations or to rehabilitate a damaged reputation. Environmental health education is a more didactic approach to science education with respect to health and the environment. Social marketing uses conventional marketing methods to achieve a socially desirable purpose but is more heavily used in health promotion generally. Communication models and styles in environmental health are specialized to serve the needs of the field in communicating with the community. They are highly structured and executed in different ways but have in common a relative lack of emphasis on changing personal or lifestyle behavior compared with health promotion and public health in general and a tendency to emphasize content on specific environmental issues and decision frameworks for protecting oneself or the community through collective action.

  14. A predictive model of the effect of environmental factors on the occurrence of otters (Lutra lutra L. in Hungary

    Directory of Open Access Journals (Sweden)

    Ildikó Kemenes

    1995-12-01

    Full Text Available Abstract A survey of the distribution of otters (Lutra lutra L. in Hungary revealed that this species is common in most parts of the country where there appear to be suitable aquatic habitats. However, there were a large number of apparently "good" habitats where no otters were found. On the other hand, in some places where, based on a qualitative assessment, otters should not have been present, we still found signs of them. The only strictly and consistently limiting factor was heavy chemical pollution of the water which could not be assayed during the survey but was analysed based on data provided by the water authorities. These observations led us to employ a quantitative method which takes into account 3 scalable and 5 non-scalable variables of the environment and their relationships which might influence the occurrence of otters. The technique was based on a non-parametric multiple regression method specifically developed for use on PCs. This so called logistic regression model is useful for investigating the relationships between a binary dependent variable and a set of categorical independent variables. We recorded the presence (1 or absence (0 of signs of otters as well as the water depth, steepness of the bank, density of the bank vegetation and the presence or absence of various disturbance factors, such as agricultural use of the water bank, obvious signs of pollution of the water, etc., at 369 sites in Hungary. The three former environmental variables were scaled, whereas the disturbance factors were each assigned a value of either 0 or 1 (0 = absent, 1 = present. The analysis has shown that this method can be used to characterise particular combinations of factors at which otters were most likely to occur and even predictions can be made on the probability of finding otters at particular places with a known combination of these environmental factors. Besides its theoretical importance, this method is a very

  15. Predictions models with neural nets

    Directory of Open Access Journals (Sweden)

    Vladimír Konečný

    2008-01-01

    Full Text Available The contribution is oriented to basic problem trends solution of economic pointers, using neural networks. Problems include choice of the suitable model and consequently configuration of neural nets, choice computational function of neurons and the way prediction learning. The contribution contains two basic models that use structure of multilayer neural nets and way of determination their configuration. It is postulate a simple rule for teaching period of neural net, to get most credible prediction.Experiments are executed with really data evolution of exchange rate Kč/Euro. The main reason of choice this time series is their availability for sufficient long period. In carry out of experiments the both given basic kind of prediction models with most frequent use functions of neurons are verified. Achieve prediction results are presented as in numerical and so in graphical forms.

  16. Underwater noise modelling for environmental impact assessment

    Energy Technology Data Exchange (ETDEWEB)

    Farcas, Adrian [Centre for Environment, Fisheries and Aquaculture Science (Cefas), Pakefield Road, Lowestoft, NR33 0HT (United Kingdom); Thompson, Paul M. [Lighthouse Field Station, Institute of Biological and Environmental Sciences, University of Aberdeen, Cromarty IV11 8YL (United Kingdom); Merchant, Nathan D., E-mail: nathan.merchant@cefas.co.uk [Centre for Environment, Fisheries and Aquaculture Science (Cefas), Pakefield Road, Lowestoft, NR33 0HT (United Kingdom)

    2016-02-15

    Assessment of underwater noise is increasingly required by regulators of development projects in marine and freshwater habitats, and noise pollution can be a constraining factor in the consenting process. Noise levels arising from the proposed activity are modelled and the potential impact on species of interest within the affected area is then evaluated. Although there is considerable uncertainty in the relationship between noise levels and impacts on aquatic species, the science underlying noise modelling is well understood. Nevertheless, many environmental impact assessments (EIAs) do not reflect best practice, and stakeholders and decision makers in the EIA process are often unfamiliar with the concepts and terminology that are integral to interpreting noise exposure predictions. In this paper, we review the process of underwater noise modelling and explore the factors affecting predictions of noise exposure. Finally, we illustrate the consequences of errors and uncertainties in noise modelling, and discuss future research needs to reduce uncertainty in noise assessments.

  17. Alternative Testing Methods for Predicting Health Risk from Environmental Exposures

    Directory of Open Access Journals (Sweden)

    Annamaria Colacci

    2014-08-01

    Full Text Available Alternative methods to animal testing are considered as promising tools to support the prediction of toxicological risks from environmental exposure. Among the alternative testing methods, the cell transformation assay (CTA appears to be one of the most appropriate approaches to predict the carcinogenic properties of single chemicals, complex mixtures and environmental pollutants. The BALB/c 3T3 CTA shows a good degree of concordance with the in vivo rodent carcinogenesis tests. Whole-genome transcriptomic profiling is performed to identify genes that are transcriptionally regulated by different kinds of exposures. Its use in cell models representative of target organs may help in understanding the mode of action and predicting the risk for human health. Aiming at associating the environmental exposure to health-adverse outcomes, we used an integrated approach including the 3T3 CTA and transcriptomics on target cells, in order to evaluate the effects of airborne particulate matter (PM on toxicological complex endpoints. Organic extracts obtained from PM2.5 and PM1 samples were evaluated in the 3T3 CTA in order to identify effects possibly associated with different aerodynamic diameters or airborne chemical components. The effects of the PM2.5 extracts on human health were assessed by using whole-genome 44 K oligo-microarray slides. Statistical analysis by GeneSpring GX identified genes whose expression was modulated in response to the cell treatment. Then, modulated genes were associated with pathways, biological processes and diseases through an extensive biological analysis. Data derived from in vitro methods and omics techniques could be valuable for monitoring the exposure to toxicants, understanding the modes of action via exposure-associated gene expression patterns and to highlight the role of genes in key events related to adversity.

  18. Modeling Environmental Literacy of University Students

    Science.gov (United States)

    Teksoz, Gaye; Sahin, Elvan; Tekkaya-Oztekin, Ceren

    2012-01-01

    The present study proposed an Environmental Literacy Components Model to explain how environmental attitudes, environmental responsibility, environmental concern, and environmental knowledge as well as outdoor activities related to each other. A total of 1,345 university students responded to an environmental literacy survey (Kaplowitz and Levine…

  19. Prediction of Chemical Function: Model Development and Application

    Science.gov (United States)

    The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (...

  20. What do saliency models predict?

    Science.gov (United States)

    Koehler, Kathryn; Guo, Fei; Zhang, Sheng; Eckstein, Miguel P.

    2014-01-01

    Saliency models have been frequently used to predict eye movements made during image viewing without a specified task (free viewing). Use of a single image set to systematically compare free viewing to other tasks has never been performed. We investigated the effect of task differences on the ability of three models of saliency to predict the performance of humans viewing a novel database of 800 natural images. We introduced a novel task where 100 observers made explicit perceptual judgments about the most salient image region. Other groups of observers performed a free viewing task, saliency search task, or cued object search task. Behavior on the popular free viewing task was not best predicted by standard saliency models. Instead, the models most accurately predicted the explicit saliency selections and eye movements made while performing saliency judgments. Observers' fixations varied similarly across images for the saliency and free viewing tasks, suggesting that these two tasks are related. The variability of observers' eye movements was modulated by the task (lowest for the object search task and greatest for the free viewing and saliency search tasks) as well as the clutter content of the images. Eye movement variability in saliency search and free viewing might be also limited by inherent variation of what observers consider salient. Our results contribute to understanding the tasks and behavioral measures for which saliency models are best suited as predictors of human behavior, the relationship across various perceptual tasks, and the factors contributing to observer variability in fixational eye movements. PMID:24618107

  1. Models for predicting fuel consumption in sagebrush-dominated ecosystems

    Science.gov (United States)

    Clinton S. Wright

    2013-01-01

    Fuel consumption predictions are necessary to accurately estimate or model fire effects, including pollutant emissions during wildland fires. Fuel and environmental measurements on a series of operational prescribed fires were used to develop empirical models for predicting fuel consumption in big sagebrush (Artemisia tridentate Nutt.) ecosystems....

  2. Prediction methods environmental-effect reporting

    International Nuclear Information System (INIS)

    Jonker, R.J.; Koester, H.W.

    1987-12-01

    This report provides a survey of prediction methods which can be applied to the calculation of emissions in cuclear-reactor accidents, in the framework of environment-effect reports (dutch m.e.r.) or risk analyses. Also emissions during normal operation are important for m.e.r.. These can be derived from measured emissions of power plants being in operation. Data concerning the latter are reported. The report consists of an introduction into reactor technology, among which a description of some reactor types, the corresponding fuel cycle and dismantling scenarios - a discussion of risk-analyses for nuclear power plants and the physical processes which can play a role during accidents - a discussion of prediction methods to be employed and the expected developments in this area - some background information. (aughor). 145 refs.; 21 figs.; 20 tabs

  3. Predicting vegetation type through physiological and environmental interactions with leaf traits: evergreen and deciduous forests in an earth system modeling framework.

    Science.gov (United States)

    Weng, Ensheng; Farrior, Caroline E; Dybzinski, Ray; Pacala, Stephen W

    2017-06-01

    Earth system models are incorporating plant trait diversity into their land components to better predict vegetation dynamics in a changing climate. However, extant plant trait distributions will not allow extrapolations to novel community assemblages in future climates, which will require a mechanistic understanding of the trade-offs that determine trait diversity. In this study, we show how physiological trade-offs involving leaf mass per unit area (LMA), leaf lifespan, leaf nitrogen, and leaf respiration may explain the distribution patterns of evergreen and deciduous trees in the temperate and boreal zones based on (1) an evolutionary analysis of a simple mathematical model and (2) simulation experiments of an individual-based dynamic vegetation model (i.e., LM3-PPA). The evolutionary analysis shows that these leaf traits set up a trade-off between carbon- and nitrogen-use efficiency at the scale of individual trees and therefore determine competitively dominant leaf strategies. As soil nitrogen availability increases, the dominant leaf strategy switches from one that is high in nitrogen-use efficiency to one that is high in carbon-use efficiency or, equivalently, from high-LMA/long-lived leaves (i.e., evergreen) to low-LMA/short-lived leaves (i.e., deciduous). In a region of intermediate soil nitrogen availability, the dominant leaf strategy may be either deciduous or evergreen depending on the initial conditions of plant trait abundance (i.e., founder controlled) due to feedbacks of leaf traits on soil nitrogen mineralization through litter quality. Simulated successional patterns by LM3-PPA from the leaf physiological trade-offs are consistent with observed successional dynamics of evergreen and deciduous forests at three sites spanning the temperate to boreal zones. © 2016 John Wiley & Sons Ltd.

  4. Vehicle navigation in populated areas using predictive control with environmental uncertainty handling

    Directory of Open Access Journals (Sweden)

    Skrzypczyk Krzysztof

    2017-06-01

    Full Text Available This paper addresses the problem of navigating an autonomous vehicle using environmental dynamics prediction. The usefulness of the Game Against Nature formalism adapted to modelling environmental prediction uncertainty is discussed. The possibility of the control law synthesis on the basis of strategies against Nature is presented. The properties and effectiveness of the approach presented are verified by simulations carried out in MATLAB.

  5. Spatial prediction of Soil Organic Carbon contents in croplands, grasslands and forests using environmental covariates and Generalized Additive Models (Southern Belgium)

    Science.gov (United States)

    Chartin, Caroline; Stevens, Antoine; van Wesemael, Bas

    2015-04-01

    Providing spatially continuous Soil Organic Carbon data (SOC) is needed to support decisions regarding soil management, and inform the political debate with quantified estimates of the status and change of the soil resource. Digital Soil Mapping techniques are based on relations existing between a soil parameter (measured at different locations in space at a defined period) and relevant covariates (spatially continuous data) that are factors controlling soil formation and explaining the spatial variability of the target variable. This study aimed at apply DSM techniques to recent SOC content measurements (2005-2013) in three different landuses, i.e. cropland, grassland, and forest, in the Walloon region (Southern Belgium). For this purpose, SOC databases of two regional Soil Monitoring Networks (CARBOSOL for croplands and grasslands, and IPRFW for forests) were first harmonized, totalising about 1,220 observations. Median values of SOC content for croplands, grasslands, and forests, are respectively of 12.8, 29.0, and 43.1 g C kg-1. Then, a set of spatial layers were prepared with a resolution of 40 meters and with the same grid topology, containing environmental covariates such as, landuses, Digital Elevation Model and its derivatives, soil texture, C factor, carbon inputs by manure, and climate. Here, in addition to the three classical texture classes (clays, silt, and sand), we tested the use of clays + fine silt content (particles agricultural soils and forests was for the first time computed for the Walloon region.

  6. Development of computer program ENMASK for prediction of residual environmental masking-noise spectra, from any three independent environmental parameters

    Energy Technology Data Exchange (ETDEWEB)

    Chang, Y.-S.; Liebich, R. E.; Chun, K. C.

    2000-03-31

    Residual environmental sound can mask intrusive4 (unwanted) sound. It is a factor that can affect noise impacts and must be considered both in noise-impact studies and in noise-mitigation designs. Models for quantitative prediction of sensation level (audibility) and psychological effects of intrusive noise require an input with 1/3 octave-band spectral resolution of environmental masking noise. However, the majority of published residual environmental masking-noise data are given with either octave-band frequency resolution or only single A-weighted decibel values. A model has been developed that enables estimation of 1/3 octave-band residual environmental masking-noise spectra and relates certain environmental parameters to A-weighted sound level. This model provides a correlation among three environmental conditions: measured residual A-weighted sound-pressure level, proximity to a major roadway, and population density. Cited field-study data were used to compute the most probable 1/3 octave-band sound-pressure spectrum corresponding to any selected one of these three inputs. In turn, such spectra can be used as an input to models for prediction of noise impacts. This paper discusses specific algorithms included in the newly developed computer program ENMASK. In addition, the relative audibility of the environmental masking-noise spectra at different A-weighted sound levels is discussed, which is determined by using the methodology of program ENAUDIBL.

  7. Predicting the risk of toxic blooms of golden alga from cell abundance and environmental covariates

    Science.gov (United States)

    Patino, Reynaldo; VanLandeghem, Matthew M.; Denny, Shawn

    2016-01-01

    Golden alga (Prymnesium parvum) is a toxic haptophyte that has caused considerable ecological damage to marine and inland aquatic ecosystems worldwide. Studies focused primarily on laboratory cultures have indicated that toxicity is poorly correlated with the abundance of golden alga cells. This relationship, however, has not been rigorously evaluated in the field where environmental conditions are much different. The ability to predict toxicity using readily measured environmental variables and golden alga abundance would allow managers rapid assessments of ichthyotoxicity potential without laboratory bioassay confirmation, which requires additional resources to accomplish. To assess the potential utility of these relationships, several a priori models relating lethal levels of golden alga ichthyotoxicity to golden alga abundance and environmental covariates were constructed. Model parameters were estimated using archived data from four river basins in Texas and New Mexico (Colorado, Brazos, Red, Pecos). Model predictive ability was quantified using cross-validation, sensitivity, and specificity, and the relative ranking of environmental covariate models was determined by Akaike Information Criterion values and Akaike weights. Overall, abundance was a generally good predictor of ichthyotoxicity as cross validation of golden alga abundance-only models ranged from ∼ 80% to ∼ 90% (leave-one-out cross-validation). Environmental covariates improved predictions, especially the ability to predict lethally toxic events (i.e., increased sensitivity), and top-ranked environmental covariate models differed among the four basins. These associations may be useful for monitoring as well as understanding the abiotic factors that influence toxicity during blooms.

  8. Predicting effects of environmental change on river inflows to ...

    Science.gov (United States)

    Estuarine river watersheds provide valued ecosystem services to their surrounding communities including drinking water, fish habitat, and regulation of estuarine water quality. However, the provisioning of these services can be affected by changes in the quantity and quality of river water, such as those caused by altered landscapes or shifting temperatures or precipitation. We used the ecohydrology model, VELMA, in the Trask River watershed to simulate the effects of environmental change scenarios on estuarine river inputs to Tillamook Bay (OR) estuary. The Trask River watershed is 453 km2 and contains extensive agriculture, silviculture, urban, and wetland areas. VELMA was parameterized using existing spatial datasets of elevation, soil type, land use, air temperature, precipitation, river flow, and water quality. Simulated land use change scenarios included alterations in the distribution of the nitrogen-fixing tree species Alnus rubra, and comparisons of varying timber harvest plans. Scenarios involving spatial and temporal shifts in air temperature and precipitation trends were also simulated. Our research demonstrates the utility of ecohydrology models such as VELMA to aid in watershed management decision-making. Model outputs of river water flow, temperature, and nutrient concentrations can be used to predict effects on drinking water quality, salmonid populations, and estuarine water quality. This modeling effort is part of a larger framework of

  9. Adolescent environmental tobacco smoke exposure predicts academic achievement test failure.

    Science.gov (United States)

    Collins, Bradley N; Wileyto, E Paul; Murphy, Michael F G; Munafò, Marcus R

    2007-10-01

    Research has linked prenatal tobacco exposure to neurocognitive and behavioral problems that can disrupt learning and school performance in childhood. Less is known about its effects on academic achievement in adolescence when controlling for known confounding factors (e.g., environmental tobacco smoke [ETS]). We hypothesized that prenatal tobacco exposure would decrease the likelihood of passing academic achievement tests taken at 16 and 18 years of age. This study was a longitudinal analysis of birth cohort data including 6,380 pregnant women and offspring from the 1958 National Child Development Study (NCDS). Academic pass/fail performance was measured on British standardized achievement tests ("Ordinary Level" [O-Level] and Advanced Level: [A-Level]). Prenatal tobacco exposure plus controlling variables (ETS, teen offspring smoking and gender, maternal age at pregnancy, maternal smoking before pregnancy, and socioeconomic status) were included in regression models predicting O- and A-Level test failure. Significant predictors of test failure in the O-Level model included exposure to maternal (OR = 0.71, p teen smoking, female gender, and lower SES. Prenatal tobacco exposure did not influence failure. Similar factors emerged in the A-Level model except that male gender contributed to likelihood of failure. Prenatal exposure remained nonsignificant. Our model suggests that adolescent exposure to ETS, not prenatal tobacco exposure, predicted failure on both O- and A-Level achievement tests when controlling for other factors known to influence achievement. Although this study has limitations, results bolster growing evidence of academic-related ETS consequences in adolescence.

  10. Evaluation of uncertainties in selected environmental dispersion models

    International Nuclear Information System (INIS)

    Little, C.A.; Miller, C.W.

    1979-01-01

    Compliance with standards of radiation dose to the general public has necessitated the use of dispersion models to predict radionuclide concentrations in the environment due to releases from nuclear facilities. Because these models are only approximations of reality and because of inherent variations in the input parameters used in these models, their predictions are subject to uncertainty. Quantification of this uncertainty is necessary to assess the adequacy of these models for use in determining compliance with protection standards. This paper characterizes the capabilities of several dispersion models to predict accurately pollutant concentrations in environmental media. Three types of models are discussed: aquatic or surface water transport models, atmospheric transport models, and terrestrial and aquatic food chain models. Using data published primarily by model users, model predictions are compared to observations

  11. Biodiversity in environmental assessment-current practice and tools for prediction

    International Nuclear Information System (INIS)

    Gontier, Mikael; Balfors, Berit; Moertberg, Ulla

    2006-01-01

    Habitat loss and fragmentation are major threats to biodiversity. Environmental impact assessment and strategic environmental assessment are essential instruments used in physical planning to address such problems. Yet there are no well-developed methods for quantifying and predicting impacts of fragmentation on biodiversity. In this study, a literature review was conducted on GIS-based ecological models that have potential as prediction tools for biodiversity assessment. Further, a review of environmental impact statements for road and railway projects from four European countries was performed, to study how impact prediction concerning biodiversity issues was addressed. The results of the study showed the existing gap between research in GIS-based ecological modelling and current practice in biodiversity assessment within environmental assessment

  12. Predictive Models for Carcinogenicity and Mutagenicity ...

    Science.gov (United States)

    Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendous cost (in time, money, animals) of rodent carcinogenicity bioassays. Both mutagenicity and carcinogenicity involve complex, cellular processes that are only partially understood. Advances in technologies and generation of new data will permit a much deeper understanding. In silico methods for predicting mutagenicity and rodent carcinogenicity based on chemical structural features, along with current mutagenicity and carcinogenicity data sets, have performed well for local prediction (i.e., within specific chemical classes), but are less successful for global prediction (i.e., for a broad range of chemicals). The predictivity of in silico methods can be improved by improving the quality of the data base and endpoints used for modelling. In particular, in vitro assays for clastogenicity need to be improved to reduce false positives (relative to rodent carcinogenicity) and to detect compounds that do not interact directly with DNA or have epigenetic activities. New assays emerging to complement or replace some of the standard assays include VitotoxTM, GreenScreenGC, and RadarScreen. The needs of industry and regulators to assess thousands of compounds necessitate the development of high-t

  13. Human Stem Cell-Derived Cardiomyocytes: An Alternative Model to Evaluate Environmental Chemical Cardiac Safety and Development of Predictive Adverse Outcome Pathways

    Science.gov (United States)

    Biomonitoring over the last 14 years has shown human exposure to environmental chemicals has increased ~10-fold (1). In addition, mortality and morbidity related cardiovascular disease continues to be the leading national and global public health issue (2, 3). The association bet...

  14. Environmental model for a capital city

    Directory of Open Access Journals (Sweden)

    Claudia Eugenia Toca Torres

    2013-06-01

    Full Text Available From a review of the various options for modeling a sustainable development in its environmental dimension, this research proposes a model of environmental impact for Bogota, using the Vensim PLE software to model the pollution, the pollution load and soil contamination. The model includes a limited number of endogenous variables, as well as a greater number of exogenous variables. This modeling allows us to anticipate the environmental situation in the capital, in order to support public policies for addressing issues such as economic sanctions and moral regulations on emissions, discharges and waste, environmental measures and environmentally friendly practices

  15. SPEEDI: system for prediction of environmental emergency dose information

    International Nuclear Information System (INIS)

    Chino, Masamichi; Ishikawa, Hirohiko; Kai, Michiaki

    1984-03-01

    In this report a computer code system for prediction of environmental emergency dose information , i.e., SPEEDI for short, is presented. In case of an accidental release of radioactive materials from a nuclear plant, it is very important for an emergency planning to predict the concentration and dose caused by the materials. The SPEEDI code system has been developed for this purpose and it has features to predict by calculation the released nuclides, wind fields, concentrations and dose based on release information, actual weather and topographical data. (author)

  16. Environmental problems indicator under environmental modeling toward sustainable development

    OpenAIRE

    P. Sutthichaimethee; W. Tanoamchard; P. Sawangwong; P Pachana; N. Witit-Anun

    2015-01-01

    This research aims to apply a model to the study and analysis of environmental and natural resource costs created in supply chains of goods and services produced in Thailand, and propose indicators for environmental problem management, caused by goods and services production, based on concepts of sustainable production and consumer behavior. The research showed that the highest environmental cost in terms of Natural Resource Materials was from pipelines and gas distribution, while the lowest ...

  17. Modeling Adaptive and Nonadaptive Responses of Populations to Environmental Change.

    Science.gov (United States)

    Coulson, Tim; Kendall, Bruce E; Barthold, Julia; Plard, Floriane; Schindler, Susanne; Ozgul, Arpat; Gaillard, Jean-Michel

    2017-09-01

    Understanding how the natural world will be impacted by environmental change over the coming decades is one of the most pressing challenges facing humanity. Addressing this challenge is difficult because environmental change can generate both population-level plastic and evolutionary responses, with plastic responses being either adaptive or nonadaptive. We develop an approach that links quantitative genetic theory with data-driven structured models to allow prediction of population responses to environmental change via plasticity and adaptive evolution. After introducing general new theory, we construct a number of example models to demonstrate that evolutionary responses to environmental change over the short-term will be considerably slower than plastic responses and that the rate of adaptive evolution to a new environment depends on whether plastic responses are adaptive or nonadaptive. Parameterization of the models we develop requires information on genetic and phenotypic variation and demography that will not always be available, meaning that simpler models will often be required to predict responses to environmental change. We consequently develop a method to examine whether the full machinery of the evolutionarily explicit models we develop will be needed to predict responses to environmental change or whether simpler nonevolutionary models that are now widely constructed may be sufficient.

  18. Uncertainties in environmental radiological assessment models and their implications

    International Nuclear Information System (INIS)

    Hoffman, F.O.; Miller, C.W.

    1983-01-01

    Environmental radiological assessments rely heavily on the use of mathematical models. The predictions of these models are inherently uncertain because these models are inexact representations of real systems. The major sources of this uncertainty are related to biases in model formulation and parameter estimation. The best approach for estimating the actual extent of over- or underprediction is model validation, a procedure that requires testing over the range of the intended realm of model application. Other approaches discussed are the use of screening procedures, sensitivity and stochastic analyses, and model comparison. The magnitude of uncertainty in model predictions is a function of the questions asked of the model and the specific radionuclides and exposure pathways of dominant importance. Estimates are made of the relative magnitude of uncertainty for situations requiring predictions of individual and collective risks for both chronic and acute releases of radionuclides. It is concluded that models developed as research tools should be distinguished from models developed for assessment applications. Furthermore, increased model complexity does not necessarily guarantee increased accuracy. To improve the realism of assessment modeling, stochastic procedures are recommended that translate uncertain parameter estimates into a distribution of predicted values. These procedures also permit the importance of model parameters to be ranked according to their relative contribution to the overall predicted uncertainty. Although confidence in model predictions can be improved through site-specific parameter estimation and increased model validation, risk factors and internal dosimetry models will probably remain important contributors to the amount of uncertainty that is irreducible

  19. Prediction of Chemical Function: Model Development and ...

    Science.gov (United States)

    The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (HT) screening-level exposures developed under ExpoCast can be combined with HT screening (HTS) bioactivity data for the risk-based prioritization of chemicals for further evaluation. The functional role (e.g. solvent, plasticizer, fragrance) that a chemical performs can drive both the types of products in which it is found and the concentration in which it is present and therefore impacting exposure potential. However, critical chemical use information (including functional role) is lacking for the majority of commercial chemicals for which exposure estimates are needed. A suite of machine-learning based models for classifying chemicals in terms of their likely functional roles in products based on structure were developed. This effort required collection, curation, and harmonization of publically-available data sources of chemical functional use information from government and industry bodies. Physicochemical and structure descriptor data were generated for chemicals with function data. Machine-learning classifier models for function were then built in a cross-validated manner from the descriptor/function data using the method of random forests. The models were applied to: 1) predict chemi

  20. A model to predict the beginning of the pollen season

    DEFF Research Database (Denmark)

    Toldam-Andersen, Torben Bo

    1991-01-01

    In order to predict the beginning of the pollen season, a model comprising the Utah phenoclirnatography Chill Unit (CU) and ASYMCUR-Growing Degree Hour (GDH) submodels were used to predict the first bloom in Alms, Ulttirrs and Berirln. The model relates environmental temperatures to rest completion...... and bud development. As phenologic parameter 14 years of pollen counts were used. The observed datcs for the beginning of the pollen seasons were defined from the pollen counts and compared with the model prediction. The CU and GDH submodels were used as: 1. A fixed day model, using only the GDH model...... for fruit trees are generally applicable, and give a reasonable description of the growth processes of other trees. This type of model can therefore be of value in predicting the start of the pollen season. The predicted dates were generally within 3-5 days of the observed. Finally the possibility of frost...

  1. Toxicity challenges in environmental chemicals: Prediction of human plasma protein binding through quantitative structure-activity relationship (QSAR) models (2016 IVIVE Workshop Proceedings)

    Science.gov (United States)

    Physiologically based pharmacokinetic (PBPK) models bridge the gap between in vitro assays and in vivo effects by accounting for the adsorption, distribution, metabolism, and excretion of xenobiotics, which is especially useful in the assessment of human toxicity. Quantitative st...

  2. National Centers for Environmental Prediction-Department of Energy (NCEP-DOE) Atmospheric Model Intercomparison Project (AMIP)-II Reanalysis (Reanalysis-2)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The NCEP-DOE Atmospheric Model Intercomparison Project (AMIP-II) reanalysis is a follow-on project to the "50-year" (1948-present) NCEP-NCAR Reanalysis Project....

  3. Using integrated environmental modeling to automate a process-based Quantitative Microbial Risk Assessment

    Science.gov (United States)

    Integrated Environmental Modeling (IEM) organizes multidisciplinary knowledge that explains and predicts environmental-system response to stressors. A Quantitative Microbial Risk Assessment (QMRA) is an approach integrating a range of disparate data (fate/transport, exposure, an...

  4. Using Integrated Environmental Modeling to Automate a Process-Based Quantitative Microbial Risk Assessment (presentation)

    Science.gov (United States)

    Integrated Environmental Modeling (IEM) organizes multidisciplinary knowledge that explains and predicts environmental-system response to stressors. A Quantitative Microbial Risk Assessment (QMRA) is an approach integrating a range of disparate data (fate/transport, exposure, and...

  5. Dissolved Organic Matter: a Master Variable for Predicting and Modeling the Effects of Climatic and Environmental Change on Mercury Transport and Reactivity

    Science.gov (United States)

    Aiken, G.

    2013-12-01

    It is known that dissolved organic matter (DOM) exerts strong controls on the transport and reactivity of Hg in aquatic systems. Our research has demonstrated that DOM binds Hg strongly, interacts with nanoparticulate HgS to stabilize and enhance reactivity, and controls, in part, the availability of Hg for methylation by micro-organisms. In many rivers and streams, DOM and dissolved Hg concentrations are strongly positively correlated and DOM optical properties have been shown to be excellent proxies for Hg concentration. Of particular importance is the hydrophobic acid fraction of DOM that contains primarily terrestrially derived aquatic humic substances. This fraction is derived, in large part, from watershed soils and plant litter, is chromophore-rich, and strongly influences DOM optical properties, such as ultraviolet (UV) absorbance, fluorescence, and specific UV absorbance (SUVA - an indicator of DOM aromaticity). In most rivers and streams studied by our group, the relationships between total dissolved Hg concentration and hydrophobic organic acid (HPOA) content are often stronger than those observed between dissolved Hg and DOM. These results and those of lab studies support the hypothesis that interactions between Hg and the HPOA fraction are important drivers for the transport and reactivity of dissolved Hg in aquatic systems. Therefore, understanding how climate or land use related changes may influence DOM and HPOA export and yield within a particular watershed is key to predicting in the fate and bioaccumulation of Hg in that system. Watershed hydrology, the nature of source materials, and biogeochemical processes throughout the entire ecosystem drive DOM composition. In particular, the abundance of wetlands within a river basin is an excellent indicator of DOM concentration, DOM optical properties, and the concentration of HPOA. For instance, for 17 major North American rivers we found significant positive correlations between basin wetland-cover and

  6. Mathematical models for indoor radon prediction

    International Nuclear Information System (INIS)

    Malanca, A.; Pessina, V.; Dallara, G.

    1995-01-01

    It is known that the indoor radon (Rn) concentration can be predicted by means of mathematical models. The simplest model relies on two variables only: the Rn source strength and the air exchange rate. In the Lawrence Berkeley Laboratory (LBL) model several environmental parameters are combined into a complex equation; besides, a correlation between the ventilation rate and the Rn entry rate from the soil is admitted. The measurements were carried out using activated carbon canisters. Seventy-five measurements of Rn concentrations were made inside two rooms placed on the second floor of a building block. One of the rooms had a single-glazed window whereas the other room had a double pane window. During three different experimental protocols, the mean Rn concentration was always higher into the room with a double-glazed window. That behavior can be accounted for by the simplest model. A further set of 450 Rn measurements was collected inside a ground-floor room with a grounding well in it. This trend maybe accounted for by the LBL model

  7. Integrating environmental and genetic effects to predict responses of tree populations to climate.

    Science.gov (United States)

    Wang, Tongli; O'Neill, Gregory A; Aitken, Sally N

    2010-01-01

    Climate is a major environmental factor affecting the phenotype of trees and is also a critical agent of natural selection that has molded among-population genetic variation. Population response functions describe the environmental effect of planting site climates on the performance of a single population, whereas transfer functions describe among-population genetic variation molded by natural selection for climate. Although these approaches are widely used to predict the responses of trees to climate change, both have limitations. We present a novel approach that integrates both genetic and environmental effects into a single "universal response function" (URF) to better predict the influence of climate on phenotypes. Using a large lodgepole pine (Pinus contorta Dougl. ex Loud.) field transplant experiment composed of 140 populations planted on 62 sites to demonstrate the methodology, we show that the URF makes full use of data from provenance trials to: (1) improve predictions of climate change impacts on phenotypes; (2) reduce the size and cost of future provenance trials without compromising predictive power; (3) more fully exploit existing, less comprehensive provenance tests; (4) quantify and compare environmental and genetic effects of climate on population performance; and (5) predict the performance of any population growing in any climate. Finally, we discuss how the last attribute allows the URF to be used as a mechanistic model to predict population and species ranges for the future and to guide assisted migration of seed for reforestation, restoration, or afforestation and genetic conservation in a changing climate.

  8. Iowa calibration of MEPDG performance prediction models.

    Science.gov (United States)

    2013-06-01

    This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement : performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 : representative p...

  9. Model-based uncertainty in species range prediction

    DEFF Research Database (Denmark)

    Pearson, R. G.; Thuiller, Wilfried; Bastos Araujo, Miguel

    2006-01-01

    algorithm when extrapolating beyond the range of data used to build the model. The effects of these factors should be carefully considered when using this modelling approach to predict species ranges. Main conclusions We highlight an important source of uncertainty in assessments of the impacts of climate......Aim Many attempts to predict the potential range of species rely on environmental niche (or 'bioclimate envelope') modelling, yet the effects of using different niche-based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions......, identify key reasons why model output may differ and discuss the implications that model uncertainty has for policy-guiding applications. Location The Western Cape of South Africa. Methods We applied nine of the most widely used modelling techniques to model potential distributions under current...

  10. Environmental Factors Affecting Asthma and Allergies: Predicting and Simulating Downwind Exposure to Airborne Pollen

    Science.gov (United States)

    Luvall, Jeffrey; Estes, Sue; Sprigg, William A.; Nickovic, Slobodan; Huete, Alfredo; Solano, Ramon; Ratana, Piyachat; Jiang, Zhangyan; Flowers, Len; Zelicoff, Alan

    2009-01-01

    This slide presentation reviews the environmental factors that affect asthma and allergies and work to predict and simulate the downwind exposure to airborne pollen. Using a modification of Dust REgional Atmosphere Model (DREAM) that incorporates phenology (i.e. PREAM) the aim was to predict concentrations of pollen in time and space. The strategy for using the model to simulate downwind pollen dispersal, and evaluate the results. Using MODerate-resolution Imaging Spectroradiometer (MODIS), to get seasonal sampling of Juniper, the pollen chosen for the study, land cover on a near daily basis. The results of the model are reviewed.

  11. Consensus models to predict endocrine disruption for all ...

    Science.gov (United States)

    Humans are potentially exposed to tens of thousands of man-made chemicals in the environment. It is well known that some environmental chemicals mimic natural hormones and thus have the potential to be endocrine disruptors. Most of these environmental chemicals have never been tested for their ability to disrupt the endocrine system, in particular, their ability to interact with the estrogen receptor. EPA needs tools to prioritize thousands of chemicals, for instance in the Endocrine Disruptor Screening Program (EDSP). Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) was intended to be a demonstration of the use of predictive computational models on HTS data including ToxCast and Tox21 assays to prioritize a large chemical universe of 32464 unique structures for one specific molecular target – the estrogen receptor. CERAPP combined multiple computational models for prediction of estrogen receptor activity, and used the predicted results to build a unique consensus model. Models were developed in collaboration between 17 groups in the U.S. and Europe and applied to predict the common set of chemicals. Structure-based techniques such as docking and several QSAR modeling approaches were employed, mostly using a common training set of 1677 compounds provided by U.S. EPA, to build a total of 42 classification models and 8 regression models for binding, agonist and antagonist activity. All predictions were evaluated on ToxCast data and on an exte

  12. Model complexity control for hydrologic prediction

    NARCIS (Netherlands)

    Schoups, G.; Van de Giesen, N.C.; Savenije, H.H.G.

    2008-01-01

    A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore

  13. Water Quality, Cyanobacteria, and Environmental Factors and Their Relations to Microcystin Concentrations for Use in Predictive Models at Ohio Lake Erie and Inland Lake Recreational Sites, 2013-14

    Science.gov (United States)

    Francy, Donna S.; Graham, Jennifer L.; Stelzer, Erin A.; Ecker, Christopher D.; Brady, Amie M. G.; Pam Struffolino,; Loftin, Keith A.

    2015-11-06

    Harmful cyanobacterial “algal” blooms (cyanoHABs) and associated toxins, such as microcystin, are a major water-quality issue for Lake Erie and inland lakes in Ohio. Predicting when and where a bloom may occur is important to protect the public that uses and consumes a water resource; however, predictions are complicated and likely site specific because of the many factors affecting toxin production. Monitoring for a variety of environmental and water-quality factors, for concentrations of cyanobacteria by molecular methods, and for algal pigments such as chlorophyll and phycocyanin by using optical sensors may provide data that can be used to predict the occurrence of cyanoHABs.

  14. Using a Prediction Model to Manage Cyber Security Threats.

    Science.gov (United States)

    Jaganathan, Venkatesh; Cherurveettil, Priyesh; Muthu Sivashanmugam, Premapriya

    2015-01-01

    Cyber-attacks are an important issue faced by all organizations. Securing information systems is critical. Organizations should be able to understand the ecosystem and predict attacks. Predicting attacks quantitatively should be part of risk management. The cost impact due to worms, viruses, or other malicious software is significant. This paper proposes a mathematical model to predict the impact of an attack based on significant factors that influence cyber security. This model also considers the environmental information required. It is generalized and can be customized to the needs of the individual organization.

  15. Using a Prediction Model to Manage Cyber Security Threats

    Directory of Open Access Journals (Sweden)

    Venkatesh Jaganathan

    2015-01-01

    Full Text Available Cyber-attacks are an important issue faced by all organizations. Securing information systems is critical. Organizations should be able to understand the ecosystem and predict attacks. Predicting attacks quantitatively should be part of risk management. The cost impact due to worms, viruses, or other malicious software is significant. This paper proposes a mathematical model to predict the impact of an attack based on significant factors that influence cyber security. This model also considers the environmental information required. It is generalized and can be customized to the needs of the individual organization.

  16. Hierarchical modelling for the environmental sciences statistical methods and applications

    CERN Document Server

    Clark, James S

    2006-01-01

    New statistical tools are changing the way in which scientists analyze and interpret data and models. Hierarchical Bayes and Markov Chain Monte Carlo methods for analysis provide a consistent framework for inference and prediction where information is heterogeneous and uncertain, processes are complicated, and responses depend on scale. Nowhere are these methods more promising than in the environmental sciences.

  17. Staying Power of Churn Prediction Models

    NARCIS (Netherlands)

    Risselada, Hans; Verhoef, Peter C.; Bijmolt, Tammo H. A.

    In this paper, we study the staying power of various churn prediction models. Staying power is defined as the predictive performance of a model in a number of periods after the estimation period. We examine two methods, logit models and classification trees, both with and without applying a bagging

  18. Modeling adaptive and non-adaptive responses to environmental change

    DEFF Research Database (Denmark)

    Coulson, Tim; Kendall, Bruce E; Barthold, Julia A.

    2017-01-01

    Understanding how the natural world will be impacted by environmental change over the coming decades is one of the most pressing challenges facing humanity. Addressing this challenge is difficult because environmental change can generate both population level plastic and evolutionary responses...... construct a number of example models to demonstrate that evolutionary responses to environmental change over the short-term will be considerably slower than plastic responses, and that the rate of adaptive evolution to a new environment depends upon whether plastic responses are adaptive or non...... machinery of the evolutionarily explicit models we develop will be needed to predict responses to environmental change, or whether simpler non-evolutionary models that are now widely constructed may be sufficient....

  19. Enhancing Flood Prediction Reliability Using Bayesian Model Averaging

    Science.gov (United States)

    Liu, Z.; Merwade, V.

    2017-12-01

    Uncertainty analysis is an indispensable part of modeling the hydrology and hydrodynamics of non-idealized environmental systems. Compared to reliance on prediction from one model simulation, using on ensemble of predictions that consider uncertainty from different sources is more reliable. In this study, Bayesian model averaging (BMA) is applied to Black River watershed in Arkansas and Missouri by combining multi-model simulations to get reliable deterministic water stage and probabilistic inundation extent predictions. The simulation ensemble is generated from 81 LISFLOOD-FP subgrid model configurations that include uncertainty from channel shape, channel width, channel roughness and discharge. Model simulation outputs are trained with observed water stage data during one flood event, and BMA prediction ability is validated for another flood event. Results from this study indicate that BMA does not always outperform all members in the ensemble, but it provides relatively robust deterministic flood stage predictions across the basin. Station based BMA (BMA_S) water stage prediction has better performance than global based BMA (BMA_G) prediction which is superior to the ensemble mean prediction. Additionally, high-frequency flood inundation extent (probability greater than 60%) in BMA_G probabilistic map is more accurate than the probabilistic flood inundation extent based on equal weights.

  20. Comparison of Prediction-Error-Modelling Criteria

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Jørgensen, Sten Bay

    2007-01-01

    Single and multi-step prediction-error-methods based on the maximum likelihood and least squares criteria are compared. The prediction-error methods studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model, which is a r...

  1. Calibration of PMIS pavement performance prediction models.

    Science.gov (United States)

    2012-02-01

    Improve the accuracy of TxDOTs existing pavement performance prediction models through calibrating these models using actual field data obtained from the Pavement Management Information System (PMIS). : Ensure logical performance superiority patte...

  2. Predictive Model Assessment for Count Data

    National Research Council Canada - National Science Library

    Czado, Claudia; Gneiting, Tilmann; Held, Leonhard

    2007-01-01

    .... In case studies, we critique count regression models for patent data, and assess the predictive performance of Bayesian age-period-cohort models for larynx cancer counts in Germany. Key words: Calibration...

  3. Modeling and Prediction Using Stochastic Differential Equations

    DEFF Research Database (Denmark)

    Juhl, Rune; Møller, Jan Kloppenborg; Jørgensen, John Bagterp

    2016-01-01

    deterministic and can predict the future perfectly. A more realistic approach would be to allow for randomness in the model due to e.g., the model be too simple or errors in input. We describe a modeling and prediction setup which better reflects reality and suggests stochastic differential equations (SDEs......) for modeling and forecasting. It is argued that this gives models and predictions which better reflect reality. The SDE approach also offers a more adequate framework for modeling and a number of efficient tools for model building. A software package (CTSM-R) for SDE-based modeling is briefly described....... that describes the variation between subjects. The ODE setup implies that the variation for a single subject is described by a single parameter (or vector), namely the variance (covariance) of the residuals. Furthermore the prediction of the states is given as the solution to the ODEs and hence assumed...

  4. Modeling nanomaterial environmental fate in aquatic systems.

    Science.gov (United States)

    Dale, Amy L; Casman, Elizabeth A; Lowry, Gregory V; Lead, Jamie R; Viparelli, Enrica; Baalousha, Mohammed

    2015-03-03

    Mathematical models improve our fundamental understanding of the environmental behavior, fate, and transport of engineered nanomaterials (NMs, chemical substances or materials roughly 1-100 nm in size) and facilitate risk assessment and management activities. Although today's large-scale environmental fate models for NMs are a considerable improvement over early efforts, a gap still remains between the experimental research performed to date on the environmental fate of NMs and its incorporation into models. This article provides an introduction to the current state of the science in modeling the fate and behavior of NMs in aquatic environments. We address the strengths and weaknesses of existing fate models, identify the challenges facing researchers in developing and validating these models, and offer a perspective on how these challenges can be addressed through the combined efforts of modelers and experimentalists.

  5. Predictive models for arteriovenous fistula maturation.

    Science.gov (United States)

    Al Shakarchi, Julien; McGrogan, Damian; Van der Veer, Sabine; Sperrin, Matthew; Inston, Nicholas

    2016-05-07

    Haemodialysis (HD) is a lifeline therapy for patients with end-stage renal disease (ESRD). A critical factor in the survival of renal dialysis patients is the surgical creation of vascular access, and international guidelines recommend arteriovenous fistulas (AVF) as the gold standard of vascular access for haemodialysis. Despite this, AVFs have been associated with high failure rates. Although risk factors for AVF failure have been identified, their utility for predicting AVF failure through predictive models remains unclear. The objectives of this review are to systematically and critically assess the methodology and reporting of studies developing prognostic predictive models for AVF outcomes and assess them for suitability in clinical practice. Electronic databases were searched for studies reporting prognostic predictive models for AVF outcomes. Dual review was conducted to identify studies that reported on the development or validation of a model constructed to predict AVF outcome following creation. Data were extracted on study characteristics, risk predictors, statistical methodology, model type, as well as validation process. We included four different studies reporting five different predictive models. Parameters identified that were common to all scoring system were age and cardiovascular disease. This review has found a small number of predictive models in vascular access. The disparity between each study limits the development of a unified predictive model.

  6. Model Predictive Control Fundamentals | Orukpe | Nigerian Journal ...

    African Journals Online (AJOL)

    Model Predictive Control (MPC) has developed considerably over the last two decades, both within the research control community and in industries. MPC strategy involves the optimization of a performance index with respect to some future control sequence, using predictions of the output signal based on a process model, ...

  7. Unreachable Setpoints in Model Predictive Control

    DEFF Research Database (Denmark)

    Rawlings, James B.; Bonné, Dennis; Jørgensen, John Bagterp

    2008-01-01

    In this work, a new model predictive controller is developed that handles unreachable setpoints better than traditional model predictive control methods. The new controller induces an interesting fast/slow asymmetry in the tracking response of the system. Nominal asymptotic stability of the optim...

  8. Models in environmental regulatory decision making

    National Research Council Canada - National Science Library

    Committee on Models in the Regulatory Decision Process; National Research Council; Division on Earth and Life Studies; National Research Council

    2007-01-01

    .... Models help EPA explain environmental phenomena in settings where direct observations are limited or unavailable, and anticipate the effects of agency policies on the environment, human health and the economy...

  9. Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database.

    Science.gov (United States)

    Wessler, Benjamin S; Lai Yh, Lana; Kramer, Whitney; Cangelosi, Michael; Raman, Gowri; Lutz, Jennifer S; Kent, David M

    2015-07-01

    Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease, there are numerous CPMs available although the extent of this literature is not well described. We conducted a systematic review for articles containing CPMs for cardiovascular disease published between January 1990 and May 2012. Cardiovascular disease includes coronary heart disease, heart failure, arrhythmias, stroke, venous thromboembolism, and peripheral vascular disease. We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. Seven hundred seventeen (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions, including 215 CPMs for patients with coronary artery disease, 168 CPMs for population samples, and 79 models for patients with heart failure. There are 77 distinct index/outcome pairings. Of the de novo models in this database, 450 (63%) report a c-statistic and 259 (36%) report some information on calibration. There is an abundance of CPMs available for a wide assortment of cardiovascular disease conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood. © 2015 American Heart Association, Inc.

  10. To predict the niche, model colonization and extinction

    Science.gov (United States)

    Yackulic, Charles B.; Nichols, James D.; Reid, Janice; Der, Ricky

    2015-01-01

    Ecologists frequently try to predict the future geographic distributions of species. Most studies assume that the current distribution of a species reflects its environmental requirements (i.e., the species' niche). However, the current distributions of many species are unlikely to be at equilibrium with the current distribution of environmental conditions, both because of ongoing invasions and because the distribution of suitable environmental conditions is always changing. This mismatch between the equilibrium assumptions inherent in many analyses and the disequilibrium conditions in the real world leads to inaccurate predictions of species' geographic distributions and suggests the need for theory and analytical tools that avoid equilibrium assumptions. Here, we develop a general theory of environmental associations during periods of transient dynamics. We show that time-invariant relationships between environmental conditions and rates of local colonization and extinction can produce substantial temporal variation in occupancy–environment relationships. We then estimate occupancy–environment relationships during three avian invasions. Changes in occupancy–environment relationships over time differ among species but are predicted by dynamic occupancy models. Since estimates of the occupancy–environment relationships themselves are frequently poor predictors of future occupancy patterns, research should increasingly focus on characterizing how rates of local colonization and extinction vary with environmental conditions.

  11. System for prediction of environmental emergency dose information network system

    International Nuclear Information System (INIS)

    Misawa, Makoto; Nagamori, Fumio

    2009-01-01

    In cases when an accident happens to arise with some risk for emission of a large amount radioactivity from the nuclear facilities, the environmental emergency due to this accident should be predicted rapidly and be informed immediately. The SPEEDI network system for such purpose was completed and now operated by Nuclear Safety Technology Center (NUSTEC) commissioned to do by Ministry of Education, Culture, Sports, Science and Technology, Japan. Fujitsu has been contributing to this project by developing the principal parts of the network performance, by introducing necessary servers, and also by keeping the network in good condition, such as with construction of the system followed by continuous operation and maintenance of the system. Real-time prediction of atmospheric diffusion of radionuclides for nuclear accidents in the world is now available with experimental verification for the real-time emergency response system. Improvement of worldwide version of the SPEEDI network system, accidental discharge of radionuclides with the function of simultaneous prediction for multiple domains and its evaluation is possible. (S. Ohno)

  12. Evaluating the Predictive Value of Growth Prediction Models

    Science.gov (United States)

    Murphy, Daniel L.; Gaertner, Matthew N.

    2014-01-01

    This study evaluates four growth prediction models--projection, student growth percentile, trajectory, and transition table--commonly used to forecast (and give schools credit for) middle school students' future proficiency. Analyses focused on vertically scaled summative mathematics assessments, and two performance standards conditions (high…

  13. Model predictive control classical, robust and stochastic

    CERN Document Server

    Kouvaritakis, Basil

    2016-01-01

    For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplic...

  14. Environmental Modeling Framework using Stacked Gaussian Processes

    OpenAIRE

    Abdelfatah, Kareem; Bao, Junshu; Terejanu, Gabriel

    2016-01-01

    A network of independently trained Gaussian processes (StackedGP) is introduced to obtain predictions of quantities of interest with quantified uncertainties. The main applications of the StackedGP framework are to integrate different datasets through model composition, enhance predictions of quantities of interest through a cascade of intermediate predictions, and to propagate uncertainties through emulated dynamical systems driven by uncertain forcing variables. By using analytical first an...

  15. The integrated environmental control model

    Energy Technology Data Exchange (ETDEWEB)

    Rubin, E.S.; Berkenpas, M.B.; Kalagnanam, J.R. [Carnegie Mellon Univ., Pittsburgh, PA (United States)

    1995-11-01

    The capability to estimate the performance and cost of emission control systems is critical to a variety of planning and analysis requirements faced by utilities, regulators, researchers and analysts in the public and private sectors. The computer model described in this paper has been developed for DOe to provide an up-to-date capability for analyzing a variety of pre-combustion, combustion, and post-combustion options in an integrated framework. A unique capability allows performance and costs to be modeled probabilistically, which allows explicit characterization of uncertainties and risks.

  16. Environmental modelling as an interface between policy and science

    International Nuclear Information System (INIS)

    Blaauboer, R.O.

    1991-01-01

    In this paper some recent developments in environmental modelling in the Netherlands are reviewed from a radiological background. An example is the growing interest in the integration of risk-analysis for exposures to ionizing radiation with that for chemicals and other environmental pollutants. Also since the Chernobyl accident the need of interdisciplinary cooperation, in order to obtain on-line predictive models is more strongly felt. For example atmospheric transport models for chemical pollutants are used for the modelling of the dispersion of radioactive material and vice versa models that were primarily meant for radiation protection purposes are used for other environmental pollutants such as dioxins. At the same time this tendency to integrate and/or couple compartmental models requires harmonization and has its own specific problems. Examples are the difficulties in handling uncertainties, validation, handling of large data bases, correlations between processes in various models and the differing methods and applications of the constituent models (deterministic, probabilistic, for normal or emergency situations etc.). It is thought that the radiological expertise can play an important role in some if not most of these trends in environmental modelling. (9 refs., 1 fig.)

  17. Prediction skill of rainstorm events over India in the TIGGE weather prediction models

    Science.gov (United States)

    Karuna Sagar, S.; Rajeevan, M.; Vijaya Bhaskara Rao, S.; Mitra, A. K.

    2017-12-01

    Extreme rainfall events pose a serious threat of leading to severe floods in many countries worldwide. Therefore, advance prediction of its occurrence and spatial distribution is very essential. In this paper, an analysis has been made to assess the skill of numerical weather prediction models in predicting rainstorms over India. Using gridded daily rainfall data set and objective criteria, 15 rainstorms were identified during the monsoon season (June to September). The analysis was made using three TIGGE (THe Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble) models. The models considered are the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centre for Environmental Prediction (NCEP) and the UK Met Office (UKMO). Verification of the TIGGE models for 43 observed rainstorm days from 15 rainstorm events has been made for the period 2007-2015. The comparison reveals that rainstorm events are predictable up to 5 days in advance, however with a bias in spatial distribution and intensity. The statistical parameters like mean error (ME) or Bias, root mean square error (RMSE) and correlation coefficient (CC) have been computed over the rainstorm region using the multi-model ensemble (MME) mean. The study reveals that the spread is large in ECMWF and UKMO followed by the NCEP model. Though the ensemble spread is quite small in NCEP, the ensemble member averages are not well predicted. The rank histograms suggest that the forecasts are under prediction. The modified Contiguous Rain Area (CRA) technique was used to verify the spatial as well as the quantitative skill of the TIGGE models. Overall, the contribution from the displacement and pattern errors to the total RMSE is found to be more in magnitude. The volume error increases from 24 hr forecast to 48 hr forecast in all the three models.

  18. Predicting farm-level animal populations using environmental and socioeconomic variables.

    Science.gov (United States)

    van Andel, Mary; Jewell, Christopher; McKenzie, Joanna; Hollings, Tracey; Robinson, Andrew; Burgman, Mark; Bingham, Paul; Carpenter, Tim

    2017-09-15

    Accurate information on the geographic distribution of domestic animal populations helps biosecurity authorities to efficiently prepare for and rapidly eradicate exotic diseases, such as Foot and Mouth Disease (FMD). Developing and maintaining sufficiently high-quality data resources is expensive and time consuming. Statistical modelling of population density and distribution has only begun to be applied to farm animal populations, although it is commonly used in wildlife ecology. We developed zero-inflated Poisson regression models in a Bayesian framework using environmental and socioeconomic variables to predict the counts of livestock units (LSUs) and of cattle on spatially referenced farm polygons in a commercially available New Zealand farm database, Agribase. Farm-level counts of cattle and of LSUs varied considerably by region, because of the heterogeneous farming landscape in New Zealand. The amount of high quality pasture per farm was significantly associated with the presence of both cattle and LSUs. Internal model validation (predictive performance) showed that the models were able to predict the count of the animal population on groups of farms that were located in randomly selected 3km zones with a high level of accuracy. Predicting cattle or LSU counts on individual farms was less accurate. Predicted counts were statistically significantly more variable for farms that were contract grazing dry stock, such as replacement dairy heifers and dairy cattle not currently producing milk, compared with other farm types. This analysis presents a way to predict numbers of LSUs and cattle for farms using environmental and socio-economic data. The technique has the potential to be extrapolated to predicting other pastoral based livestock species. Copyright © 2017 Elsevier B.V. All rights reserved.

  19. Prediction of environmental impacts of quarry blasting operation using fuzzy logic.

    Science.gov (United States)

    Fişne, Abdullah; Kuzu, Cengiz; Hüdaverdi, Türker

    2011-03-01

    Blast-induced ground vibration is one of the most important environmental impacts of blasting operations because it may cause severe damage to structures and plants in nearby environment. Estimation of ground vibration levels induced by blasting has vital importance for restricting the environmental effects of blasting operations. Several predictor equations have been proposed by various researchers to predict ground vibration prior to blasting, but these are site specific and not generally applicable beyond the specific conditions. In this study, an attempt has been made to predict the peak particle velocity (PPV) with the help of fuzzy logic approach using parameters of distance from blast face to vibration monitoring point and charge weight per delay. The PPV and charge weight per delay were recorded for 33 blast events at various distances and used for the validation of the proposed fuzzy model. The results of the fuzzy model were also compared with the values obtained from classical regression analysis. The root mean square error estimated for fuzzy-based model was 5.31, whereas it was 11.32 for classical regression-based model. Finally, the relationship between the measured and predicted values of PPV showed that the correlation coefficient for fuzzy model (0.96) is higher than that for regression model (0.82).

  20. A Global Model for Bankruptcy Prediction.

    Science.gov (United States)

    Alaminos, David; Del Castillo, Agustín; Fernández, Manuel Ángel

    2016-01-01

    The recent world financial crisis has increased the number of bankruptcies in numerous countries and has resulted in a new area of research which responds to the need to predict this phenomenon, not only at the level of individual countries, but also at a global level, offering explanations of the common characteristics shared by the affected companies. Nevertheless, few studies focus on the prediction of bankruptcies globally. In order to compensate for this lack of empirical literature, this study has used a methodological framework of logistic regression to construct predictive bankruptcy models for Asia, Europe and America, and other global models for the whole world. The objective is to construct a global model with a high capacity for predicting bankruptcy in any region of the world. The results obtained have allowed us to confirm the superiority of the global model in comparison to regional models over periods of up to three years prior to bankruptcy.

  1. Environmental capacity and the limits of predictive science

    International Nuclear Information System (INIS)

    Taylor, P.

    1991-01-01

    This paper examines the failure of pollution control and hazardous waste management strategies in the light of rapid environmental degradation observed in the decade of the 1980s. It focuses upon the central role of predictive science and assimilative capacity concepts in that failure and the development, a s a consequence, of a paradigm shift in approach, utilising the principles of precautionary action with regard to all substances, programmes of clean production applied to all industrial sectors, and source reduction applied to dissipative activities giving rise to hazardous waste. The past 'assimilative capacity' approaches are criticised as an inadequate foundation for development. In particular the nuclear regulatory concepts of 'justification', 'optimisation' and 'dose-limitation' are seriously deficient. New assessment procedures under development in the London dumping convention are discussed in the light of the precautionary principle. (au)

  2. Fingerprint verification prediction model in hand dermatitis.

    Science.gov (United States)

    Lee, Chew K; Chang, Choong C; Johor, Asmah; Othman, Puwira; Baba, Roshidah

    2015-07-01

    Hand dermatitis associated fingerprint changes is a significant problem and affects fingerprint verification processes. This study was done to develop a clinically useful prediction model for fingerprint verification in patients with hand dermatitis. A case-control study involving 100 patients with hand dermatitis. All patients verified their thumbprints against their identity card. Registered fingerprints were randomized into a model derivation and model validation group. Predictive model was derived using multiple logistic regression. Validation was done using the goodness-of-fit test. The fingerprint verification prediction model consists of a major criterion (fingerprint dystrophy area of ≥ 25%) and two minor criteria (long horizontal lines and long vertical lines). The presence of the major criterion predicts it will almost always fail verification, while presence of both minor criteria and presence of one minor criterion predict high and low risk of fingerprint verification failure, respectively. When none of the criteria are met, the fingerprint almost always passes the verification. The area under the receiver operating characteristic curve was 0.937, and the goodness-of-fit test showed agreement between the observed and expected number (P = 0.26). The derived fingerprint verification failure prediction model is validated and highly discriminatory in predicting risk of fingerprint verification in patients with hand dermatitis. © 2014 The International Society of Dermatology.

  3. Prediction of Low Community Sanitation Coverage Using Environmental and Sociodemographic Factors in Amhara Region, Ethiopia.

    Science.gov (United States)

    Oswald, William E; Stewart, Aisha E P; Flanders, W Dana; Kramer, Michael R; Endeshaw, Tekola; Zerihun, Mulat; Melaku, Birhanu; Sata, Eshetu; Gessesse, Demelash; Teferi, Tesfaye; Tadesse, Zerihun; Guadie, Birhan; King, Jonathan D; Emerson, Paul M; Callahan, Elizabeth K; Moe, Christine L; Clasen, Thomas F

    2016-09-07

    This study developed and validated a model for predicting the probability that communities in Amhara Region, Ethiopia, have low sanitation coverage, based on environmental and sociodemographic conditions. Community sanitation coverage was measured between 2011 and 2014 through trachoma control program evaluation surveys. Information on environmental and sociodemographic conditions was obtained from available data sources and linked with community data using a geographic information system. Logistic regression was used to identify predictors of low community sanitation coverage (sanitation coverage were mapped. Among 1,502 communities, 344 (22.90%) had coverage below 20%. The selected model included measures for high topsoil gravel content, an indicator for low-lying land, population density, altitude, and rainfall and had reasonable predictive discrimination (area under the curve = 0.75, 95% confidence interval = 0.72, 0.78). Measures of soil stability were strongly associated with low community sanitation coverage, controlling for community wealth, and other factors. A model using available environmental and sociodemographic data predicted low community sanitation coverage for areas across Amhara Region with fair discrimination. This approach could assist sanitation programs and trachoma control programs, scaling up or in hyperendemic areas, to target vulnerable areas with additional activities or alternate technologies. © The American Society of Tropical Medicine and Hygiene.

  4. Massive Predictive Modeling using Oracle R Enterprise

    CERN Multimedia

    CERN. Geneva

    2014-01-01

    R is fast becoming the lingua franca for analyzing data via statistics, visualization, and predictive analytics. For enterprise-scale data, R users have three main concerns: scalability, performance, and production deployment. Oracle's R-based technologies - Oracle R Distribution, Oracle R Enterprise, Oracle R Connector for Hadoop, and the R package ROracle - address these concerns. In this talk, we introduce Oracle's R technologies, highlighting how each enables R users to achieve scalability and performance while making production deployment of R results a natural outcome of the data analyst/scientist efforts. The focus then turns to Oracle R Enterprise with code examples using the transparency layer and embedded R execution, targeting massive predictive modeling. One goal behind massive predictive modeling is to build models per entity, such as customers, zip codes, simulations, in an effort to understand behavior and tailor predictions at the entity level. Predictions...

  5. Predictive Model of Systemic Toxicity (SOT)

    Science.gov (United States)

    In an effort to ensure chemical safety in light of regulatory advances away from reliance on animal testing, USEPA and L’Oréal have collaborated to develop a quantitative systemic toxicity prediction model. Prediction of human systemic toxicity has proved difficult and remains a ...

  6. Adaptive Gaussian Predictive Process Models for Large Spatial Datasets

    Science.gov (United States)

    Guhaniyogi, Rajarshi; Finley, Andrew O.; Banerjee, Sudipto; Gelfand, Alan E.

    2011-01-01

    Large point referenced datasets occur frequently in the environmental and natural sciences. Use of Bayesian hierarchical spatial models for analyzing these datasets is undermined by onerous computational burdens associated with parameter estimation. Low-rank spatial process models attempt to resolve this problem by projecting spatial effects to a lower-dimensional subspace. This subspace is determined by a judicious choice of “knots” or locations that are fixed a priori. One such representation yields a class of predictive process models (e.g., Banerjee et al., 2008) for spatial and spatial-temporal data. Our contribution here expands upon predictive process models with fixed knots to models that accommodate stochastic modeling of the knots. We view the knots as emerging from a point pattern and investigate how such adaptive specifications can yield more flexible hierarchical frameworks that lead to automated knot selection and substantial computational benefits. PMID:22298952

  7. Testicular Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  8. Pancreatic Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  9. Colorectal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  10. Prostate Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  11. Bladder Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  12. Esophageal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  13. Cervical Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  14. Breast Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  15. Lung Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  16. Liver Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  17. Ovarian Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  18. Posterior Predictive Model Checking in Bayesian Networks

    Science.gov (United States)

    Crawford, Aaron

    2014-01-01

    This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex…

  19. Advancing environmentally explicit structured population models of plants

    DEFF Research Database (Denmark)

    Ehrlén, Johan; Morris, William; von Euler, Tove

    2016-01-01

    The relationship between the performance of individuals and the surrounding environment is fundamental in ecology and evolutionary biology. Assessing how abiotic and biotic environmental factors influence demographic processes is necessary to understand and predict population dynamics, as well...... as species distributions and abundances. We searched the literature for studies that have linked abiotic and biotic environmental factors to vital rates and, using structured demographic models, population growth rates of plants. We found 136 studies that had examined the environmental drivers of plant...... demography. The number of studies has been increasing rapidly in recent years. Based on the reviewed studies, we identify and discuss several major gaps in our knowledge of environmentally driven demography of plants. We argue that some drivers may have been underexplored and that the full potential...

  20. Predicting and Modeling RNA Architecture

    Science.gov (United States)

    Westhof, Eric; Masquida, Benoît; Jossinet, Fabrice

    2011-01-01

    SUMMARY A general approach for modeling the architecture of large and structured RNA molecules is described. The method exploits the modularity and the hierarchical folding of RNA architecture that is viewed as the assembly of preformed double-stranded helices defined by Watson-Crick base pairs and RNA modules maintained by non-Watson-Crick base pairs. Despite the extensive molecular neutrality observed in RNA structures, specificity in RNA folding is achieved through global constraints like lengths of helices, coaxiality of helical stacks, and structures adopted at the junctions of helices. The Assemble integrated suite of computer tools allows for sequence and structure analysis as well as interactive modeling by homology or ab initio assembly with possibilities for fitting within electronic density maps. The local key role of non-Watson-Crick pairs guides RNA architecture formation and offers metrics for assessing the accuracy of three-dimensional models in a more useful way than usual root mean square deviation (RMSD) values. PMID:20504963

  1. Multiple Steps Prediction with Nonlinear ARX Models

    OpenAIRE

    Zhang, Qinghua; Ljung, Lennart

    2007-01-01

    NLARX (NonLinear AutoRegressive with eXogenous inputs) models are frequently used in black-box nonlinear system identication. Though it is easy to make one step ahead prediction with such models, multiple steps prediction is far from trivial. The main difficulty is that in general there is no easy way to compute the mathematical expectation of an output conditioned by past measurements. An optimal solution would require intensive numerical computations related to nonlinear filltering. The pur...

  2. Predictability of extreme values in geophysical models

    Directory of Open Access Journals (Sweden)

    A. E. Sterk

    2012-09-01

    Full Text Available Extreme value theory in deterministic systems is concerned with unlikely large (or small values of an observable evaluated along evolutions of the system. In this paper we study the finite-time predictability of extreme values, such as convection, energy, and wind speeds, in three geophysical models. We study whether finite-time Lyapunov exponents are larger or smaller for initial conditions leading to extremes. General statements on whether extreme values are better or less predictable are not possible: the predictability of extreme values depends on the observable, the attractor of the system, and the prediction lead time.

  3. Model complexity control for hydrologic prediction

    Science.gov (United States)

    Schoups, G.; van de Giesen, N. C.; Savenije, H. H. G.

    2008-12-01

    A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore needed. We compare three model complexity control methods for hydrologic prediction, namely, cross validation (CV), Akaike's information criterion (AIC), and structural risk minimization (SRM). Results show that simulation of water flow using non-physically-based models (polynomials in this case) leads to increasingly better calibration fits as the model complexity (polynomial order) increases. However, prediction uncertainty worsens for complex non-physically-based models because of overfitting of noisy data. Incorporation of physically based constraints into the model (e.g., storage-discharge relationship) effectively bounds prediction uncertainty, even as the number of parameters increases. The conclusion is that overparameterization and equifinality do not lead to a continued increase in prediction uncertainty, as long as models are constrained by such physical principles. Complexity control of hydrologic models reduces parameter equifinality and identifies the simplest model that adequately explains the data, thereby providing a means of hydrologic generalization and classification. SRM is a promising technique for this purpose, as it (1) provides analytic upper bounds on prediction uncertainty, hence avoiding the computational burden of CV, and (2) extends the applicability of classic methods such as AIC to finite data. The main hurdle in applying SRM is the need for an a priori estimation of the complexity of the hydrologic model, as measured by its Vapnik-Chernovenkis (VC) dimension. Further research is needed in this area.

  4. Middle Range Sea Ice Prediction System of Voyage Environmental Information System in Arctic Sea Route

    Science.gov (United States)

    Lim, H. S.

    2017-12-01

    Due to global warming, the sea ice in the Arctic Ocean is melting dramatically in summer, which is providing a new opportunity to exploit the Northern Sea Route (NSR) connecting Asia and Europe ship route. Recent increases in logistics transportation through NSR and resource development reveal the possible threats of marine pollution and marine transportation accidents without real-time navigation system. To develop a safe Voyage Environmental Information System (VEIS) for vessels operating, the Korea Institute of Ocean Science and Technology (KIOST) which is supported by the Ministry of Oceans and Fisheries, Korea has initiated the development of short-term and middle range prediction system for the sea ice concentration (SIC) and sea ice thickness (SIT) in NSR since 2014. The sea ice prediction system of VEIS consists of AMSR2 satellite composite images (a day), short-term (a week) prediction system, and middle range (a month) prediction system using a statistical method with re-analysis data (TOPAZ) and short-term predicted model data. In this study, the middle range prediction system for the SIC and SIT in NSR is calibrated with another middle range predicted atmospheric and oceanic data (NOAA CFSv2). The system predicts one month SIC and SIT on a daily basis, as validated with dynamic composite SIC data extracted from AMSR2 L2 satellite images.

  5. Parametric linear hybrid automata for complex environmental systems modeling

    OpenAIRE

    Tareen, Samar H. K.; Ahmad, Jamil; Roux, Olivier

    2015-01-01

    Environmental systems, whether they be weather patterns or predator–prey relationships, are dependent on a number different variables, each directly or indirectly affecting the system at large. Since not all of these factors are known, these systems take on non-linear dynamics, making it difficult to accurately predict meaningful behavioral trends far into the future. However, such dynamics do not warrant complete ignorance of different efforts to understand and model close approximations of ...

  6. Quantifying predictive accuracy in survival models.

    Science.gov (United States)

    Lirette, Seth T; Aban, Inmaculada

    2017-12-01

    For time-to-event outcomes in medical research, survival models are the most appropriate to use. Unlike logistic regression models, quantifying the predictive accuracy of these models is not a trivial task. We present the classes of concordance (C) statistics and R 2 statistics often used to assess the predictive ability of these models. The discussion focuses on Harrell's C, Kent and O'Quigley's R 2 , and Royston and Sauerbrei's R 2 . We present similarities and differences between the statistics, discuss the software options from the most widely used statistical analysis packages, and give a practical example using the Worcester Heart Attack Study dataset.

  7. Predictive power of nuclear-mass models

    Directory of Open Access Journals (Sweden)

    Yu. A. Litvinov

    2013-12-01

    Full Text Available Ten different theoretical models are tested for their predictive power in the description of nuclear masses. Two sets of experimental masses are used for the test: the older set of 2003 and the newer one of 2011. The predictive power is studied in two regions of nuclei: the global region (Z, N ≥ 8 and the heavy-nuclei region (Z ≥ 82, N ≥ 126. No clear correlation is found between the predictive power of a model and the accuracy of its description of the masses.

  8. Return Predictability, Model Uncertainty, and Robust Investment

    DEFF Research Database (Denmark)

    Lukas, Manuel

    Stock return predictability is subject to great uncertainty. In this paper we use the model confidence set approach to quantify uncertainty about expected utility from investment, accounting for potential return predictability. For monthly US data and six representative return prediction models, we...... find that confidence sets are very wide, change significantly with the predictor variables, and frequently include expected utilities for which the investor prefers not to invest. The latter motivates a robust investment strategy maximizing the minimal element of the confidence set. The robust investor...... allocates a much lower share of wealth to stocks compared to a standard investor....

  9. Spatial Economics Model Predicting Transport Volume

    Directory of Open Access Journals (Sweden)

    Lu Bo

    2016-10-01

    Full Text Available It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Zhuanghe as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Zhuanghe and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.

  10. Accuracy assessment of landslide prediction models

    International Nuclear Information System (INIS)

    Othman, A N; Mohd, W M N W; Noraini, S

    2014-01-01

    The increasing population and expansion of settlements over hilly areas has greatly increased the impact of natural disasters such as landslide. Therefore, it is important to developed models which could accurately predict landslide hazard zones. Over the years, various techniques and models have been developed to predict landslide hazard zones. The aim of this paper is to access the accuracy of landslide prediction models developed by the authors. The methodology involved the selection of study area, data acquisition, data processing and model development and also data analysis. The development of these models are based on nine different landslide inducing parameters i.e. slope, land use, lithology, soil properties, geomorphology, flow accumulation, aspect, proximity to river and proximity to road. Rank sum, rating, pairwise comparison and AHP techniques are used to determine the weights for each of the parameters used. Four (4) different models which consider different parameter combinations are developed by the authors. Results obtained are compared to landslide history and accuracies for Model 1, Model 2, Model 3 and Model 4 are 66.7, 66.7%, 60% and 22.9% respectively. From the results, rank sum, rating and pairwise comparison can be useful techniques to predict landslide hazard zones

  11. Plant water potential improves prediction of empirical stomatal models.

    Directory of Open Access Journals (Sweden)

    William R L Anderegg

    Full Text Available Climate change is expected to lead to increases in drought frequency and severity, with deleterious effects on many ecosystems. Stomatal responses to changing environmental conditions form the backbone of all ecosystem models, but are based on empirical relationships and are not well-tested during drought conditions. Here, we use a dataset of 34 woody plant species spanning global forest biomes to examine the effect of leaf water potential on stomatal conductance and test the predictive accuracy of three major stomatal models and a recently proposed model. We find that current leaf-level empirical models have consistent biases of over-prediction of stomatal conductance during dry conditions, particularly at low soil water potentials. Furthermore, the recently proposed stomatal conductance model yields increases in predictive capability compared to current models, and with particular improvement during drought conditions. Our results reveal that including stomatal sensitivity to declining water potential and consequent impairment of plant water transport will improve predictions during drought conditions and show that many biomes contain a diversity of plant stomatal strategies that range from risky to conservative stomatal regulation during water stress. Such improvements in stomatal simulation are greatly needed to help unravel and predict the response of ecosystems to future climate extremes.

  12. Models and parameters for environmental radiological assessments

    Energy Technology Data Exchange (ETDEWEB)

    Miller, C W [ed.

    1984-01-01

    This book presents a unified compilation of models and parameters appropriate for assessing the impact of radioactive discharges to the environment. Models examined include those developed for the prediction of atmospheric and hydrologic transport and deposition, for terrestrial and aquatic food-chain bioaccumulation, and for internal and external dosimetry. Chapters have been entered separately into the data base. (ACR)

  13. Models and parameters for environmental radiological assessments

    International Nuclear Information System (INIS)

    Miller, C.W.

    1984-01-01

    This book presents a unified compilation of models and parameters appropriate for assessing the impact of radioactive discharges to the environment. Models examined include those developed for the prediction of atmospheric and hydrologic transport and deposition, for terrestrial and aquatic food-chain bioaccumulation, and for internal and external dosimetry. Chapters have been entered separately into the data base

  14. Dynamic Probabilistic Modeling of Environmental Emissions of Engineered Nanomaterials.

    Science.gov (United States)

    Sun, Tian Yin; Bornhöft, Nikolaus A; Hungerbühler, Konrad; Nowack, Bernd

    2016-05-03

    The need for an environmental risk assessment for engineered nanomaterials (ENM) necessitates the knowledge about their environmental concentrations. Despite significant advances in analytical methods, it is still not possible to measure the concentrations of ENM in natural systems. Material flow and environmental fate models have been used to provide predicted environmental concentrations. However, almost all current models are static and consider neither the rapid development of ENM production nor the fact that many ENM are entering an in-use stock and are released with a lag phase. Here we use dynamic probabilistic material flow modeling to predict the flows of four ENM (nano-TiO2, nano-ZnO, nano-Ag and CNT) to the environment and to quantify their amounts in (temporary) sinks such as the in-use stock and ("final") environmental sinks such as soil and sediment. Caused by the increase in production, the concentrations of all ENM in all compartments are increasing. Nano-TiO2 had far higher concentrations than the other three ENM. Sediment showed in our worst-case scenario concentrations ranging from 6.7 μg/kg (CNT) to about 40 000 μg/kg (nano-TiO2). In most cases the concentrations in waste incineration residues are at the "mg/kg" level. The flows to the environment that we provide will constitute the most accurate and reliable input of masses for environmental fate models which are using process-based descriptions of the fate and behavior of ENM in natural systems and rely on accurate mass input parameters.

  15. Predictive validation of an influenza spread model.

    Directory of Open Access Journals (Sweden)

    Ayaz Hyder

    Full Text Available BACKGROUND: Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. METHODS AND FINDINGS: We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998-1999. Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type. Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. CONCLUSIONS: Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve

  16. Predictive Validation of an Influenza Spread Model

    Science.gov (United States)

    Hyder, Ayaz; Buckeridge, David L.; Leung, Brian

    2013-01-01

    Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive

  17. The predictive performance and stability of six species distribution models.

    Science.gov (United States)

    Duan, Ren-Yan; Kong, Xiao-Quan; Huang, Min-Yi; Fan, Wei-Yi; Wang, Zhi-Gao

    2014-01-01

    Predicting species' potential geographical range by species distribution models (SDMs) is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs. We collected the distribution data of five common tree species (Pinus massoniana, Betula platyphylla, Quercus wutaishanica, Quercus mongolica and Quercus variabilis) and simulated their potential distribution area using 13 environmental variables and six widely used SDMs: BIOCLIM, DOMAIN, MAHAL, RF, MAXENT, and SVM. Each model run was repeated 100 times (trials). We compared the predictive performance by testing the consistency between observations and simulated distributions and assessed the stability by the standard deviation, coefficient of variation, and the 99% confidence interval of Kappa and AUC values. The mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (pSDMs (MAHAL, RF, MAXENT, and SVM) had higher prediction accuracy, smaller confidence intervals, and were more stable and less affected by the random variable (randomly selected pseudo-absence points). According to the prediction performance and stability of SDMs, we can divide these six SDMs into two categories: a high performance and stability group including MAHAL, RF, MAXENT, and SVM, and a low performance and stability group consisting of BIOCLIM, and DOMAIN. We highlight that choosing appropriate SDMs to address a specific problem is an important part of the modeling process.

  18. Posterior predictive checking of multiple imputation models.

    Science.gov (United States)

    Nguyen, Cattram D; Lee, Katherine J; Carlin, John B

    2015-07-01

    Multiple imputation is gaining popularity as a strategy for handling missing data, but there is a scarcity of tools for checking imputation models, a critical step in model fitting. Posterior predictive checking (PPC) has been recommended as an imputation diagnostic. PPC involves simulating "replicated" data from the posterior predictive distribution of the model under scrutiny. Model fit is assessed by examining whether the analysis from the observed data appears typical of results obtained from the replicates produced by the model. A proposed diagnostic measure is the posterior predictive "p-value", an extreme value of which (i.e., a value close to 0 or 1) suggests a misfit between the model and the data. The aim of this study was to evaluate the performance of the posterior predictive p-value as an imputation diagnostic. Using simulation methods, we deliberately misspecified imputation models to determine whether posterior predictive p-values were effective in identifying these problems. When estimating the regression parameter of interest, we found that more extreme p-values were associated with poorer imputation model performance, although the results highlighted that traditional thresholds for classical p-values do not apply in this context. A shortcoming of the PPC method was its reduced ability to detect misspecified models with increasing amounts of missing data. Despite the limitations of posterior predictive p-values, they appear to have a valuable place in the imputer's toolkit. In addition to automated checking using p-values, we recommend imputers perform graphical checks and examine other summaries of the test quantity distribution. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  19. Factors Predicting the Ocular Surface Response to Desiccating Environmental Stress

    Science.gov (United States)

    Alex, Anastasia; Edwards, Austin; Hays, J. Daniel; Kerkstra, Michelle; Shih, Amanda; de Paiva, Cintia S.; Pflugfelder, Stephen C.

    2013-01-01

    Purpose. To identify factors predicting the ocular surface response to experimental desiccating stress. Methods. The ocular surfaces of both eyes of 15 normal and 10 dry eye subjects wearing goggles were exposed to a controlled desiccating environment (15%–25% relative humidity and 2–5 L/min airflow) for 90 minutes. Eye irritation symptoms, blink rate, tear meniscus dimensions, noninvasive (RBUT) and invasive tear break-up time, and corneal fluorescein and conjunctival lissamine green-dye staining were recorded before and after desiccating stress. Pre- and postexposure measurements were compared, and Pearson correlations between clinical parameters before and after desiccating stress were calculated. Results. Corneal and conjunctival dye staining significantly increased in all subjects following 90-minute exposure to desiccating environment, and the magnitude of change was similar in normal and dry eye subjects; except superior cornea staining was greater in dry eye. Irritation severity in the desiccating environment was associated with baseline dye staining, baseline tear meniscus height, and blink rate after 45 minutes. Desiccation-induced change in corneal fluorescein staining was inversely correlated to baseline tear meniscus width, whereas change in total ocular surface dye staining was inversely correlated to baseline dye staining, RBUT, and tear meniscus height and width. Blink rate from 30 to 90 minutes in desiccating environment was higher in the dry eye than normal group. Blink rate significantly correlated to baseline corneal fluorescein staining and environmental-induced change in corneal fluorescein staining. Conclusions. Ocular surface dye staining increases in response to desiccating stress. Baseline ocular surface dye staining, tear meniscus height, and blink rate predict severity of ocular surface dye staining following exposure to a desiccating environment. PMID:23572103

  20. Predicting Protein Secondary Structure with Markov Models

    DEFF Research Database (Denmark)

    Fischer, Paul; Larsen, Simon; Thomsen, Claus

    2004-01-01

    we are considering here, is to predict the secondary structure from the primary one. To this end we train a Markov model on training data and then use it to classify parts of unknown protein sequences as sheets, helices or coils. We show how to exploit the directional information contained...... in the Markov model for this task. Classifications that are purely based on statistical models might not always be biologically meaningful. We present combinatorial methods to incorporate biological background knowledge to enhance the prediction performance....

  1. Energy based prediction models for building acoustics

    DEFF Research Database (Denmark)

    Brunskog, Jonas

    2012-01-01

    In order to reach robust and simplified yet accurate prediction models, energy based principle are commonly used in many fields of acoustics, especially in building acoustics. This includes simple energy flow models, the framework of statistical energy analysis (SEA) as well as more elaborated...... principles as, e.g., wave intensity analysis (WIA). The European standards for building acoustic predictions, the EN 12354 series, are based on energy flow and SEA principles. In the present paper, different energy based prediction models are discussed and critically reviewed. Special attention is placed...... on underlying basic assumptions, such as diffuse fields, high modal overlap, resonant field being dominant, etc., and the consequences of these in terms of limitations in the theory and in the practical use of the models....

  2. Comparative Study of Bancruptcy Prediction Models

    Directory of Open Access Journals (Sweden)

    Isye Arieshanti

    2013-09-01

    Full Text Available Early indication of bancruptcy is important for a company. If companies aware of  potency of their bancruptcy, they can take a preventive action to anticipate the bancruptcy. In order to detect the potency of a bancruptcy, a company can utilize a a model of bancruptcy prediction. The prediction model can be built using a machine learning methods. However, the choice of machine learning methods should be performed carefully. Because the suitability of a model depends on the problem specifically. Therefore, in this paper we perform a comparative study of several machine leaning methods for bancruptcy prediction. According to the comparative study, the performance of several models that based on machine learning methods (k-NN, fuzzy k-NN, SVM, Bagging Nearest Neighbour SVM, Multilayer Perceptron(MLP, Hybrid of MLP + Multiple Linear Regression, it can be showed that fuzzy k-NN method achieve the best performance with accuracy 77.5%

  3. Flexing the PECs: Predicting environmental concentrations of veterinary drugs in Canadian agricultural soils.

    Science.gov (United States)

    Kullik, Sigrun A; Belknap, Andrew M

    2017-03-01

    Veterinary drugs administered to food animals primarily enter ecosystems through the application of livestock waste to agricultural land. Although veterinary drugs are essential for protecting animal health, their entry into the environment may pose a risk for nontarget organisms. A means to predict environmental concentrations of new veterinary drug ingredients in soil is required to assess their environmental fate, distribution, and potential effects. The Canadian predicted environmental concentrations in soil (PECsoil) for new veterinary drug ingredients for use in intensively reared animals is based on the approach currently used by the European Medicines Agency for VICH Phase I environmental assessments. The calculation for the European Medicines Agency PECsoil can be adapted to account for regional animal husbandry and land use practices. Canadian agricultural practices for intensively reared cattle, pigs, and poultry differ substantially from those in the European Union. The development of PECsoil default values and livestock categories representative of typical Canadian animal production methods and nutrient management practices culminates several years of research and an extensive survey and analysis of the scientific literature, Canadian agricultural statistics, national and provincial management recommendations, veterinary product databases, and producers. A PECsoil can be used to rapidly identify new veterinary drugs intended for intensive livestock production that should undergo targeted ecotoxicity and fate testing. The Canadian PECsoil model is readily available, transparent, and requires minimal inputs to generate a screening level environmental assessment for veterinary drugs that can be refined if additional data are available. PECsoil values for a hypothetical veterinary drug dosage regimen are presented and discussed in an international context. Integr Environ Assess Manag 2017;13:331-341. © 2016 Her Majesty the Queen in Right of Canada

  4. Physical and JIT Model Based Hybrid Modeling Approach for Building Thermal Load Prediction

    Science.gov (United States)

    Iino, Yutaka; Murai, Masahiko; Murayama, Dai; Motoyama, Ichiro

    Energy conservation in building fields is one of the key issues in environmental point of view as well as that of industrial, transportation and residential fields. The half of the total energy consumption in a building is occupied by HVAC (Heating, Ventilating and Air Conditioning) systems. In order to realize energy conservation of HVAC system, a thermal load prediction model for building is required. This paper propose a hybrid modeling approach with physical and Just-in-Time (JIT) model for building thermal load prediction. The proposed method has features and benefits such as, (1) it is applicable to the case in which past operation data for load prediction model learning is poor, (2) it has a self checking function, which always supervises if the data driven load prediction and the physical based one are consistent or not, so it can find if something is wrong in load prediction procedure, (3) it has ability to adjust load prediction in real-time against sudden change of model parameters and environmental conditions. The proposed method is evaluated with real operation data of an existing building, and the improvement of load prediction performance is illustrated.

  5. Prediction Models for Dynamic Demand Response

    Energy Technology Data Exchange (ETDEWEB)

    Aman, Saima; Frincu, Marc; Chelmis, Charalampos; Noor, Muhammad; Simmhan, Yogesh; Prasanna, Viktor K.

    2015-11-02

    As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D2R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D2R, which we address in this paper. Our first contribution is the formal definition of D2R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D2R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D2R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D2R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D2R. Also, prediction models require just few days’ worth of data indicating that small amounts of

  6. Stochastic modeling for environmental stress screening

    OpenAIRE

    Cha, Ji Hwan; Finkelstein, Maxim

    2014-01-01

    Environmental stress screening (ESS) of manufactured items is used to reduce the occurrence of future failures that are caused by latent defects by eliminating the items with these defects. Some practical descriptions of the relevant ESS procedures can be found in the literature; however, the appropriate stochastic modeling and the corresponding thorough analysis have not been reported. In this paper we develop a stochastic model for the ESS, analyze the effect of this operation o...

  7. Modeling compliant grasps exploiting environmental constraints

    OpenAIRE

    Salvietti, Gionata; Malvezzi, Monica; Gioioso, Guido; Prattichizzo, Domenico

    2015-01-01

    In this paper we present a mathematical framework to describe the interaction between compliant hands and environmental constraints during grasping tasks. In the proposed model, we considered compliance at wrist, joint and contact level. We modeled the general case in which the hand is in contact with the object and the surrounding environment. All the other contact cases can be derived from the proposed system of equations. We performed several numerical simulation using the SynGrasp Matlab ...

  8. Are animal models predictive for humans?

    Directory of Open Access Journals (Sweden)

    Greek Ray

    2009-01-01

    Full Text Available Abstract It is one of the central aims of the philosophy of science to elucidate the meanings of scientific terms and also to think critically about their application. The focus of this essay is the scientific term predict and whether there is credible evidence that animal models, especially in toxicology and pathophysiology, can be used to predict human outcomes. Whether animals can be used to predict human response to drugs and other chemicals is apparently a contentious issue. However, when one empirically analyzes animal models using scientific tools they fall far short of being able to predict human responses. This is not surprising considering what we have learned from fields such evolutionary and developmental biology, gene regulation and expression, epigenetics, complexity theory, and comparative genomics.

  9. Evaluation of CASP8 model quality predictions

    KAUST Repository

    Cozzetto, Domenico

    2009-01-01

    The model quality assessment problem consists in the a priori estimation of the overall and per-residue accuracy of protein structure predictions. Over the past years, a number of methods have been developed to address this issue and CASP established a prediction category to evaluate their performance in 2006. In 2008 the experiment was repeated and its results are reported here. Participants were invited to infer the correctness of the protein models submitted by the registered automatic servers. Estimates could apply to both whole models and individual amino acids. Groups involved in the tertiary structure prediction categories were also asked to assign local error estimates to each predicted residue in their own models and their results are also discussed here. The correlation between the predicted and observed correctness measures was the basis of the assessment of the results. We observe that consensus-based methods still perform significantly better than those accepting single models, similarly to what was concluded in the previous edition of the experiment. © 2009 WILEY-LISS, INC.

  10. Streamlining environmental product declarations: a stage model

    Science.gov (United States)

    Lefebvre, Elisabeth; Lefebvre, Louis A.; Talbot, Stephane; Le Hen, Gael

    2001-02-01

    General public environmental awareness and education is increasing, therefore stimulating the demand for reliable, objective and comparable information about products' environmental performances. The recently published standard series ISO 14040 and ISO 14025 are normalizing the preparation of Environmental Product Declarations (EPDs) containing comprehensive information relevant to a product's environmental impact during its life cycle. So far, only a few environmentally leading manufacturing organizations have experimented the preparation of EPDs (mostly from Europe), demonstrating its great potential as a marketing weapon. However the preparation of EPDs is a complex process, requiring collection and analysis of massive amounts of information coming from disparate sources (suppliers, sub-contractors, etc.). In a foreseeable future, the streamlining of the EPD preparation process will require product manufacturers to adapt their information systems (ERP, MES, SCADA) in order to make them capable of gathering, and transmitting the appropriate environmental information. It also requires strong functional integration all along the product supply chain in order to ensure that all the information is made available in a standardized and timely manner. The goal of the present paper is two fold: first to propose a transitional model towards green supply chain management and EPD preparation; second to identify key technologies and methodologies allowing to streamline the EPD process and subsequently the transition toward sustainable product development

  11. Model predictive controller design of hydrocracker reactors

    OpenAIRE

    GÖKÇE, Dila

    2014-01-01

    This study summarizes the design of a Model Predictive Controller (MPC) in Tüpraş, İzmit Refinery Hydrocracker Unit Reactors. Hydrocracking process, in which heavy vacuum gasoil is converted into lighter and valuable products at high temperature and pressure is described briefly. Controller design description, identification and modeling studies are examined and the model variables are presented. WABT (Weighted Average Bed Temperature) equalization and conversion increase are simulate...

  12. Medard - An Environmental Modelling Project for the Territory of the Czech Republic

    Czech Academy of Sciences Publication Activity Database

    Eben, Kryštof; Juruš, Pavel; Resler, Jaroslav; Belda, Michal; Krueger, B.C.

    č. 61 (2005), s. 18-19 ISSN 0926-4981 Institutional research plan: CEZ:AV0Z10300504 Keywords : air quality forecasting * chemistry transport models * operational environmental prediction * data assimilation Subject RIV: BA - General Mathematics

  13. Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness.

    Science.gov (United States)

    Li, Jin; Tran, Maggie; Siwabessy, Justy

    2016-01-01

    Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia's marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage at limited locations. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e., hard90 and hard70). We developed optimal predictive models to predict seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), knowledge informed AVI (KIAVI), Boruta and regularized RF (RRF) were tested based on predictive accuracy. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were examined. Finally, spatial predictions generated using the most accurate models were visually examined and analysed. This study confirmed that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness of four classes can be predicted with a high degree of accuracy; 3) the typical approach used to pre-select predictive variables by excluding highly correlated variables needs to be re-examined; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving predictive models; 5) FS methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to 'small p and large n' problems in environmental sciences. Additionally, automated computational programs for AVI need to be developed to increase its computational efficiency and

  14. Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness

    Science.gov (United States)

    Li, Jin; Tran, Maggie; Siwabessy, Justy

    2016-01-01

    Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia’s marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage at limited locations. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e., hard90 and hard70). We developed optimal predictive models to predict seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), knowledge informed AVI (KIAVI), Boruta and regularized RF (RRF) were tested based on predictive accuracy. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were examined. Finally, spatial predictions generated using the most accurate models were visually examined and analysed. This study confirmed that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness of four classes can be predicted with a high degree of accuracy; 3) the typical approach used to pre-select predictive variables by excluding highly correlated variables needs to be re-examined; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving predictive models; 5) FS methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to ‘small p and large n’ problems in environmental sciences. Additionally, automated computational programs for AVI need to be developed to increase its computational efficiency and

  15. Prediction of Dominant Forest Tree Species Using QuickBird and Environmental Data

    Directory of Open Access Journals (Sweden)

    Azadeh Abdollahnejad

    2017-02-01

    Full Text Available Modelling the spatial distribution of plants is one of the indirect methods for predicting the properties of plants and can be defined based on the relationship between the spatial distribution of vegetation and environmental variables. In this article, we introduce a new method for the spatial prediction of the dominant trees and species, through a combination of environmental and satellite data. Based on the basal area factor (BAF frequency for each tree species in a total of 518 sample plots, the dominant tree species were determined for each plot. Also, topographical maps of primary and secondary properties were prepared using the digital elevation model (DEM. Categories of soil and the climate maps database of the Doctor Bahramnia Forestry Plan were extracted as well. After pre-processing and processing of spectral data, the pixel values at the sample locations in all the independent factors such as spectral and non-spectral data, were extracted. The modelling rates of tree and shrub species diversity using data mining algorithms of 80% of the sampling plots were taken. Assessment of model accuracy was conducted using 20% of samples and evaluation criteria. Random forest (RF, support vector machine (SVM and k-nearest neighbor (k-NN algorithms were used for spatial distribution modelling of dominant species groups using environmental and spectral variables from 80% of the sample plots. Results showed physiographic factors, especially altitude in combination with soil and climate factors as the most important variables in the distribution of species, while the best model was created by the integration of physiographic factors (in combination with soil and climate with an overall accuracy of 63.85%. In addition, the results of the comparison between the algorithms, showed that the RF algorithm was the most accurate in modelling the diversity.

  16. Robust predictive modelling of water pollution using biomarker data.

    Science.gov (United States)

    Budka, Marcin; Gabrys, Bogdan; Ravagnan, Elisa

    2010-05-01

    This paper describes the methodology of building a predictive model for the purpose of marine pollution monitoring, based on low quality biomarker data. A step-by-step, systematic data analysis approach is presented, resulting in design of a purely data-driven model, able to accurately discriminate between various coastal water pollution levels. The environmental scientists often try to apply various machine learning techniques to their data without much success, mostly because of the lack of experience with different methods and required 'under the hood' knowledge. Thus this paper is a result of a collaboration between the machine learning and environmental science communities, presenting a predictive model development workflow, as well as discussing and addressing potential pitfalls and difficulties. The novelty of the modelling approach presented lays in successful application of machine learning techniques to high dimensional, incomplete biomarker data, which to our knowledge has not been done before and is the result of close collaboration between machine learning and environmental science communities.

  17. Multi-Model Ensemble Wake Vortex Prediction

    Science.gov (United States)

    Koerner, Stephan; Holzaepfel, Frank; Ahmad, Nash'at N.

    2015-01-01

    Several multi-model ensemble methods are investigated for predicting wake vortex transport and decay. This study is a joint effort between National Aeronautics and Space Administration and Deutsches Zentrum fuer Luft- und Raumfahrt to develop a multi-model ensemble capability using their wake models. An overview of different multi-model ensemble methods and their feasibility for wake applications is presented. The methods include Reliability Ensemble Averaging, Bayesian Model Averaging, and Monte Carlo Simulations. The methodologies are evaluated using data from wake vortex field experiments.

  18. Thermodynamic modeling of activity coefficient and prediction of solubility: Part 1. Predictive models.

    Science.gov (United States)

    Mirmehrabi, Mahmoud; Rohani, Sohrab; Perry, Luisa

    2006-04-01

    A new activity coefficient model was developed from excess Gibbs free energy in the form G(ex) = cA(a) x(1)(b)...x(n)(b). The constants of the proposed model were considered to be function of solute and solvent dielectric constants, Hildebrand solubility parameters and specific volumes of solute and solvent molecules. The proposed model obeys the Gibbs-Duhem condition for activity coefficient models. To generalize the model and make it as a purely predictive model without any adjustable parameters, its constants were found using the experimental activity coefficient and physical properties of 20 vapor-liquid systems. The predictive capability of the proposed model was tested by calculating the activity coefficients of 41 binary vapor-liquid equilibrium systems and showed good agreement with the experimental data in comparison with two other predictive models, the UNIFAC and Hildebrand models. The only data used for the prediction of activity coefficients, were dielectric constants, Hildebrand solubility parameters, and specific volumes of the solute and solvent molecules. Furthermore, the proposed model was used to predict the activity coefficient of an organic compound, stearic acid, whose physical properties were available in methanol and 2-butanone. The predicted activity coefficient along with the thermal properties of the stearic acid were used to calculate the solubility of stearic acid in these two solvents and resulted in a better agreement with the experimental data compared to the UNIFAC and Hildebrand predictive models.

  19. PREDICTIVE CAPACITY OF ARCH FAMILY MODELS

    Directory of Open Access Journals (Sweden)

    Raphael Silveira Amaro

    2016-03-01

    Full Text Available In the last decades, a remarkable number of models, variants from the Autoregressive Conditional Heteroscedastic family, have been developed and empirically tested, making extremely complex the process of choosing a particular model. This research aim to compare the predictive capacity, using the Model Confidence Set procedure, than five conditional heteroskedasticity models, considering eight different statistical probability distributions. The financial series which were used refers to the log-return series of the Bovespa index and the Dow Jones Industrial Index in the period between 27 October 2008 and 30 December 2014. The empirical evidences showed that, in general, competing models have a great homogeneity to make predictions, either for a stock market of a developed country or for a stock market of a developing country. An equivalent result can be inferred for the statistical probability distributions that were used.

  20. A revised prediction model for natural conception.

    Science.gov (United States)

    Bensdorp, Alexandra J; van der Steeg, Jan Willem; Steures, Pieternel; Habbema, J Dik F; Hompes, Peter G A; Bossuyt, Patrick M M; van der Veen, Fulco; Mol, Ben W J; Eijkemans, Marinus J C

    2017-06-01

    One of the aims in reproductive medicine is to differentiate between couples that have favourable chances of conceiving naturally and those that do not. Since the development of the prediction model of Hunault, characteristics of the subfertile population have changed. The objective of this analysis was to assess whether additional predictors can refine the Hunault model and extend its applicability. Consecutive subfertile couples with unexplained and mild male subfertility presenting in fertility clinics were asked to participate in a prospective cohort study. We constructed a multivariable prediction model with the predictors from the Hunault model and new potential predictors. The primary outcome, natural conception leading to an ongoing pregnancy, was observed in 1053 women of the 5184 included couples (20%). All predictors of the Hunault model were selected into the revised model plus an additional seven (woman's body mass index, cycle length, basal FSH levels, tubal status,history of previous pregnancies in the current relationship (ongoing pregnancies after natural conception, fertility treatment or miscarriages), semen volume, and semen morphology. Predictions from the revised model seem to concur better with observed pregnancy rates compared with the Hunault model; c-statistic of 0.71 (95% CI 0.69 to 0.73) compared with 0.59 (95% CI 0.57 to 0.61). Copyright © 2017. Published by Elsevier Ltd.

  1. Modelling of radionuclide interception and loss processes in vegetation and of transfer in semi-natural ecosystems. Second report of the VAMP terrestrial working group. Part of the IAEA/CEC co-ordinated research programme on the validation of environmental model predictions (VAMP)

    International Nuclear Information System (INIS)

    1996-01-01

    Following the Chernobyl accident and on the recommendation of the International Nuclear Safety Advisory Group (INSAG) in its Summary Report on the Post-Accident Review Meeting on the Chernobyl Accident, the IAEA established a Co-ordinated Research Programme on ''The Validation of Models for the Transfer of Radionuclides in Terrestrial, Urban and Aquatic Environments and the Acquisition of Data for that Purpose''. The programme seeks to use the information on the environmental behaviour of radionuclides which became available as a result of the measurement programmes instituted in the countries of the former USSR and in many European countries after April 1986 for the purpose of testing the reliability of assessment models. Such models find application in assessing the radiological impact of all parts of the nuclear fuel cycle. They are used at the planning and design stage to predict the radiological impact of planned nuclear facilities, in assessing the possible consequences of accidents involving releases of radioactive material to the environment and in establishing criteria for the implementation of countermeasures. In the operational phase they are used together with the results of environmental monitoring to demonstrate compliance with regulatory requirements regarding release limitation. Refs, figs and tabs

  2. Validation of models using Chernobyl fallout data from southern Finland. Scenario S. Second report of the VAMP multiple pathways assessment working group. Part of the IAEA/CEC co-ordinated research programme on the validation of environmental model predictions (VAMP)

    International Nuclear Information System (INIS)

    1996-09-01

    Following the Chernobyl accident and on the recommendation of the International Nuclear Safety Advisory Group (INSAG) in its Summary Report on the Post-Accident Review Meeting on the Chernobyl Accident (Safety Series No. 75-INSAG-1, IAEA, Vienna, 1986), the IAEA established a Co-ordinated Research Programme on ''The Validation of Models for the Transfer of Radionuclides in Terrestrial, Urban and Aquatic Environments and the Acquisition of Data for that Purpose''. The programme used the information on the environmental behaviour of radionuclides which became available as a result of the measurement programmes instituted in countries of the former Soviet Union and in many European countries after April 1986 for the purpose of testing the reliability of assessment models. Such models find application in assessing the radiological impact of all parts of the nuclear fuel cycle. They are used in the planning and design stage to predict the radiological impact of nuclear facilities and in assessing the possible consequences of accidents involving releases of radioactive material to the environment and in establishing criteria for the implementation of countermeasures. In the operational phase, they are used together with the results of environmental monitoring to demonstrate compliance with regulatory requirements concerned with radiation dose limitation. Refs, figs, tabs

  3. Microbial forensics: predicting phenotypic characteristics and environmental conditions from large-scale gene expression profiles.

    Science.gov (United States)

    Kim, Minseung; Zorraquino, Violeta; Tagkopoulos, Ilias

    2015-03-01

    A tantalizing question in cellular physiology is whether the cellular state and environmental conditions can be inferred by the expression signature of an organism. To investigate this relationship, we created an extensive normalized gene expression compendium for the bacterium Escherichia coli that was further enriched with meta-information through an iterative learning procedure. We then constructed an ensemble method to predict environmental and cellular state, including strain, growth phase, medium, oxygen level, antibiotic and carbon source presence. Results show that gene expression is an excellent predictor of environmental structure, with multi-class ensemble models achieving balanced accuracy between 70.0% (±3.5%) to 98.3% (±2.3%) for the various characteristics. Interestingly, this performance can be significantly boosted when environmental and strain characteristics are simultaneously considered, as a composite classifier that captures the inter-dependencies of three characteristics (medium, phase and strain) achieved 10.6% (±1.0%) higher performance than any individual models. Contrary to expectations, only 59% of the top informative genes were also identified as differentially expressed under the respective conditions. Functional analysis of the respective genetic signatures implicates a wide spectrum of Gene Ontology terms and KEGG pathways with condition-specific information content, including iron transport, transferases, and enterobactin synthesis. Further experimental phenotypic-to-genotypic mapping that we conducted for knock-out mutants argues for the information content of top-ranked genes. This work demonstrates the degree at which genome-scale transcriptional information can be predictive of latent, heterogeneous and seemingly disparate phenotypic and environmental characteristics, with far-reaching applications.

  4. Modeling of adipose/blood partition coefficient for environmental chemicals.

    Science.gov (United States)

    Papadaki, K C; Karakitsios, S P; Sarigiannis, D A

    2017-12-01

    A Quantitative Structure Activity Relationship (QSAR) model was developed in order to predict the adipose/blood partition coefficient of environmental chemical compounds. The first step of QSAR modeling was the collection of inputs. Input data included the experimental values of adipose/blood partition coefficient and two sets of molecular descriptors for 67 organic chemical compounds; a) the descriptors from Linear Free Energy Relationship (LFER) and b) the PaDEL descriptors. The datasets were split to training and prediction set and were analysed using two statistical methods; Genetic Algorithm based Multiple Linear Regression (GA-MLR) and Artificial Neural Networks (ANN). The models with LFER and PaDEL descriptors, coupled with ANN, produced satisfying performance results. The fitting performance (R 2 ) of the models, using LFER and PaDEL descriptors, was 0.94 and 0.96, respectively. The Applicability Domain (AD) of the models was assessed and then the models were applied to a large number of chemical compounds with unknown values of adipose/blood partition coefficient. In conclusion, the proposed models were checked for fitting, validity and applicability. It was demonstrated that they are stable, reliable and capable to predict the values of adipose/blood partition coefficient of "data poor" chemical compounds that fall within the applicability domain. Copyright © 2017. Published by Elsevier Ltd.

  5. Variation in Environmentalism among University Students: Majoring in Outdoor Recreation, Parks, and Tourism Predicts Environmental Concerns and Behaviors

    Science.gov (United States)

    Arnocky, Steven; Stroink, Mirella L.

    2011-01-01

    In a survey of Canadian university students (N = 205), the relationship between majoring in an outdoor recreation university program and environmental concern, cooperation, and behavior were examined. Stepwise linear regression indicated that enrollment in outdoor recreation was predictive of environmental behavior and ecological cooperation; and…

  6. Modelling the predictive performance of credit scoring

    Directory of Open Access Journals (Sweden)

    Shi-Wei Shen

    2013-07-01

    Research purpose: The purpose of this empirical paper was to examine the predictive performance of credit scoring systems in Taiwan. Motivation for the study: Corporate lending remains a major business line for financial institutions. However, in light of the recent global financial crises, it has become extremely important for financial institutions to implement rigorous means of assessing clients seeking access to credit facilities. Research design, approach and method: Using a data sample of 10 349 observations drawn between 1992 and 2010, logistic regression models were utilised to examine the predictive performance of credit scoring systems. Main findings: A test of Goodness of fit demonstrated that credit scoring models that incorporated the Taiwan Corporate Credit Risk Index (TCRI, micro- and also macroeconomic variables possessed greater predictive power. This suggests that macroeconomic variables do have explanatory power for default credit risk. Practical/managerial implications: The originality in the study was that three models were developed to predict corporate firms’ defaults based on different microeconomic and macroeconomic factors such as the TCRI, asset growth rates, stock index and gross domestic product. Contribution/value-add: The study utilises different goodness of fits and receiver operator characteristics during the examination of the robustness of the predictive power of these factors.

  7. Modelling language evolution: Examples and predictions

    Science.gov (United States)

    Gong, Tao; Shuai, Lan; Zhang, Menghan

    2014-06-01

    We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon-syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines.

  8. A neural network based model for urban noise prediction.

    Science.gov (United States)

    Genaro, N; Torija, A; Ramos-Ridao, A; Requena, I; Ruiz, D P; Zamorano, M

    2010-10-01

    Noise is a global problem. In 1972 the World Health Organization (WHO) classified noise as a pollutant. Since then, most industrialized countries have enacted laws and local regulations to prevent and reduce acoustic environmental pollution. A further aim is to alert people to the dangers of this type of pollution. In this context, urban planners need to have tools that allow them to evaluate the degree of acoustic pollution. Scientists in many countries have modeled urban noise, using a wide range of approaches, but their results have not been as good as expected. This paper describes a model developed for the prediction of environmental urban noise using Soft Computing techniques, namely Artificial Neural Networks (ANN). The model is based on the analysis of variables regarded as influential by experts in the field and was applied to data collected on different types of streets. The results were compared to those obtained with other models. The study found that the ANN system was able to predict urban noise with greater accuracy, and thus, was an improvement over those models. The principal component analysis (PCA) was also used to try to simplify the model. Although there was a slight decline in the accuracy of the results, the values obtained were also quite acceptable.

  9. A case study predicting environmental impacts of urban transport planning in China.

    Science.gov (United States)

    Chen, Chong; Shao, Li-guo; Xu, Ling; Shang, Jin-cheng

    2009-10-01

    Predicting environmental impacts is essential when performing an environmental assessment on urban transport planning. System dynamics (SD) is usually used to solve complex nonlinear problems. In this study, we utilized system dynamics (SD) to evaluate the environmental impacts associated with urban transport planning in Jilin City, China with respect to the local economy, society, transport, the environment and resources. To accomplish this, we generated simulation models comprising interrelated subsystems designed to utilize changes in the economy, society, road construction, changes in the number of vehicles, the capacity of the road network capacity, nitrogen oxides emission, traffic noise, land used for road construction and fuel consumption associated with traffic to estimate dynamic trends in the environmental impacts associated with Jilin's transport planning. Two simulation scenarios were then analyzed comparatively. The results of this study indicated that implementation of Jilin transport planning would improve the current urban traffic conditions and boost the local economy and development while benefiting the environment in Jilin City. In addition, comparative analysis of the two scenarios provided additional information that can be used to aid in scientific decision-making regarding which aspects of the transport planning to implement in Jilin City. This study demonstrates that our application of the SD method, which is referred to as the Strategic Environmental Assessment (SEA), is feasible for use in urban transport planning.

  10. Model Predictive Control of Sewer Networks

    DEFF Research Database (Denmark)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik

    2016-01-01

    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....... and controlled have thus become essential factors for efficient performance of waste water treatment plants. This paper examines methods for simplified modelling and controlling a sewer network. A practical approach to the problem is used by analysing simplified design model, which is based on the Barcelona...

  11. Bayesian Predictive Models for Rayleigh Wind Speed

    DEFF Research Database (Denmark)

    Shahirinia, Amir; Hajizadeh, Amin; Yu, David C

    2017-01-01

    predictive model of the wind speed aggregates the non-homogeneous distributions into a single continuous distribution. Therefore, the result is able to capture the variation among the probability distributions of the wind speeds at the turbines’ locations in a wind farm. More specifically, instead of using...... a wind speed distribution whose parameters are known or estimated, the parameters are considered as random whose variations are according to probability distributions. The Bayesian predictive model for a Rayleigh which only has a single model scale parameter has been proposed. Also closed-form posterior......One of the major challenges with the increase in wind power generation is the uncertain nature of wind speed. So far the uncertainty about wind speed has been presented through probability distributions. Also the existing models that consider the uncertainty of the wind speed primarily view...

  12. Comparison of two ordinal prediction models

    DEFF Research Database (Denmark)

    Kattan, Michael W; Gerds, Thomas A

    2015-01-01

    system (i.e. old or new), such as the level of evidence for one or more factors included in the system or the general opinions of expert clinicians. However, given the major objective of estimating prognosis on an ordinal scale, we argue that the rival staging system candidates should be compared...... on their ability to predict outcome. We sought to outline an algorithm that would compare two rival ordinal systems on their predictive ability. RESULTS: We devised an algorithm based largely on the concordance index, which is appropriate for comparing two models in their ability to rank observations. We...... demonstrate our algorithm with a prostate cancer staging system example. CONCLUSION: We have provided an algorithm for selecting the preferred staging system based on prognostic accuracy. It appears to be useful for the purpose of selecting between two ordinal prediction models....

  13. Fine-scale habitat modeling of a top marine predator: do prey data improve predictive capacity?

    Science.gov (United States)

    Torres, Leigh G; Read, Andrew J; Halpin, Patrick

    2008-10-01

    Predators and prey assort themselves relative to each other, the availability of resources and refuges, and the temporal and spatial scale of their interaction. Predictive models of predator distributions often rely on these relationships by incorporating data on environmental variability and prey availability to determine predator habitat selection patterns. This approach to predictive modeling holds true in marine systems where observations of predators are logistically difficult, emphasizing the need for accurate models. In this paper, we ask whether including prey distribution data in fine-scale predictive models of bottlenose dolphin (Tursiops truncatus) habitat selection in Florida Bay, Florida, U.S.A., improves predictive capacity. Environmental characteristics are often used as predictor variables in habitat models of top marine predators with the assumption that they act as proxies of prey distribution. We examine the validity of this assumption by comparing the response of dolphin distribution and fish catch rates to the same environmental variables. Next, the predictive capacities of four models, with and without prey distribution data, are tested to determine whether dolphin habitat selection can be predicted without recourse to describing the distribution of their prey. The final analysis determines the accuracy of predictive maps of dolphin distribution produced by modeling areas of high fish catch based on significant environmental characteristics. We use spatial analysis and independent data sets to train and test the models. Our results indicate that, due to high habitat heterogeneity and the spatial variability of prey patches, fine-scale models of dolphin habitat selection in coastal habitats will be more successful if environmental variables are used as predictor variables of predator distributions rather than relying on prey data as explanatory variables. However, predictive modeling of prey distribution as the response variable based on

  14. Atmospheric dispersion models for environmental pollution applications

    International Nuclear Information System (INIS)

    Gifford, F.A.

    1976-01-01

    Pollutants are introduced into the air by many of man's activities. The potentially harmful effects these can cause are, broadly speaking, of two kinds: long-term, possibly large-scale and wide-spread chronic effects, including long-term effects on the earth's climate; and acute, short-term effects such as those associated with urban air pollution. This section is concerned with mathematical cloud or plume models describing the role of the atmosphere, primarily in relation to the second of these, the acute effects of air pollution, i.e., those arising from comparatively high concentration levels. The need for such air pollution modeling studies has increased spectacularly as a result of the National Environmental Policy Act of 1968 and, especially, two key court decisions; the Calvert Cliffs decision, and the Sierra Club ruling on environmental non-degradation

  15. Predictive analytics can support the ACO model.

    Science.gov (United States)

    Bradley, Paul

    2012-04-01

    Predictive analytics can be used to rapidly spot hard-to-identify opportunities to better manage care--a key tool in accountable care. When considering analytics models, healthcare providers should: Make value-based care a priority and act on information from analytics models. Create a road map that includes achievable steps, rather than major endeavors. Set long-term expectations and recognize that the effectiveness of an analytics program takes time, unlike revenue cycle initiatives that may show a quick return.

  16. Predictive modeling in homogeneous catalysis: a tutorial

    NARCIS (Netherlands)

    Maldonado, A.G.; Rothenberg, G.

    2010-01-01

    Predictive modeling has become a practical research tool in homogeneous catalysis. It can help to pinpoint ‘good regions’ in the catalyst space, narrowing the search for the optimal catalyst for a given reaction. Just like any other new idea, in silico catalyst optimization is accepted by some

  17. Model predictive control of smart microgrids

    DEFF Research Database (Denmark)

    Hu, Jiefeng; Zhu, Jianguo; Guerrero, Josep M.

    2014-01-01

    required to realise high-performance of distributed generations and will realise innovative control techniques utilising model predictive control (MPC) to assist in coordinating the plethora of generation and load combinations, thus enable the effective exploitation of the clean renewable energy sources...

  18. Feedback model predictive control by randomized algorithms

    NARCIS (Netherlands)

    Batina, Ivo; Stoorvogel, Antonie Arij; Weiland, Siep

    2001-01-01

    In this paper we present a further development of an algorithm for stochastic disturbance rejection in model predictive control with input constraints based on randomized algorithms. The algorithm presented in our work can solve the problem of stochastic disturbance rejection approximately but with

  19. A Robustly Stabilizing Model Predictive Control Algorithm

    Science.gov (United States)

    Ackmece, A. Behcet; Carson, John M., III

    2007-01-01

    A model predictive control (MPC) algorithm that differs from prior MPC algorithms has been developed for controlling an uncertain nonlinear system. This algorithm guarantees the resolvability of an associated finite-horizon optimal-control problem in a receding-horizon implementation.

  20. Hierarchical Model Predictive Control for Resource Distribution

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Trangbæk, K; Stoustrup, Jakob

    2010-01-01

    This paper deals with hierarchichal model predictive control (MPC) of distributed systems. A three level hierachical approach is proposed, consisting of a high level MPC controller, a second level of so-called aggregators, controlled by an online MPC-like algorithm, and a lower level of autonomous...

  1. Model Predictive Control based on Finite Impulse Response Models

    DEFF Research Database (Denmark)

    Prasath, Guru; Jørgensen, John Bagterp

    2008-01-01

    We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations ...

  2. Parametric Linear Hybrid Automata for Complex Environmental Systems Modeling

    Directory of Open Access Journals (Sweden)

    Samar Hayat Khan Tareen

    2015-07-01

    Full Text Available Environmental systems, whether they be weather patterns or predator-prey relationships, are dependent on a number of different variables, each directly or indirectly affecting the system at large. Since not all of these factors are known, these systems take on non-linear dynamics, making it difficult to accurately predict meaningful behavioral trends far into the future. However, such dynamics do not warrant complete ignorance of different efforts to understand and model close approximations of these systems. Towards this end, we have applied a logical modeling approach to model and analyze the behavioral trends and systematic trajectories that these systems exhibit without delving into their quantification. This approach, formalized by René Thomas for discrete logical modeling of Biological Regulatory Networks (BRNs and further extended in our previous studies as parametric biological linear hybrid automata (Bio-LHA, has been previously employed for the analyses of different molecular regulatory interactions occurring across various cells and microbial species. As relationships between different interacting components of a system can be simplified as positive or negative influences, we can employ the Bio-LHA framework to represent different components of the environmental system as positive or negative feedbacks. In the present study, we highlight the benefits of hybrid (discrete/continuous modeling which lead to refinements among the fore-casted behaviors in order to find out which ones are actually possible. We have taken two case studies: an interaction of three microbial species in a freshwater pond, and a more complex atmospheric system, to show the applications of the Bio-LHA methodology for the timed hybrid modeling of environmental systems. Results show that the approach using the Bio-LHA is a viable method for behavioral modeling of complex environmental systems by finding timing constraints while keeping the complexity of the model

  3. Models to predict the start of the airborne pollen season

    Science.gov (United States)

    Siniscalco, Consolata; Caramiello, Rosanna; Migliavacca, Mirco; Busetto, Lorenzo; Mercalli, Luca; Colombo, Roberto; Richardson, Andrew D.

    2015-07-01

    Aerobiological data can be used as indirect but reliable measures of flowering phenology to analyze the response of plant species to ongoing climate changes. The aims of this study are to evaluate the performance of several phenological models for predicting the pollen start of season (PSS) in seven spring-flowering trees ( Alnus glutinosa, Acer negundo, Carpinus betulus, Platanus occidentalis, Juglans nigra, Alnus viridis, and Castanea sativa) and in two summer-flowering herbaceous species ( Artemisia vulgaris and Ambrosia artemisiifolia) by using a 26-year aerobiological data set collected in Turin (Northern Italy). Data showed a reduced interannual variability of the PSS in the summer-flowering species compared to the spring-flowering ones. Spring warming models with photoperiod limitation performed best for the greater majority of the studied species, while chilling class models were selected only for the early spring flowering species. For Ambrosia and Artemisia, spring warming models were also selected as the best models, indicating that temperature sums are positively related to flowering. However, the poor variance explained by the models suggests that further analyses have to be carried out in order to develop better models for predicting the PSS in these two species. Modeling the pollen season start on a very wide data set provided a new opportunity to highlight the limits of models in elucidating the environmental factors driving the pollen season start when some factors are always fulfilled, as chilling or photoperiod or when the variance is very poor and is not explained by the models.

  4. Disease prediction models and operational readiness.

    Directory of Open Access Journals (Sweden)

    Courtney D Corley

    Full Text Available The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011. We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4, spatial (26, ecological niche (28, diagnostic or clinical (6, spread or response (9, and reviews (3. The model parameters (e.g., etiology, climatic, spatial, cultural and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological were recorded and reviewed. A component of this review is the identification of verification and validation (V&V methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology

  5. Caries risk assessment models in caries prediction

    Directory of Open Access Journals (Sweden)

    Amila Zukanović

    2013-11-01

    Full Text Available Objective. The aim of this research was to assess the efficiency of different multifactor models in caries prediction. Material and methods. Data from the questionnaire and objective examination of 109 examinees was entered into the Cariogram, Previser and Caries-Risk Assessment Tool (CAT multifactor risk assessment models. Caries risk was assessed with the help of all three models for each patient, classifying them as low, medium or high-risk patients. The development of new caries lesions over a period of three years [Decay Missing Filled Tooth (DMFT increment = difference between Decay Missing Filled Tooth Surface (DMFTS index at baseline and follow up], provided for examination of the predictive capacity concerning different multifactor models. Results. The data gathered showed that different multifactor risk assessment models give significantly different results (Friedman test: Chi square = 100.073, p=0.000. Cariogram is the model which identified the majority of examinees as medium risk patients (70%. The other two models were more radical in risk assessment, giving more unfavorable risk –profiles for patients. In only 12% of the patients did the three multifactor models assess the risk in the same way. Previser and CAT gave the same results in 63% of cases – the Wilcoxon test showed that there is no statistically significant difference in caries risk assessment between these two models (Z = -1.805, p=0.071. Conclusions. Evaluation of three different multifactor caries risk assessment models (Cariogram, PreViser and CAT showed that only the Cariogram can successfully predict new caries development in 12-year-old Bosnian children.

  6. Towards policy relevant environmental modeling: contextual validity and pragmatic models

    Science.gov (United States)

    Miles, Scott B.

    2000-01-01

    "What makes for a good model?" In various forms, this question is a question that, undoubtedly, many people, businesses, and institutions ponder with regards to their particular domain of modeling. One particular domain that is wrestling with this question is the multidisciplinary field of environmental modeling. Examples of environmental models range from models of contaminated ground water flow to the economic impact of natural disasters, such as earthquakes. One of the distinguishing claims of the field is the relevancy of environmental modeling to policy and environment-related decision-making in general. A pervasive view by both scientists and decision-makers is that a "good" model is one that is an accurate predictor. Thus, determining whether a model is "accurate" or "correct" is done by comparing model output to empirical observations. The expected outcome of this process, usually referred to as "validation" or "ground truthing," is a stamp on the model in question of "valid" or "not valid" that serves to indicate whether or not the model will be reliable before it is put into service in a decision-making context. In this paper, I begin by elaborating on the prevailing view of model validation and why this view must change. Drawing from concepts coming out of the studies of science and technology, I go on to propose a contextual view of validity that can overcome the problems associated with "ground truthing" models as an indicator of model goodness. The problem of how we talk about and determine model validity has much to do about how we perceive the utility of environmental models. In the remainder of the paper, I argue that we should adopt ideas of pragmatism in judging what makes for a good model and, in turn, developing good models. From such a perspective of model goodness, good environmental models should facilitate communication, convey—not bury or "eliminate"—uncertainties, and, thus, afford the active building of consensus decisions, instead

  7. Environmental noise and noise modelling-some aspects in Malaysian development

    International Nuclear Information System (INIS)

    Leong, Mohd Salman; Mohd Shafiek bin Hj Yaacob

    1994-01-01

    Environmental noise is of growing concern in Malaysia with the increasing awareness of the need for an environmental quality consistent with improved quality of life. While noise is one of the several elements in an Environmental Impact Assessment report, the degree of emphasis in the assessment is not as thorough as other aspects in the EIA study. The measurements, prediction (if at all any), and evaluation tended to be superficial. The paper presents a summary of correct noise descriptors and annoyance assessment parameters appropriate for the evaluation of environmental noise. The paper further highlights current inadequacies in the Environmental Quality Act for noise pollution, and annoyance assessment. Some examples of local noise pollution are presented. A discussion on environmental noise modelling is presented. Examples illustrating environmental noise modelling for a mining operation and a power station are given. It is the authors' recommendation that environmental noise modelling be made mandatory in all EIA studies such that a more definitive assessment could be realised

  8. Link Prediction via Sparse Gaussian Graphical Model

    Directory of Open Access Journals (Sweden)

    Liangliang Zhang

    2016-01-01

    Full Text Available Link prediction is an important task in complex network analysis. Traditional link prediction methods are limited by network topology and lack of node property information, which makes predicting links challenging. In this study, we address link prediction using a sparse Gaussian graphical model and demonstrate its theoretical and practical effectiveness. In theory, link prediction is executed by estimating the inverse covariance matrix of samples to overcome information limits. The proposed method was evaluated with four small and four large real-world datasets. The experimental results show that the area under the curve (AUC value obtained by the proposed method improved by an average of 3% and 12.5% compared to 13 mainstream similarity methods, respectively. This method outperforms the baseline method, and the prediction accuracy is superior to mainstream methods when using only 80% of the training set. The method also provides significantly higher AUC values when using only 60% in Dolphin and Taro datasets. Furthermore, the error rate of the proposed method demonstrates superior performance with all datasets compared to mainstream methods.

  9. The use of specialisation indices to predict vulnerability of coral-feeding butterflyfishes to environmental change

    KAUST Repository

    Lawton, Rebecca J.

    2011-07-14

    In the absence of detailed assessments of extinction risk, ecological specialisation is often used as a proxy of vulnerability to environmental disturbances and extinction risk. Numerous indices can be used to estimate specialisation; however, the utility of these different indices to predict vulnerability to future environmental change is unknown. Here we compare the performance of specialisation indices using coral-feeding butterflyfishes as a model group. Our aims were to 1) quantify the dietary preferences of three butterflyfish species across habitats with differing levels of resource availability; 2) investigate how estimates of dietary specialisation vary with the use of different specialisation indices; 3) determine which specialisation indices best inform predictions of vulnerability to environmental change; and 4) assess the utility of resource selection functions to inform predictions of vulnerability to environmental change. The relative level of dietary specialisation estimated for all three species varied when different specialisation indices were used, indicating that the choice of index can have a considerable impact upon estimates of specialisation. Specialisation indices that do not consider resource abundance may fail to distinguish species that primarily use common resources from species that actively target resources disproportionately more than they are available. Resource selection functions provided the greatest insights into the potential response of species to changes in resource availability. Examination of resource selection functions, in addition to specialisation indices, indicated that Chaetodon trifascialis was the most specialised feeder, with highly conserved dietary preferences across all sites, suggesting that this species is highly vulnerable to the impacts of climate-induced coral loss on reefs. Our results indicate that vulnerability assessments based on some specialisation indices may be misleading and the best estimates of

  10. Electrostatic ion thrusters - towards predictive modeling

    Energy Technology Data Exchange (ETDEWEB)

    Kalentev, O.; Matyash, K.; Duras, J.; Lueskow, K.F.; Schneider, R. [Ernst-Moritz-Arndt Universitaet Greifswald, D-17489 (Germany); Koch, N. [Technische Hochschule Nuernberg Georg Simon Ohm, Kesslerplatz 12, D-90489 Nuernberg (Germany); Schirra, M. [Thales Electronic Systems GmbH, Soeflinger Strasse 100, D-89077 Ulm (Germany)

    2014-02-15

    The development of electrostatic ion thrusters so far has mainly been based on empirical and qualitative know-how, and on evolutionary iteration steps. This resulted in considerable effort regarding prototype design, construction and testing and therefore in significant development and qualification costs and high time demands. For future developments it is anticipated to implement simulation tools which allow for quantitative prediction of ion thruster performance, long-term behavior and space craft interaction prior to hardware design and construction. Based on integrated numerical models combining self-consistent kinetic plasma models with plasma-wall interaction modules a new quality in the description of electrostatic thrusters can be reached. These open the perspective for predictive modeling in this field. This paper reviews the application of a set of predictive numerical modeling tools on an ion thruster model of the HEMP-T (High Efficiency Multi-stage Plasma Thruster) type patented by Thales Electron Devices GmbH. (copyright 2014 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

  11. Characterizing Attention with Predictive Network Models.

    Science.gov (United States)

    Rosenberg, M D; Finn, E S; Scheinost, D; Constable, R T; Chun, M M

    2017-04-01

    Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals' attentional abilities. While being some of the first generalizable neuromarkers of cognitive function, these models also inform our basic understanding of attention, providing empirical evidence that: (i) attention is a network property of brain computation; (ii) the functional architecture that underlies attention can be measured while people are not engaged in any explicit task; and (iii) this architecture supports a general attentional ability that is common to several laboratory-based tasks and is impaired in attention deficit hyperactivity disorder (ADHD). Looking ahead, connectivity-based predictive models of attention and other cognitive abilities and behaviors may potentially improve the assessment, diagnosis, and treatment of clinical dysfunction. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. Genetic models of homosexuality: generating testable predictions

    Science.gov (United States)

    Gavrilets, Sergey; Rice, William R

    2006-01-01

    Homosexuality is a common occurrence in humans and other species, yet its genetic and evolutionary basis is poorly understood. Here, we formulate and study a series of simple mathematical models for the purpose of predicting empirical patterns that can be used to determine the form of selection that leads to polymorphism of genes influencing homosexuality. Specifically, we develop theory to make contrasting predictions about the genetic characteristics of genes influencing homosexuality including: (i) chromosomal location, (ii) dominance among segregating alleles and (iii) effect sizes that distinguish between the two major models for their polymorphism: the overdominance and sexual antagonism models. We conclude that the measurement of the genetic characteristics of quantitative trait loci (QTLs) found in genomic screens for genes influencing homosexuality can be highly informative in resolving the form of natural selection maintaining their polymorphism. PMID:17015344

  13. Hydra, a model system for environmental studies.

    Science.gov (United States)

    Quinn, Brian; Gagné, François; Blaise, Christian

    2012-01-01

    Hydra have been extensively used for studying the teratogenic and toxic potential of numerous toxins throughout the years and are more recently growing in popularity to assess the impacts of environmental pollutants. Hydra are an appropriate bioindicator species for use in environmental assessment owing to their easily measurable physical (morphology), biochemical (xenobiotic biotransformation; oxidative stress), behavioural (feeding) and reproductive (sexual and asexual) endpoints. Hydra also possess an unparalleled ability to regenerate, allowing the assessment of teratogenic compounds and the impact of contaminants on stem cells. Importantly, Hydra are ubiquitous throughout freshwater environments and relatively easy to culture making them appropriate for use in small scale bioassay systems. Hydra have been used to assess the environmental impacts of numerous environmental pollutants including metals, organic toxicants (including pharmaceuticals and endocrine disrupting compounds), nanomaterials and industrial and municipal effluents. They have been found to be among the most sensitive animals tested for metals and certain effluents, comparing favourably with more standardised toxicity tests. Despite their lack of use in formalised monitoring programmes, Hydra have been extensively used and are regarded as a model organism in aquatic toxicology.

  14. Integrated environmental modeling: a vision and roadmap for the future

    Science.gov (United States)

    Laniak, Gerard F.; Olchin, Gabriel; Goodall, Jonathan; Voinov, Alexey; Hill, Mary; Glynn, Pierre; Whelan, Gene; Geller, Gary; Quinn, Nigel; Blind, Michiel; Peckham, Scott; Reaney, Sim; Gaber, Noha; Kennedy, Philip R.; Hughes, Andrew

    2013-01-01

    Integrated environmental modeling (IEM) is inspired by modern environmental problems, decisions, and policies and enabled by transdisciplinary science and computer capabilities that allow the environment to be considered in a holistic way. The problems are characterized by the extent of the environmental system involved, dynamic and interdependent nature of stressors and their impacts, diversity of stakeholders, and integration of social, economic, and environmental considerations. IEM provides a science-based structure to develop and organize relevant knowledge and information and apply it to explain, explore, and predict the behavior of environmental systems in response to human and natural sources of stress. During the past several years a number of workshops were held that brought IEM practitioners together to share experiences and discuss future needs and directions. In this paper we organize and present the results of these discussions. IEM is presented as a landscape containing four interdependent elements: applications, science, technology, and community. The elements are described from the perspective of their role in the landscape, current practices, and challenges that must be addressed. Workshop participants envision a global scale IEM community that leverages modern technologies to streamline the movement of science-based knowledge from its sources in research, through its organization into databases and models, to its integration and application for problem solving purposes. Achieving this vision will require that the global community of IEM stakeholders transcend social, and organizational boundaries and pursue greater levels of collaboration. Among the highest priorities for community action are the development of standards for publishing IEM data and models in forms suitable for automated discovery, access, and integration; education of the next generation of environmental stakeholders, with a focus on transdisciplinary research, development, and

  15. Proceedings of the international symposium on environmental modeling and radioecology

    International Nuclear Information System (INIS)

    Hisamatsu, Shun'ichi; Ueda, Shinji; Kakiuchi, Hideki; Akata, Naofumi

    2007-03-01

    Environmental models using radioecological parameters are essential for predicting the behavior of radionuclides in the environment. Due to the complex behaviors of radionuclides in the environment, simplified models and parameters with ample margins are used for the safety assessment of nuclear facilities to ensure the safety of people in the surrounding area. As a consequence, radiation exposure doses from the radionuclides have generally been overestimated. Information with more precise predictions of the fate of the radionuclides in the environment and realistic radiation dose estimates are necessary for the public acceptance of nuclear facilities. Realistic dose estimates require continuous improvement of the models and their parameters as well as using state of the art modeling techniques and radioecological knowledge. The first commercial nuclear fuel reprocessing plant in Japan has been built in Rokkasho, Aomori, and the Institute for Environmental Sciences was established for the purpose of assessing the effects of radionuclides released from the plant. Test runs by the plant using actual spent nuclear fuel began in March 2006. With commercial operation soon to begin, there is increasing concern regarding the behavior of radionuclides in the environment. This was a good time to hold a symposium here in Rokkasho to discuss recent progress in the field of environmental modeling and studies of the behaviors of radionuclides in the environment. The exchange of up-to-date information between modelers and experiments was an important aspect of the symposium. The symposium featured 26 oral lectures and 32 poster presentations. The 57 of the presented papers are indexed individually. (J.P.N.)

  16. Crops Models for Varying Environmental Conditions

    Science.gov (United States)

    Jones, Harry; Cavazzoni, James; Keas, Paul

    2001-01-01

    New variable environment Modified Energy Cascade (MEC) crop models were developed for all the Advanced Life Support (ALS) candidate crops and implemented in SIMULINK. The MEC models are based on the Volk, Bugbee, and Wheeler Energy Cascade (EC) model and are derived from more recent Top-Level Energy Cascade (TLEC) models. The MEC models simulate crop plant responses to day-to-day changes in photosynthetic photon flux, photoperiod, carbon dioxide level, temperature, and relative humidity. The original EC model allows changes in light energy but uses a less accurate linear approximation. The simulation outputs of the new MEC models for constant nominal environmental conditions are very similar to those of earlier EC models that use parameters produced by the TLEC models. There are a few differences. The new MEC models allow setting the time for seed emergence, have realistic exponential canopy growth, and have corrected harvest dates for potato and tomato. The new MEC models indicate that the maximum edible biomass per meter squared per day is produced at the maximum allowed carbon dioxide level, the nominal temperatures, and the maximum light input. Reducing the carbon dioxide level from the maximum to the minimum allowed in the model reduces crop production significantly. Increasing temperature decreases production more than it decreases the time to harvest, so productivity in edible biomass per meter squared per day is greater at nominal than maximum temperatures, The productivity in edible biomass per meter squared per day is greatest at the maximum light energy input allowed in the model, but the edible biomass produced per light energy input unit is lower than at nominal light levels. Reducing light levels increases light and power use efficiency. The MEC models suggest we can adjust the light energy day-to- day to accommodate power shortages or Lise excess power while monitoring and controlling edible biomass production.

  17. A statistical model for predicting muscle performance

    Science.gov (United States)

    Byerly, Diane Leslie De Caix

    The objective of these studies was to develop a capability for predicting muscle performance and fatigue to be utilized for both space- and ground-based applications. To develop this predictive model, healthy test subjects performed a defined, repetitive dynamic exercise to failure using a Lordex spinal machine. Throughout the exercise, surface electromyography (SEMG) data were collected from the erector spinae using a Mega Electronics ME3000 muscle tester and surface electrodes placed on both sides of the back muscle. These data were analyzed using a 5th order Autoregressive (AR) model and statistical regression analysis. It was determined that an AR derived parameter, the mean average magnitude of AR poles, significantly correlated with the maximum number of repetitions (designated Rmax) that a test subject was able to perform. Using the mean average magnitude of AR poles, a test subject's performance to failure could be predicted as early as the sixth repetition of the exercise. This predictive model has the potential to provide a basis for improving post-space flight recovery, monitoring muscle atrophy in astronauts and assessing the effectiveness of countermeasures, monitoring astronaut performance and fatigue during Extravehicular Activity (EVA) operations, providing pre-flight assessment of the ability of an EVA crewmember to perform a given task, improving the design of training protocols and simulations for strenuous International Space Station assembly EVA, and enabling EVA work task sequences to be planned enhancing astronaut performance and safety. Potential ground-based, medical applications of the predictive model include monitoring muscle deterioration and performance resulting from illness, establishing safety guidelines in the industry for repetitive tasks, monitoring the stages of rehabilitation for muscle-related injuries sustained in sports and accidents, and enhancing athletic performance through improved training protocols while reducing

  18. Knowledge-Based Environmental Context Modeling

    Science.gov (United States)

    Pukite, P. R.; Challou, D. J.

    2017-12-01

    As we move from the oil-age to an energy infrastructure based on renewables, the need arises for new educational tools to support the analysis of geophysical phenomena and their behavior and properties. Our objective is to present models of these phenomena to make them amenable for incorporation into more comprehensive analysis contexts. Starting at the level of a college-level computer science course, the intent is to keep the models tractable and therefore practical for student use. Based on research performed via an open-source investigation managed by DARPA and funded by the Department of Interior [1], we have adapted a variety of physics-based environmental models for a computer-science curriculum. The original research described a semantic web architecture based on patterns and logical archetypal building-blocks (see figure) well suited for a comprehensive environmental modeling framework. The patterns span a range of features that cover specific land, atmospheric and aquatic domains intended for engineering modeling within a virtual environment. The modeling engine contained within the server relied on knowledge-based inferencing capable of supporting formal terminology (through NASA JPL's Semantic Web for Earth and Environmental Technology (SWEET) ontology and a domain-specific language) and levels of abstraction via integrated reasoning modules. One of the key goals of the research was to simplify models that were ordinarily computationally intensive to keep them lightweight enough for interactive or virtual environment contexts. The breadth of the elements incorporated is well-suited for learning as the trend toward ontologies and applying semantic information is vital for advancing an open knowledge infrastructure. As examples of modeling, we have covered such geophysics topics as fossil-fuel depletion, wind statistics, tidal analysis, and terrain modeling, among others. Techniques from the world of computer science will be necessary to promote efficient

  19. Prediction models : the right tool for the right problem

    NARCIS (Netherlands)

    Kappen, Teus H.; Peelen, Linda M.

    2016-01-01

    PURPOSE OF REVIEW: Perioperative prediction models can help to improve personalized patient care by providing individual risk predictions to both patients and providers. However, the scientific literature on prediction model development and validation can be quite technical and challenging to

  20. Neuro-fuzzy modeling in bankruptcy prediction

    Directory of Open Access Journals (Sweden)

    Vlachos D.

    2003-01-01

    Full Text Available For the past 30 years the problem of bankruptcy prediction had been thoroughly studied. From the paper of Altman in 1968 to the recent papers in the '90s, the progress of prediction accuracy was not satisfactory. This paper investigates an alternative modeling of the system (firm, combining neural networks and fuzzy controllers, i.e. using neuro-fuzzy models. Classical modeling is based on mathematical models that describe the behavior of the firm under consideration. The main idea of fuzzy control, on the other hand, is to build a model of a human control expert who is capable of controlling the process without thinking in a mathematical model. This control expert specifies his control action in the form of linguistic rules. These control rules are translated into the framework of fuzzy set theory providing a calculus, which can stimulate the behavior of the control expert and enhance its performance. The accuracy of the model is studied using datasets from previous research papers.

  1. Life Prediction Issues in Thermal/Environmental Barrier Coatings in Ceramic Matrix Composites

    Science.gov (United States)

    Shah, Ashwin R.; Brewer, David N.; Murthy, Pappu L. N.

    2001-01-01

    Issues and design requirements for the environmental barrier coating (EBC)/thermal barrier coating (TBC) life that are general and those specific to the NASA Ultra-Efficient Engine Technology (UEET) development program have been described. The current state and trend of the research, methods in vogue related to the failure analysis, and long-term behavior and life prediction of EBCITBC systems are reported. Also, the perceived failure mechanisms, variables, and related uncertainties governing the EBCITBC system life are summarized. A combined heat transfer and structural analysis approach based on the oxidation kinetics using the Arrhenius theory is proposed to develop a life prediction model for the EBC/TBC systems. Stochastic process-based reliability approach that includes the physical variables such as gas pressure, temperature, velocity, moisture content, crack density, oxygen content, etc., is suggested. Benefits of the reliability-based approach are also discussed in the report.

  2. Physio-environmental sensing and live modeling.

    Science.gov (United States)

    Castiglione, Filippo; Diaz, Vanessa; Gaggioli, Andrea; Liò, Pietro; Mazzà, Claudia; Merelli, Emanuela; Meskers, Carel G M; Pappalardo, Francesco; von Ammon, Rainer

    2013-01-30

    In daily life, humans are constantly interacting with their environment. Evidence is emerging that this interaction is a very important modulator of health and well-being, even more so in our rapidly ageing society. Information and communication technology lies at the heart of the human health care revolution. It cannot remain acceptable to use out of date data analysis and predictive algorithms when superior alternatives exist. Communication network speed, high penetration of home broadband, availability of various mobile network options, together with the available detailed biological data for individuals, are producing promising advances in computerized systems that will turn information on human-environment interactions into actual knowledge with the potential to help make medical and lifestyle decisions. We introduced and discussed a key scenario in which hardware and software technologies capable of simultaneously sensing physiological and environmental signals process health care data in real-time to issue alarms, warnings, or simple recommendations to the patient or carers.

  3. The predictive performance and stability of six species distribution models.

    Directory of Open Access Journals (Sweden)

    Ren-Yan Duan

    Full Text Available Predicting species' potential geographical range by species distribution models (SDMs is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs.We collected the distribution data of five common tree species (Pinus massoniana, Betula platyphylla, Quercus wutaishanica, Quercus mongolica and Quercus variabilis and simulated their potential distribution area using 13 environmental variables and six widely used SDMs: BIOCLIM, DOMAIN, MAHAL, RF, MAXENT, and SVM. Each model run was repeated 100 times (trials. We compared the predictive performance by testing the consistency between observations and simulated distributions and assessed the stability by the standard deviation, coefficient of variation, and the 99% confidence interval of Kappa and AUC values.The mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (p<0.05, while the associated standard deviations and coefficients of variation were larger for BIOCLIM and DOMAIN trials (p<0.05, and the 99% confidence intervals for AUC and Kappa values were narrower for MAHAL, RF, MAXENT, and SVM. Compared to BIOCLIM and DOMAIN, other SDMs (MAHAL, RF, MAXENT, and SVM had higher prediction accuracy, smaller confidence intervals, and were more stable and less affected by the random variable (randomly selected pseudo-absence points.According to the prediction performance and stability of SDMs, we can divide these six SDMs into two categories: a high performance and stability group including MAHAL, RF, MAXENT, and SVM, and a low performance and stability group consisting of BIOCLIM, and DOMAIN. We highlight that choosing appropriate SDMs to address a specific problem is an important part of the modeling process.

  4. Error Estimation of An Ensemble Statistical Seasonal Precipitation Prediction Model

    Science.gov (United States)

    Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Gui-Long

    2001-01-01

    This NASA Technical Memorandum describes an optimal ensemble canonical correlation forecasting model for seasonal precipitation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. Since new CCA scheme is derived for continuous fields of predictor and predictand, an area-factor is automatically included. Thus our model is an improvement of the spectral CCA scheme of Barnett and Preisendorfer. The improvements include (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States (US) precipitation field. The predictor is the sea surface temperature (SST). The US Climate Prediction Center's reconstructed SST is used as the predictor's historical data. The US National Center for Environmental Prediction's optimally interpolated precipitation (1951-2000) is used as the predictand's historical data. Our forecast experiments show that the new ensemble canonical correlation scheme renders a reasonable forecasting skill. For example, when using September-October-November SST to predict the next season December-January-February precipitation, the spatial pattern correlation between the observed and predicted are positive in 46 years among the 50 years of experiments. The positive correlations are close to or greater than 0.4 in 29 years, which indicates excellent performance of the forecasting model. The forecasting skill can be further enhanced when several predictors are used.

  5. Disease Prediction Models and Operational Readiness

    Energy Technology Data Exchange (ETDEWEB)

    Corley, Courtney D.; Pullum, Laura L.; Hartley, David M.; Benedum, Corey M.; Noonan, Christine F.; Rabinowitz, Peter M.; Lancaster, Mary J.

    2014-03-19

    INTRODUCTION: The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. One of the primary goals of this research was to characterize the viability of biosurveillance models to provide operationally relevant information for decision makers to identify areas for future research. Two critical characteristics differentiate this work from other infectious disease modeling reviews. First, we reviewed models that attempted to predict the disease event, not merely its transmission dynamics. Second, we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). Methods: We searched dozens of commercial and government databases and harvested Google search results for eligible models utilizing terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche-modeling, The publication date of search results returned are bound by the dates of coverage of each database and the date in which the search was performed, however all searching was completed by December 31, 2010. This returned 13,767 webpages and 12,152 citations. After de-duplication and removal of extraneous material, a core collection of 6,503 items was established and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. Next, PNNL’s IN-SPIRE visual analytics software was used to cross-correlate these publications with the definition for a biosurveillance model resulting in the selection of 54 documents that matched the criteria resulting Ten of these documents, However, dealt purely with disease spread models, inactivation of bacteria, or the modeling of human immune system responses to pathogens rather than predicting disease events. As a result, we systematically reviewed 44 papers and the

  6. Worst-case prediction of normal operating containment temperatures for environmentally qualified equipment

    International Nuclear Information System (INIS)

    Krasnopoler, M.J.; Sundergill, J.E.

    1991-01-01

    Due to issues raised in NRC Information Notice No. 87-65, a southern US nuclear plant was concerned about thermal aging of environmentally qualified (EQ) equipment located in areas of elevated containment temperatures. A method to predict the worst-case monthly temperatures at various zones in the containment and calculate the qualified life using this monthly temperature was developed. Temperatures were predicted for twenty locations inside the containment. Concern about the qualified life of EQ equipment resulted from normal operating temperatures above 120F in several areas of the containment, especially during the summer. At a few locations, the temperature exceeded 140F. Also, NRC Information Notice No. 89-30 reported high containment temperatures at three other nuclear power plants. The predicted temperatures were based on a one-year containment temperature monitoring program. The monitors included permanent temperature monitors required by the Technical Specifications and temporary monitors installed specifically for this program. The temporary monitors were installed near EQ equipment in the expected worst-case areas based on design and operating experience. A semi-empirical model that combined physical and statistical models was developed. The physical model was an overall energy balance for the containment. The statistical model consists of several linear regressions that conservatively relate the monitor temperatures to the bulk containment temperature. The resulting semi-empirical model predicts the worst-case monthly service temperatures at the location of each of the containment temperature monitors. The monthly temperatures are the maximum expected because they are based on the historically worst-case atmospheric data

  7. Nonlinear model predictive control theory and algorithms

    CERN Document Server

    Grüne, Lars

    2017-01-01

    This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...

  8. Predicting on-site environmental impacts of municipal engineering works

    OpenAIRE

    Gangolells Solanellas, Marta; Casals Casanova, Miquel; Forcada Matheu, Núria; Macarulla Martí, Marcel

    2014-01-01

    The research findings fill a gap in the body of knowledge by presenting an effective way to evaluate the significance of on-site environmental impacts of municipal engineering works prior to the construction stage. First, 42 on-site environmental impacts of municipal engineering works were identified by means of a process-oriented approach. Then, 46 indicators and their corresponding significance limits were determined on the basis of a statistical analysis of 25 new-build and remodelling mun...

  9. A predictive model for dimensional errors in fused deposition modeling

    DEFF Research Database (Denmark)

    Stolfi, A.

    2015-01-01

    values of L (0.254 mm, 0.330 mm) was produced by comparing predicted values with external face-to-face measurements. After removing outliers, the results show that the developed two-parameter model can serve as tool for modeling the FDM dimensional behavior in a wide range of deposition angles....

  10. A predictive model for dimensional errors in fused deposition modeling

    DEFF Research Database (Denmark)

    Stolfi, A.

    2015-01-01

    This work concerns the effect of deposition angle (a) and layer thickness (L) on the dimensional performance of FDM parts using a predictive model based on the geometrical description of the FDM filament profile. An experimental validation over the whole a range from 0° to 177° at 3° steps and two...... values of L (0.254 mm, 0.330 mm) was produced by comparing predicted values with external face-to-face measurements. After removing outliers, the results show that the developed two-parameter model can serve as tool for modeling the FDM dimensional behavior in a wide range of deposition angles....

  11. Climate Predictions: The Chaos and Complexity in Climate Models

    Directory of Open Access Journals (Sweden)

    D. T. Mihailović

    2014-01-01

    Full Text Available Some issues which are relevant for the recent state in climate modeling have been considered. A detailed overview of literature related to this subject is given. The concept in modeling of climate, as a complex system, seen through Gödel’s theorem and Rosen’s definition of complexity and predictability is discussed. Occurrence of chaos in computing the environmental interface temperature from the energy balance equation given in a difference form is pointed out. A coupled system of equations, often used in climate models, was analyzed. It is shown that the Lyapunov exponent mostly has positive values allowing presence of chaos in this system. The horizontal energy exchange between environmental interfaces, which is described by the dynamics of driven coupled oscillators, was analyzed. Their behavior and synchronization, when a perturbation is introduced in the system, as a function of the coupling parameter, the logistic parameter, and the parameter of exchange, were studied calculating the Lyapunov exponent under simulations with the closed contour of N=100 environmental interfaces. Finally, we have explored possible differences in complexities of two global and two regional climate models using their air temperature and precipitation output time series. The complexities were obtained with the algorithm for calculating the Kolmogorov complexity.

  12. Predictive Modeling in Actinide Chemistry and Catalysis

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Ping [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-05-16

    These are slides from a presentation on predictive modeling in actinide chemistry and catalysis. The following topics are covered in these slides: Structures, bonding, and reactivity (bonding can be quantified by optical probes and theory, and electronic structures and reaction mechanisms of actinide complexes); Magnetic resonance properties (transition metal catalysts with multi-nuclear centers, and NMR/EPR parameters); Moving to more complex systems (surface chemistry of nanomaterials, and interactions of ligands with nanoparticles); Path forward and conclusions.

  13. Predictive modelling of evidence informed teaching

    OpenAIRE

    Zhang, Dell; Brown, C.

    2017-01-01

    In this paper, we analyse the questionnaire survey data collected from 79 English primary schools about the situation of evidence informed teaching, where the evidences could come from research journals or conferences. Specifically, we build a predictive model to see what external factors could help to close the gap between teachers’ belief and behaviour in evidence informed teaching, which is the first of its kind to our knowledge. The major challenge, from the data mining perspective, is th...

  14. A Predictive Model for Cognitive Radio

    Science.gov (United States)

    2006-09-14

    response in a given situation. Vadde et al. interest and produce a model for prediction of the response. have applied response surface methodology and...34 2000. [3] K. K. Vadde and V. R. Syrotiuk, "Factor interaction on service configurations to those that best meet our communication delivery in mobile ad...resulting set of configurations randomly or apply additional 2004. screening criteria. [4] K. K. Vadde , M.-V. R. Syrotiuk, and D. C. Montgomery

  15. Tectonic predictions with mantle convection models

    Science.gov (United States)

    Coltice, Nicolas; Shephard, Grace E.

    2018-04-01

    Over the past 15 yr, numerical models of convection in Earth's mantle have made a leap forward: they can now produce self-consistent plate-like behaviour at the surface together with deep mantle circulation. These digital tools provide a new window into the intimate connections between plate tectonics and mantle dynamics, and can therefore be used for tectonic predictions, in principle. This contribution explores this assumption. First, initial conditions at 30, 20, 10 and 0 Ma are generated by driving a convective flow with imposed plate velocities at the surface. We then compute instantaneous mantle flows in response to the guessed temperature fields without imposing any boundary conditions. Plate boundaries self-consistently emerge at correct locations with respect to reconstructions, except for small plates close to subduction zones. As already observed for other types of instantaneous flow calculations, the structure of the top boundary layer and upper-mantle slab is the dominant character that leads to accurate predictions of surface velocities. Perturbations of the rheological parameters have little impact on the resulting surface velocities. We then compute fully dynamic model evolution from 30 and 10 to 0 Ma, without imposing plate boundaries or plate velocities. Contrary to instantaneous calculations, errors in kinematic predictions are substantial, although the plate layout and kinematics in several areas remain consistent with the expectations for the Earth. For these calculations, varying the rheological parameters makes a difference for plate boundary evolution. Also, identified errors in initial conditions contribute to first-order kinematic errors. This experiment shows that the tectonic predictions of dynamic models over 10 My are highly sensitive to uncertainties of rheological parameters and initial temperature field in comparison to instantaneous flow calculations. Indeed, the initial conditions and the rheological parameters can be good enough

  16. Innovative mathematical modeling in environmental remediation

    International Nuclear Information System (INIS)

    Yeh, Gour T.; Gwo, Jin Ping; Siegel, Malcolm D.; Li, Ming-Hsu; Fang, Yilin; Zhang, Fan; Luo, Wensui; Yabusaki, Steven B.

    2013-01-01

    There are two different ways to model reactive transport: ad hoc and innovative reaction-based approaches. The former, such as the Kd simplification of adsorption, has been widely employed by practitioners, while the latter has been mainly used in scientific communities for elucidating mechanisms of biogeochemical transport processes. It is believed that innovative mechanistic-based models could serve as protocols for environmental remediation as well. This paper reviews the development of a mechanistically coupled fluid flow, thermal transport, hydrologic transport, and reactive biogeochemical model and example-applications to environmental remediation problems. Theoretical bases are sufficiently described. Four example problems previously carried out are used to demonstrate how numerical experimentation can be used to evaluate the feasibility of different remediation approaches. The first one involved the application of a 56-species uranium tailing problem to the Melton Branch Subwatershed at Oak Ridge National Laboratory (ORNL) using the parallel version of the model. Simulations were made to demonstrate the potential mobilization of uranium and other chelating agents in the proposed waste disposal site. The second problem simulated laboratory-scale system to investigate the role of natural attenuation in potential off-site migration of uranium from uranium mill tailings after restoration. It showed inadequacy of using a single Kd even for a homogeneous medium. The third example simulated laboratory experiments involving extremely high concentrations of uranium, technetium, aluminum, nitrate, and toxic metals (e.g.,Ni, Cr, Co). The fourth example modeled microbially-mediated immobilization of uranium in an unconfined aquifer using acetate amendment in a field-scale experiment. The purposes of these modeling studies were to simulate various mechanisms of mobilization and immobilization of radioactive wastes and to illustrate how to apply reactive transport models

  17. Innovative mathematical modeling in environmental remediation

    Energy Technology Data Exchange (ETDEWEB)

    Yeh, Gour T. [Taiwan Typhoon and Flood Research Institute (Taiwan); National Central Univ. (Taiwan); Univ. of Central Florida (United States); Gwo, Jin Ping [Nuclear Regulatory Commission (NRC), Rockville, MD (United States); Siegel, Malcolm D. [Sandia National Laboratories, Albuquerque, NM (United States); Li, Ming-Hsu [National Central Univ. (Taiwan); ; Fang, Yilin [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Zhang, Fan [Inst. of Tibetan Plateau Research, Chinese Academy of Sciences (China); Luo, Wensui [Inst. of Tibetan Plateau Research, Chinese Academy of Sciences (China); Yabusaki, Steven B. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

    2013-05-01

    There are two different ways to model reactive transport: ad hoc and innovative reaction-based approaches. The former, such as the Kd simplification of adsorption, has been widely employed by practitioners, while the latter has been mainly used in scientific communities for elucidating mechanisms of biogeochemical transport processes. It is believed that innovative mechanistic-based models could serve as protocols for environmental remediation as well. This paper reviews the development of a mechanistically coupled fluid flow, thermal transport, hydrologic transport, and reactive biogeochemical model and example-applications to environmental remediation problems. Theoretical bases are sufficiently described. Four example problems previously carried out are used to demonstrate how numerical experimentation can be used to evaluate the feasibility of different remediation approaches. The first one involved the application of a 56-species uranium tailing problem to the Melton Branch Subwatershed at Oak Ridge National Laboratory (ORNL) using the parallel version of the model. Simulations were made to demonstrate the potential mobilization of uranium and other chelating agents in the proposed waste disposal site. The second problem simulated laboratory-scale system to investigate the role of natural attenuation in potential off-site migration of uranium from uranium mill tailings after restoration. It showed inadequacy of using a single Kd even for a homogeneous medium. The third example simulated laboratory experiments involving extremely high concentrations of uranium, technetium, aluminum, nitrate, and toxic metals (e.g.,Ni, Cr, Co).The fourth example modeled microbially-mediated immobilization of uranium in an unconfined aquifer using acetate amendment in a field-scale experiment. The purposes of these modeling studies were to simulate various mechanisms of mobilization and immobilization of radioactive wastes and to illustrate how to apply reactive transport models

  18. S-World: a Global Soil Map for Environmental Modelling

    NARCIS (Netherlands)

    Stoorvogel, J.J.; Bakkenes, Michel; Temme, A.J.A.M.; Batjes, N.H.; Brink, Ten Ben

    2017-01-01

    The research community increasingly analyses global environmental problems like climate change and desertification with models. These global environmental modelling studies require global, high resolution, spatially exhaustive, and quantitative data describing the soil profile. This study aimed to

  19. Predictive Modeling of the CDRA 4BMS

    Science.gov (United States)

    Coker, Robert F.; Knox, James C.

    2016-01-01

    As part of NASA's Advanced Exploration Systems (AES) program and the Life Support Systems Project (LSSP), fully predictive models of the Four Bed Molecular Sieve (4BMS) of the Carbon Dioxide Removal Assembly (CDRA) on the International Space Station (ISS) are being developed. This virtual laboratory will be used to help reduce mass, power, and volume requirements for future missions. In this paper we describe current and planned modeling developments in the area of carbon dioxide removal to support future crewed Mars missions as well as the resolution of anomalies observed in the ISS CDRA.

  20. Towards the development of an environmental rule-based model for ...

    African Journals Online (AJOL)

    The purpose of modelling was (1) to test the optimal environmental window hypothesis (characterised by 'dome-shape' formats) for the variables studied and (2) to evaluate the application of the model as a tool for recruitment prediction. The model was run under different conditions and the following results were obtained: ...

  1. Predictive Modeling by the Cerebellum Improves Proprioception

    Science.gov (United States)

    Bhanpuri, Nasir H.; Okamura, Allison M.

    2013-01-01

    Because sensation is delayed, real-time movement control requires not just sensing, but also predicting limb position, a function hypothesized for the cerebellum. Such cerebellar predictions could contribute to perception of limb position (i.e., proprioception), particularly when a person actively moves the limb. Here we show that human cerebellar patients have proprioceptive deficits compared with controls during active movement, but not when the arm is moved passively. Furthermore, when healthy subjects move in a force field with unpredictable dynamics, they have active proprioceptive deficits similar to cerebellar patients. Therefore, muscle activity alone is likely insufficient to enhance proprioception and predictability (i.e., an internal model of the body and environment) is important for active movement to benefit proprioception. We conclude that cerebellar patients have an active proprioceptive deficit consistent with disrupted movement prediction rather than an inability to generally enhance peripheral proprioceptive signals during action and suggest that active proprioceptive deficits should be considered a fundamental cerebellar impairment of clinical importance. PMID:24005283

  2. Evaluation of Sub-Zonal Airflow Models for the Prediction of Local Interior Boundary Conditions

    DEFF Research Database (Denmark)

    Steskens, Paul W. M. H.; Janssen, Hans; Rode, Carsten

    2013-01-01

    and applicability of the sub-zonal airflow model to predict the local indoor environmental conditions, as well as the local surface transfer coefficients near building components. Two test cases were analyzed for, respectively, natural and forced convection in a room. The simulation results predicted from the sub...

  3. Gamma-Ray Pulsars Models and Predictions

    CERN Document Server

    Harding, A K

    2001-01-01

    Pulsed emission from gamma-ray pulsars originates inside the magnetosphere, from radiation by charged particles accelerated near the magnetic poles or in the outer gaps. In polar cap models, the high energy spectrum is cut off by magnetic pair production above an energy that is dependent on the local magnetic field strength. While most young pulsars with surface fields in the range B = 10^{12} - 10^{13} G are expected to have high energy cutoffs around several GeV, the gamma-ray spectra of old pulsars having lower surface fields may extend to 50 GeV. Although the gamma-ray emission of older pulsars is weaker, detecting pulsed emission at high energies from nearby sources would be an important confirmation of polar cap models. Outer gap models predict more gradual high-energy turnovers at around 10 GeV, but also predict an inverse Compton component extending to TeV energies. Detection of pulsed TeV emission, which would not survive attenuation at the polar caps, is thus an important test of outer gap models. N...

  4. A prediction model for Clostridium difficile recurrence

    Directory of Open Access Journals (Sweden)

    Francis D. LaBarbera

    2015-02-01

    Full Text Available Background: Clostridium difficile infection (CDI is a growing problem in the community and hospital setting. Its incidence has been on the rise over the past two decades, and it is quickly becoming a major concern for the health care system. High rate of recurrence is one of the major hurdles in the successful treatment of C. difficile infection. There have been few studies that have looked at patterns of recurrence. The studies currently available have shown a number of risk factors associated with C. difficile recurrence (CDR; however, there is little consensus on the impact of most of the identified risk factors. Methods: Our study was a retrospective chart review of 198 patients diagnosed with CDI via Polymerase Chain Reaction (PCR from February 2009 to Jun 2013. In our study, we decided to use a machine learning algorithm called the Random Forest (RF to analyze all of the factors proposed to be associated with CDR. This model is capable of making predictions based on a large number of variables, and has outperformed numerous other models and statistical methods. Results: We came up with a model that was able to accurately predict the CDR with a sensitivity of 83.3%, specificity of 63.1%, and area under curve of 82.6%. Like other similar studies that have used the RF model, we also had very impressive results. Conclusions: We hope that in the future, machine learning algorithms, such as the RF, will see a wider application.

  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. Evaluating predictive models of software quality

    International Nuclear Information System (INIS)

    Ciaschini, V; Canaparo, M; Ronchieri, E; Salomoni, D

    2014-01-01

    Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.

  7. A generative model for predicting terrorist incidents

    Science.gov (United States)

    Verma, Dinesh C.; Verma, Archit; Felmlee, Diane; Pearson, Gavin; Whitaker, Roger

    2017-05-01

    A major concern in coalition peace-support operations is the incidence of terrorist activity. In this paper, we propose a generative model for the occurrence of the terrorist incidents, and illustrate that an increase in diversity, as measured by the number of different social groups to which that an individual belongs, is inversely correlated with the likelihood of a terrorist incident in the society. A generative model is one that can predict the likelihood of events in new contexts, as opposed to statistical models which are used to predict the future incidents based on the history of the incidents in an existing context. Generative models can be useful in planning for persistent Information Surveillance and Reconnaissance (ISR) since they allow an estimation of regions in the theater of operation where terrorist incidents may arise, and thus can be used to better allocate the assignment and deployment of ISR assets. In this paper, we present a taxonomy of terrorist incidents, identify factors related to occurrence of terrorist incidents, and provide a mathematical analysis calculating the likelihood of occurrence of terrorist incidents in three common real-life scenarios arising in peace-keeping operations

  8. PREDICTION MODELS OF GRAIN YIELD AND CHARACTERIZATION

    Directory of Open Access Journals (Sweden)

    Narciso Ysac Avila Serrano

    2009-06-01

    Full Text Available With the objective to characterize the grain yield of five cowpea cultivars and to find linear regression models to predict it, a study was developed in La Paz, Baja California Sur, Mexico. A complete randomized blocks design was used. Simple and multivariate analyses of variance were carried out using the canonical variables to characterize the cultivars. The variables cluster per plant, pods per plant, pods per cluster, seeds weight per plant, seeds hectoliter weight, 100-seed weight, seeds length, seeds wide, seeds thickness, pods length, pods wide, pods weight, seeds per pods, and seeds weight per pods, showed significant differences (P≤ 0.05 among cultivars. Paceño and IT90K-277-2 cultivars showed the higher seeds weight per plant. The linear regression models showed correlation coefficients ≥0.92. In these models, the seeds weight per plant, pods per cluster, pods per plant, cluster per plant and pods length showed significant correlations (P≤ 0.05. In conclusion, the results showed that grain yield differ among cultivars and for its estimation, the prediction models showed determination coefficients highly dependable.

  9. Development of Reliability Based Life Prediction Methods for Thermal and Environmental Barrier Coatings in Ceramic Matrix Composites

    Science.gov (United States)

    Shah, Ashwin

    2001-01-01

    Literature survey related to the EBC/TBC (environmental barrier coating/thermal barrier coating) fife models, failure mechanisms in EBC/TBC and the initial work plan for the proposed EBC/TBC life prediction methods development was developed as well as the finite element model for the thermal/stress analysis of the GRC-developed EBC system was prepared. Technical report for these activities is given in the subsequent sections.

  10. Predictive Models for Normal Fetal Cardiac Structures.

    Science.gov (United States)

    Krishnan, Anita; Pike, Jodi I; McCarter, Robert; Fulgium, Amanda L; Wilson, Emmanuel; Donofrio, Mary T; Sable, Craig A

    2016-12-01

    Clinicians rely on age- and size-specific measures of cardiac structures to diagnose cardiac disease. No universally accepted normative data exist for fetal cardiac structures, and most fetal cardiac centers do not use the same standards. The aim of this study was to derive predictive models for Z scores for 13 commonly evaluated fetal cardiac structures using a large heterogeneous population of fetuses without structural cardiac defects. The study used archived normal fetal echocardiograms in representative fetuses aged 12 to 39 weeks. Thirteen cardiac dimensions were remeasured by a blinded echocardiographer from digitally stored clips. Studies with inadequate imaging views were excluded. Regression models were developed to relate each dimension to estimated gestational age (EGA) by dates, biparietal diameter, femur length, and estimated fetal weight by the Hadlock formula. Dimension outcomes were transformed (e.g., using the logarithm or square root) as necessary to meet the normality assumption. Higher order terms, quadratic or cubic, were added as needed to improve model fit. Information criteria and adjusted R 2 values were used to guide final model selection. Each Z-score equation is based on measurements derived from 296 to 414 unique fetuses. EGA yielded the best predictive model for the majority of dimensions; adjusted R 2 values ranged from 0.72 to 0.893. However, each of the other highly correlated (r > 0.94) biometric parameters was an acceptable surrogate for EGA. In most cases, the best fitting model included squared and cubic terms to introduce curvilinearity. For each dimension, models based on EGA provided the best fit for determining normal measurements of fetal cardiac structures. Nevertheless, other biometric parameters, including femur length, biparietal diameter, and estimated fetal weight provided results that were nearly as good. Comprehensive Z-score results are available on the basis of highly predictive models derived from gestational

  11. Complex Environmental Data Modelling Using Adaptive General Regression Neural Networks

    Science.gov (United States)

    Kanevski, Mikhail

    2015-04-01

    The research deals with an adaptation and application of Adaptive General Regression Neural Networks (GRNN) to high dimensional environmental data. GRNN [1,2,3] are efficient modelling tools both for spatial and temporal data and are based on nonparametric kernel methods closely related to classical Nadaraya-Watson estimator. Adaptive GRNN, using anisotropic kernels, can be also applied for features selection tasks when working with high dimensional data [1,3]. In the present research Adaptive GRNN are used to study geospatial data predictability and relevant feature selection using both simulated and real data case studies. The original raw data were either three dimensional monthly precipitation data or monthly wind speeds embedded into 13 dimensional space constructed by geographical coordinates and geo-features calculated from digital elevation model. GRNN were applied in two different ways: 1) adaptive GRNN with the resulting list of features ordered according to their relevancy; and 2) adaptive GRNN applied to evaluate all possible models N [in case of wind fields N=(2^13 -1)=8191] and rank them according to the cross-validation error. In both cases training were carried out applying leave-one-out procedure. An important result of the study is that the set of the most relevant features depends on the month (strong seasonal effect) and year. The predictabilities of precipitation and wind field patterns, estimated using the cross-validation and testing errors of raw and shuffled data, were studied in detail. The results of both approaches were qualitatively and quantitatively compared. In conclusion, Adaptive GRNN with their ability to select features and efficient modelling of complex high dimensional data can be widely used in automatic/on-line mapping and as an integrated part of environmental decision support systems. 1. Kanevski M., Pozdnoukhov A., Timonin V. Machine Learning for Spatial Environmental Data. Theory, applications and software. EPFL Press

  12. An analytical model for climatic predictions

    International Nuclear Information System (INIS)

    Njau, E.C.

    1990-12-01

    A climatic model based upon analytical expressions is presented. This model is capable of making long-range predictions of heat energy variations on regional or global scales. These variations can then be transformed into corresponding variations of some other key climatic parameters since weather and climatic changes are basically driven by differential heating and cooling around the earth. On the basis of the mathematical expressions upon which the model is based, it is shown that the global heat energy structure (and hence the associated climatic system) are characterized by zonally as well as latitudinally propagating fluctuations at frequencies downward of 0.5 day -1 . We have calculated the propagation speeds for those particular frequencies that are well documented in the literature. The calculated speeds are in excellent agreement with the measured speeds. (author). 13 refs

  13. An Anisotropic Hardening Model for Springback Prediction

    International Nuclear Information System (INIS)

    Zeng, Danielle; Xia, Z. Cedric

    2005-01-01

    As more Advanced High-Strength Steels (AHSS) are heavily used for automotive body structures and closures panels, accurate springback prediction for these components becomes more challenging because of their rapid hardening characteristics and ability to sustain even higher stresses. In this paper, a modified Mroz hardening model is proposed to capture realistic Bauschinger effect at reverse loading, such as when material passes through die radii or drawbead during sheet metal forming process. This model accounts for material anisotropic yield surface and nonlinear isotropic/kinematic hardening behavior. Material tension/compression test data are used to accurately represent Bauschinger effect. The effectiveness of the model is demonstrated by comparison of numerical and experimental springback results for a DP600 straight U-channel test

  14. Generic models and parameters for assessing the environmental transfer of radionuclides from routine releases

    International Nuclear Information System (INIS)

    1982-01-01

    This report is addressed to national regulatory bodies and technical and administrative personnel responsible for performing environmental impact analyses, in particular for generic assessments of doses to most exposed individuals from routine releases of radioactive effluents to atmospheric and aquatic environments. The concern of society in general for the quality of the environment and the realization that all human activities have some environmental effect, in which actions and reactions are coupled in a complex but predictable manner, have led to the development of a procedure for environmental impact analysis. This procedure is a predictive one, which tries to forecast probable environmental effects before some action, such as the construction and operation of a nuclear power station, is decided upon. The method of prediction is by the application of models that attempt to describe the environmental processes in mathematical terms in order to produce a quantitative result which can be used in the decision-making process

  15. Environmental changes impacting Echinococcus transmission: research to support predictive surveillance and control.

    Science.gov (United States)

    Atkinson, Jo-An M; Gray, Darren J; Clements, Archie C A; Barnes, Tamsin S; McManus, Donald P; Yang, Yu R

    2013-03-01

    Echinococcosis, resulting from infection with tapeworms Echinococcus granulosus and E. multilocularis, has a global distribution with 2-3 million people affected and 200,000 new cases diagnosed annually. Costs of treatment for humans and economic losses to the livestock industry have been estimated to exceed $2 billion. These figures are likely to be an underestimation given the challenges with its early detection and the lack of mandatory official reporting policies in most countries. Despite this global burden, echinococcosis remains a neglected zoonosis. The importance of environmental factors in influencing the transmission intensity and distribution of Echinococcus spp. is increasingly being recognized. With the advent of climate change and the influence of global population expansion, food insecurity and land-use changes, questions about the potential impact of changing temperature, rainfall patterns, increasing urbanization, deforestation, grassland degradation and overgrazing on zoonotic disease transmission are being raised. This study is the first to comprehensively review how climate change and anthropogenic environmental factors contribute to the transmission of echinococcosis mediated by changes in animal population dynamics, spatial overlap of competent hosts and the creation of improved conditions for egg survival. We advocate rigorous scientific research to establish the causal link between specific environmental variables and echinococcosis in humans and the incorporation of environmental, animal and human data collection within a sentinel site surveillance network that will complement satellite remote-sensing information. Identifying the environmental determinants of transmission risk to humans will be vital for the design of more accurate predictive models to guide cost-effective pre-emptive public health action against echinococcosis. © 2012 Blackwell Publishing Ltd.

  16. Environmental Journalism and Environmental Communication Education: Identifying an Educational Model.

    Science.gov (United States)

    Casey, William E.

    News coverage of the environment has been the subject of intense scrutiny for some 25 years. Content analyses have suggested that environmental coverage is plagued by omission of important information in areas of economics, health, safety, and consumer concerns. Surveys of reporters show high levels of frustration and dissatisfaction with their…

  17. Web tools for predictive toxicology model building.

    Science.gov (United States)

    Jeliazkova, Nina

    2012-07-01

    The development and use of web tools in chemistry has accumulated more than 15 years of history already. Powered by the advances in the Internet technologies, the current generation of web systems are starting to expand into areas, traditional for desktop applications. The web platforms integrate data storage, cheminformatics and data analysis tools. The ease of use and the collaborative potential of the web is compelling, despite the challenges. The topic of this review is a set of recently published web tools that facilitate predictive toxicology model building. The focus is on software platforms, offering web access to chemical structure-based methods, although some of the frameworks could also provide bioinformatics or hybrid data analysis functionalities. A number of historical and current developments are cited. In order to provide comparable assessment, the following characteristics are considered: support for workflows, descriptor calculations, visualization, modeling algorithms, data management and data sharing capabilities, availability of GUI or programmatic access and implementation details. The success of the Web is largely due to its highly decentralized, yet sufficiently interoperable model for information access. The expected future convergence between cheminformatics and bioinformatics databases provides new challenges toward management and analysis of large data sets. The web tools in predictive toxicology will likely continue to evolve toward the right mix of flexibility, performance, scalability, interoperability, sets of unique features offered, friendly user interfaces, programmatic access for advanced users, platform independence, results reproducibility, curation and crowdsourcing utilities, collaborative sharing and secure access.

  18. [Endometrial cancer: Predictive models and clinical impact].

    Science.gov (United States)

    Bendifallah, Sofiane; Ballester, Marcos; Daraï, Emile

    2017-12-01

    In France, in 2015, endometrial cancer (CE) is the first gynecological cancer in terms of incidence and the fourth cause of cancer of the woman. About 8151 new cases and nearly 2179 deaths have been reported. Treatments (surgery, external radiotherapy, brachytherapy and chemotherapy) are currently delivered on the basis of an estimation of the recurrence risk, an estimation of lymph node metastasis or an estimate of survival probability. This risk is determined on the basis of prognostic factors (clinical, histological, imaging, biological) taken alone or grouped together in the form of classification systems, which are currently insufficient to account for the evolutionary and prognostic heterogeneity of endometrial cancer. For endometrial cancer, the concept of mathematical modeling and its application to prediction have developed in recent years. These biomathematical tools have opened a new era of care oriented towards the promotion of targeted therapies and personalized treatments. Many predictive models have been published to estimate the risk of recurrence and lymph node metastasis, but a tiny fraction of them is sufficiently relevant and of clinical utility. The optimization tracks are multiple and varied, suggesting the possibility in the near future of a place for these mathematical models. The development of high-throughput genomics is likely to offer a more detailed molecular characterization of the disease and its heterogeneity. Copyright © 2017 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.

  19. Prediction of Hydrolysis Products of Organic Chemicals under Environmental pH Conditions

    Science.gov (United States)

    Cheminformatics-based software tools can predict the molecular structure of transformation products using a library of transformation reaction schemes. This paper presents the development of such a library for abiotic hydrolysis of organic chemicals under environmentally relevant...

  20. Predictive Capability Maturity Model for computational modeling and simulation.

    Energy Technology Data Exchange (ETDEWEB)

    Oberkampf, William Louis; Trucano, Timothy Guy; Pilch, Martin M.

    2007-10-01

    The Predictive Capability Maturity Model (PCMM) is a new model that can be used to assess the level of maturity of computational modeling and simulation (M&S) efforts. The development of the model is based on both the authors experience and their analysis of similar investigations in the past. The perspective taken in this report is one of judging the usefulness of a predictive capability that relies on the numerical solution to partial differential equations to better inform and improve decision making. The review of past investigations, such as the Software Engineering Institute's Capability Maturity Model Integration and the National Aeronautics and Space Administration and Department of Defense Technology Readiness Levels, indicates that a more restricted, more interpretable method is needed to assess the maturity of an M&S effort. The PCMM addresses six contributing elements to M&S: (1) representation and geometric fidelity, (2) physics and material model fidelity, (3) code verification, (4) solution verification, (5) model validation, and (6) uncertainty quantification and sensitivity analysis. For each of these elements, attributes are identified that characterize four increasing levels of maturity. Importantly, the PCMM is a structured method for assessing the maturity of an M&S effort that is directed toward an engineering application of interest. The PCMM does not assess whether the M&S effort, the accuracy of the predictions, or the performance of the engineering system satisfies or does not satisfy specified application requirements.

  1. Environmental flows in hydro-economic models

    Science.gov (United States)

    Pereau, Jean-Christophe; Pryet, Alexandre

    2018-03-01

    The protection of environmental flows, as a management objective for a regulating agency, needs to be consistent with the aquifer water balance and the degree of resource renewability. A stylized hydro-economic model is used where natural recharge, which sustains environmental flows, is considered both in the aquifer water budget and in the welfare function as ecosystem damage. Groundwater recharge and the associated natural drainage may be neglected for aquifers containing fossil water, where the groundwater is mined. However, when dealing with an aquifer that constitutes a renewable resource, for which recharge is not negligible, natural drainage should explicitly appear in the water budget. In doing so, the optimum path of net extraction rate does not necessarily converge to the recharge rate, but depends on the costs associated with ecosystem damages. The optimal paths and equilibrium values for the water volume and water extraction are analytically derived, and numerical simulations based on the Western La Mancha aquifer (southwest Spain) illustrate the theoretical results of the study.

  2. Predictive models for practical use in aquatic radioecology

    International Nuclear Information System (INIS)

    Haakanson, L.

    1997-01-01

    A given fallout of radiocaesium will be distributed and taken up by biota differently in various types of lakes. Thus, lakes have different ''sensitivities'' to radiocesium. Important environmental factors regulating the biouptake are the water retention time and the K-concentration. Several practically useful and ecologically relevant methods exist to remediate lakes, e.g., liming, potash treatment and fertilization of low-productive lakes. The basic aim of this paper (which is a brief version of paper I) is to use the VAMP model, first to illustrate the fact that different lakes have different ''sensitivities'', and then to simulate the effects of alternative remedial methods. The VAMP model has been validated against an extensive set of data from seven European lakes. It has been shown that the VAMP model yields just as good predictions as parallel sets of empirical data, and this is as good as any model can do (II). The main objective of the model is to predict radiocesium in predatory fish (used for human consumption) and in lake water (used for irrigation, drinking water, etc.)

  3. Mathematical and Numerical Techniques in Energy and Environmental Modeling

    Science.gov (United States)

    Chen, Z.; Ewing, R. E.

    Mathematical models have been widely used to predict, understand, and optimize many complex physical processes, from semiconductor or pharmaceutical design to large-scale applications such as global weather models to astrophysics. In particular, simulation of environmental effects of air pollution is extensive. Here we address the need for using similar models to understand the fate and transport of groundwater contaminants and to design in situ remediation strategies. Three basic problem areas need to be addressed in the modeling and simulation of the flow of groundwater contamination. First, one obtains an effective model to describe the complex fluid/fluid and fluid/rock interactions that control the transport of contaminants in groundwater. This includes the problem of obtaining accurate reservoir descriptions at various length scales and modeling the effects of this heterogeneity in the reservoir simulators. Next, one develops accurate discretization techniques that retain the important physical properties of the continuous models. Finally, one develops efficient numerical solution algorithms that utilize the potential of the emerging computing architectures. We will discuss recent advances and describe the contribution of each of the papers in this book in these three areas. Keywords: reservoir simulation, mathematical models, partial differential equations, numerical algorithms

  4. Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.

    Science.gov (United States)

    Cruz-Marcelo, Alejandro; Rosner, Gary L; Müller, Peter; Stewart, Clinton F

    2013-04-01

    In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparametric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.

  5. Combining GPS measurements and IRI model predictions

    International Nuclear Information System (INIS)

    Hernandez-Pajares, M.; Juan, J.M.; Sanz, J.; Bilitza, D.

    2002-01-01

    The free electrons distributed in the ionosphere (between one hundred and thousands of km in height) produce a frequency-dependent effect on Global Positioning System (GPS) signals: a delay in the pseudo-orange and an advance in the carrier phase. These effects are proportional to the columnar electron density between the satellite and receiver, i.e. the integrated electron density along the ray path. Global ionospheric TEC (total electron content) maps can be obtained with GPS data from a network of ground IGS (international GPS service) reference stations with an accuracy of few TEC units. The comparison with the TOPEX TEC, mainly measured over the oceans far from the IGS stations, shows a mean bias and standard deviation of about 2 and 5 TECUs respectively. The discrepancies between the STEC predictions and the observed values show an RMS typically below 5 TECUs (which also includes the alignment code noise). he existence of a growing database 2-hourly global TEC maps and with resolution of 5x2.5 degrees in longitude and latitude can be used to improve the IRI prediction capability of the TEC. When the IRI predictions and the GPS estimations are compared for a three month period around the Solar Maximum, they are in good agreement for middle latitudes. An over-determination of IRI TEC has been found at the extreme latitudes, the IRI predictions being, typically two times higher than the GPS estimations. Finally, local fits of the IRI model can be done by tuning the SSN from STEC GPS observations

  6. Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence.

    Science.gov (United States)

    Anwar, Mohammad Y; Lewnard, Joseph A; Parikh, Sunil; Pitzer, Virginia E

    2016-11-22

    Malaria remains endemic in Afghanistan. National control and prevention strategies would be greatly enhanced through a better ability to forecast future trends in disease incidence. It is, therefore, of interest to develop a predictive tool for malaria patterns based on the current passive and affordable surveillance system in this resource-limited region. This study employs data from Ministry of Public Health monthly reports from January 2005 to September 2015. Malaria incidence in Afghanistan was forecasted using autoregressive integrated moving average (ARIMA) models in order to build a predictive tool for malaria surveillance. Environmental and climate data were incorporated to assess whether they improve predictive power of models. Two models were identified, each appropriate for different time horizons. For near-term forecasts, malaria incidence can be predicted based on the number of cases in the four previous months and 12 months prior (Model 1); for longer-term prediction, malaria incidence can be predicted using the rates 1 and 12 months prior (Model 2). Next, climate and environmental variables were incorporated to assess whether the predictive power of proposed models could be improved. Enhanced vegetation index was found to have increased the predictive accuracy of longer-term forecasts. Results indicate ARIMA models can be applied to forecast malaria patterns in Afghanistan, complementing current surveillance systems. The models provide a means to better understand malaria dynamics in a resource-limited context with minimal data input, yielding forecasts that can be used for public health planning at the national level.

  7. A Predictive Maintenance Model for Railway Tracks

    DEFF Research Database (Denmark)

    Li, Rui; Wen, Min; Salling, Kim Bang

    2015-01-01

    presents a mathematical model based on Mixed Integer Programming (MIP) which is designed to optimize the predictive railway tamping activities for ballasted track for the time horizon up to four years. The objective function is setup to minimize the actual costs for the tamping machine (measured by time......). Five technical and economic aspects are taken into account to schedule tamping: (1) track degradation of the standard deviation of the longitudinal level over time; (2) track geometrical alignment; (3) track quality thresholds based on the train speed limits; (4) the dependency of the track quality...... recovery on the track quality after tamping operation and (5) Tamping machine operation factors. A Danish railway track between Odense and Fredericia with 57.2 km of length is applied for a time period of two to four years in the proposed maintenance model. The total cost can be reduced with up to 50...

  8. Measuring and modeling exposure from environmental radiation on tidal flats

    International Nuclear Information System (INIS)

    Gould, T.J.; Hess, C.T.

    2005-01-01

    To examine the shielding effects of the tide cycle, a high pressure ion chamber was used to measure the exposure rate from environmental radiation on tidal flats. A theoretical model is derived to predict the behavior of exposure rate as a function of time for a detector placed one meter above ground on a tidal flat. The numerical integration involved in this derivation results in an empirical formula which implies exposure rate ∝tan-1(sint). We propose that calculating the total exposure incurred on a tidal flat requires measurements of only the slope of the tidal flat and the exposure rate when no shielding occurs. Experimental results are consistent with the model

  9. An Operational Model for the Prediction of Jet Blast

    Science.gov (United States)

    2012-01-09

    This paper presents an operational model for the prediction of jet blast. The model was : developed based upon three modules including a jet exhaust model, jet centerline decay : model and aircraft motion model. The final analysis was compared with d...

  10. In silico environmental chemical science: properties and processes from statistical and computational modelling

    Energy Technology Data Exchange (ETDEWEB)

    Tratnyek, P. G.; Bylaska, Eric J.; Weber, Eric J.

    2017-01-01

    Quantitative structure–activity relationships (QSARs) have long been used in the environmental sciences. More recently, molecular modeling and chemoinformatic methods have become widespread. These methods have the potential to expand and accelerate advances in environmental chemistry because they complement observational and experimental data with “in silico” results and analysis. The opportunities and challenges that arise at the intersection between statistical and theoretical in silico methods are most apparent in the context of properties that determine the environmental fate and effects of chemical contaminants (degradation rate constants, partition coefficients, toxicities, etc.). The main example of this is the calibration of QSARs using descriptor variable data calculated from molecular modeling, which can make QSARs more useful for predicting property data that are unavailable, but also can make them more powerful tools for diagnosis of fate determining pathways and mechanisms. Emerging opportunities for “in silico environmental chemical science” are to move beyond the calculation of specific chemical properties using statistical models and toward more fully in silico models, prediction of transformation pathways and products, incorporation of environmental factors into model predictions, integration of databases and predictive models into more comprehensive and efficient tools for exposure assessment, and extending the applicability of all the above from chemicals to biologicals and materials.

  11. In silico environmental chemical science: properties and processes from statistical and computational modelling.

    Science.gov (United States)

    Tratnyek, Paul G; Bylaska, Eric J; Weber, Eric J

    2017-03-22

    Quantitative structure-activity relationships (QSARs) have long been used in the environmental sciences. More recently, molecular modeling and chemoinformatic methods have become widespread. These methods have the potential to expand and accelerate advances in environmental chemistry because they complement observational and experimental data with "in silico" results and analysis. The opportunities and challenges that arise at the intersection between statistical and theoretical in silico methods are most apparent in the context of properties that determine the environmental fate and effects of chemical contaminants (degradation rate constants, partition coefficients, toxicities, etc.). The main example of this is the calibration of QSARs using descriptor variable data calculated from molecular modeling, which can make QSARs more useful for predicting property data that are unavailable, but also can make them more powerful tools for diagnosis of fate determining pathways and mechanisms. Emerging opportunities for "in silico environmental chemical science" are to move beyond the calculation of specific chemical properties using statistical models and toward more fully in silico models, prediction of transformation pathways and products, incorporation of environmental factors into model predictions, integration of databases and predictive models into more comprehensive and efficient tools for exposure assessment, and extending the applicability of all the above from chemicals to biologicals and materials.

  12. Predicting future glacial lakes in Austria using different modelling approaches

    Science.gov (United States)

    Otto, Jan-Christoph; Helfricht, Kay; Prasicek, Günther; Buckel, Johannes; Keuschnig, Markus

    2017-04-01

    Glacier retreat is one of the most apparent consequences of temperature rise in the 20th and 21th centuries in the European Alps. In Austria, more than 240 new lakes have formed in glacier forefields since the Little Ice Age. A similar signal is reported from many mountain areas worldwide. Glacial lakes can constitute important environmental and socio-economic impacts on high mountain systems including water resource management, sediment delivery, natural hazards, energy production and tourism. Their development significantly modifies the landscape configuration and visual appearance of high mountain areas. Knowledge on the location, number and extent of these future lakes can be used to assess potential impacts on high mountain geo-ecosystems and upland-lowland interactions. Information on new lakes is critical to appraise emerging threads and potentials for society. The recent development of regional ice thickness models and their combination with high resolution glacier surface data allows predicting the topography below current glaciers by subtracting ice thickness from glacier surface. Analyzing these modelled glacier bed surfaces reveals overdeepenings that represent potential locations for future lakes. In order to predict the location of future glacial lakes below recent glaciers in the Austrian Alps we apply different ice thickness models using high resolution terrain data and glacier outlines. The results are compared and validated with ice thickness data from geophysical surveys. Additionally, we run the models on three different glacier extents provided by the Austrian Glacier Inventories from 1969, 1998 and 2006. Results of this historical glacier extent modelling are compared to existing glacier lakes and discussed focusing on geomorphological impacts on lake evolution. We discuss model performance and observed differences in the results in order to assess the approach for a realistic prediction of future lake locations. The presentation delivers

  13. Continuous-Discrete Time Prediction-Error Identification Relevant for Linear Model Predictive Control

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Jørgensen, Sten Bay

    2007-01-01

    A Prediction-error-method tailored for model based predictive control is presented. The prediction-error method studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model. The linear discrete-time stochastic state space...... model is realized from a continuous-discrete-time linear stochastic system specified using transfer functions with time-delays. It is argued that the prediction-error criterion should be selected such that it is compatible with the objective function of the predictive controller in which the model...

  14. A software engineering perspective on environmental modeling framework design: The object modeling system

    Science.gov (United States)

    The environmental modeling community has historically been concerned with the proliferation of models and the effort associated with collective model development tasks (e.g., code generation, data provisioning and transformation, etc.). Environmental modeling frameworks (EMFs) have been developed to...

  15. Aftereffect Calculation and Prediction of Methanol Tank Leak’s Environmental Risk Accident

    Science.gov (United States)

    Lang, Yueting; Zheng, Lina; Chen, Henan; Wang, Qiushi; Jiang, Hui; Pan, Yiwen

    2018-01-01

    With the increasing frequency of environmental risk accidents, more emphasis was placed on environmental risk assessment. In this article, the aftermath of an Environmental Risk Accident on Methanol Tank Leakage occurred on a cryogenic unit area in a certain oilfield processing plant have been mainly calculated and predicted. Major hazards were identified through the major hazards identification on dangerous chemicals, which could afterwards analyze maximum credible accident and confirm source item and the source intensity. In the end, the consequence of the accident has been calculated so that the impact on surrounding environment can be predicted after the accident.

  16. Predicting the fate of biodiversity using species' distribution models: enhancing model comparability and repeatability.

    Directory of Open Access Journals (Sweden)

    Genoveva Rodríguez-Castañeda

    Full Text Available Species distribution modeling (SDM is an increasingly important tool to predict the geographic distribution of species. Even though many problems associated with this method have been highlighted and solutions have been proposed, little has been done to increase comparability among studies. We reviewed recent publications applying SDMs and found that seventy nine percent failed to report methods that ensure comparability among studies, such as disclosing the maximum probability range produced by the models and reporting on the number of species occurrences used. We modeled six species of Falco from northern Europe and demonstrate that model results are altered by (1 spatial bias in species' occurrence data, (2 differences in the geographic extent of the environmental data, and (3 the effects of transformation of model output to presence/absence data when applying thresholds. Depending on the modeling decisions, forecasts of the future geographic distribution of Falco ranged from range contraction in 80% of the species to no net loss in any species, with the best model predicting no net loss of habitat in Northern Europe. The fact that predictions of range changes in response to climate change in published studies may be influenced by decisions in the modeling process seriously hampers the possibility of making sound management recommendations. Thus, each of the decisions made in generating SDMs should be reported and evaluated to ensure conclusions and policies are based on the biology and ecology of the species being modeled.

  17. Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.

    Science.gov (United States)

    Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko

    2016-03-01

    In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. Copyright © 2015 Elsevier Ltd. All rights reserved.

  18. APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR PREDICTION OF AIR POLLUTION LEVELS IN ENVIRONMENTAL MONITORING

    Directory of Open Access Journals (Sweden)

    Małgorzata Pawul

    2016-09-01

    Full Text Available Recently, a lot of attention was paid to the improvement of methods which are used to air quality forecasting. Artificial neural networks can be applied to model these problems. Their advantage is that they can solve the problem in the conditions of incomplete information, without the knowledge of the analytical relationship between the input and output data. In this paper we applied artificial neural networks to predict the PM 10 concentrations as factors determining the occurrence of smog phenomena. To create these networks we used meteorological data and concentrations of PM 10. The data were recorded in 2014 and 2015 at three measuring stations operating in Krakow under the State Environmental Monitoring. The best results were obtained by three-layer perceptron with back-propagation algorithm. The neural networks received a good fit in all cases.

  19. Aquatic Exposure Predictions of Insecticide Field Concentrations Using a Multimedia Mass-Balance Model.

    Science.gov (United States)

    Knäbel, Anja; Scheringer, Martin; Stehle, Sebastian; Schulz, Ralf

    2016-04-05

    Highly complex process-driven mechanistic fate and transport models and multimedia mass balance models can be used for the exposure prediction of pesticides in different environmental compartments. Generally, both types of models differ in spatial and temporal resolution. Process-driven mechanistic fate models are very complex, and calculations are time-intensive. This type of model is currently used within the European regulatory pesticide registration (FOCUS). Multimedia mass-balance models require fewer input parameters to calculate concentration ranges and the partitioning between different environmental media. In this study, we used the fugacity-based small-region model (SRM) to calculate predicted environmental concentrations (PEC) for 466 cases of insecticide field concentrations measured in European surface waters. We were able to show that the PECs of the multimedia model are more protective in comparison to FOCUS. In addition, our results show that the multimedia model results have a higher predictive power to simulate varying field concentrations at a higher level of field relevance. The adaptation of the model scenario to actual field conditions suggests that the performance of the SRM increases when worst-case conditions are replaced by real field data. Therefore, this study shows that a less complex modeling approach than that used in the regulatory risk assessment exhibits a higher level of protectiveness and predictiveness and that there is a need to develop and evaluate new ecologically relevant scenarios in the context of pesticide exposure modeling.

  20. Integrated Environmental Modelling: Human decisions, human challenges

    Science.gov (United States)

    Glynn, Pierre D.

    2015-01-01

    Integrated Environmental Modelling (IEM) is an invaluable tool for understanding the complex, dynamic ecosystems that house our natural resources and control our environments. Human behaviour affects the ways in which the science of IEM is assembled and used for meaningful societal applications. In particular, human biases and heuristics reflect adaptation and experiential learning to issues with frequent, sharply distinguished, feedbacks. Unfortunately, human behaviour is not adapted to the more diffusely experienced problems that IEM typically seeks to address. Twelve biases are identified that affect IEM (and science in general). These biases are supported by personal observations and by the findings of behavioural scientists. A process for critical analysis is proposed that addresses some human challenges of IEM and solicits explicit description of (1) represented processes and information, (2) unrepresented processes and information, and (3) accounting for, and cognizance of, potential human biases. Several other suggestions are also made that generally complement maintaining attitudes of watchful humility, open-mindedness, honesty and transparent accountability. These suggestions include (1) creating a new area of study in the behavioural biogeosciences, (2) using structured processes for engaging the modelling and stakeholder communities in IEM, and (3) using ‘red teams’ to increase resilience of IEM constructs and use.

  1. Integrated Environmental Modelling: human decisions, human challenges

    Science.gov (United States)

    Glynn, Pierre D.

    2015-01-01

    Integrated Environmental Modelling (IEM) is an invaluable tool for understanding the complex, dynamic ecosystems that house our natural resources and control our environments. Human behaviour affects the ways in which the science of IEM is assembled and used for meaningful societal applications. In particular, human biases and heuristics reflect adaptation and experiential learning to issues with frequent, sharply distinguished, feedbacks. Unfortunately, human behaviour is not adapted to the more diffusely experienced problems that IEM typically seeks to address. Twelve biases are identified that affect IEM (and science in general). These biases are supported by personal observations and by the findings of behavioural scientists. A process for critical analysis is proposed that addresses some human challenges of IEM and solicits explicit description of (1) represented processes and information, (2) unrepresented processes and information, and (3) accounting for, and cognizance of, potential human biases. Several other suggestions are also made that generally complement maintaining attitudes of watchful humility, open-mindedness, honesty and transparent accountability. These suggestions include (1) creating a new area of study in the behavioural biogeosciences, (2) using structured processes for engaging the modelling and stakeholder communities in IEM, and (3) using ‘red teams’ to increase resilience of IEM constructs and use.

  2. Predicting Biological Information Flow in a Model Oxygen Minimum Zone

    Science.gov (United States)

    Louca, S.; Hawley, A. K.; Katsev, S.; Beltran, M. T.; Bhatia, M. P.; Michiels, C.; Capelle, D.; Lavik, G.; Doebeli, M.; Crowe, S.; Hallam, S. J.

    2016-02-01

    Microbial activity drives marine biochemical fluxes and nutrient cycling at global scales. Geochemical measurements as well as molecular techniques such as metagenomics, metatranscriptomics and metaproteomics provide great insight into microbial activity. However, an integration of molecular and geochemical data into mechanistic biogeochemical models is still lacking. Recent work suggests that microbial metabolic pathways are, at the ecosystem level, strongly shaped by stoichiometric and energetic constraints. Hence, models rooted in fluxes of matter and energy may yield a holistic understanding of biogeochemistry. Furthermore, such pathway-centric models would allow a direct consolidation with meta'omic data. Here we present a pathway-centric biogeochemical model for the seasonal oxygen minimum zone in Saanich Inlet, a fjord off the coast of Vancouver Island. The model considers key dissimilatory nitrogen and sulfur fluxes, as well as the population dynamics of the genes that mediate them. By assuming a direct translation of biocatalyzed energy fluxes to biosynthesis rates, we make predictions about the distribution and activity of the corresponding genes. A comparison of the model to molecular measurements indicates that the model explains observed DNA, RNA, protein and cell depth profiles. This suggests that microbial activity in marine ecosystems such as oxygen minimum zones is well described by DNA abundance, which, in conjunction with geochemical constraints, determines pathway expression and process rates. Our work further demonstrates how meta'omic data can be mechanistically linked to environmental redox conditions and biogeochemical processes.

  3. Predictive modeling: potential application in prevention services.

    Science.gov (United States)

    Wilson, Moira L; Tumen, Sarah; Ota, Rissa; Simmers, Anthony G

    2015-05-01

    In 2012, the New Zealand Government announced a proposal to introduce predictive risk models (PRMs) to help professionals identify and assess children at risk of abuse or neglect as part of a preventive early intervention strategy, subject to further feasibility study and trialing. The purpose of this study is to examine technical feasibility and predictive validity of the proposal, focusing on a PRM that would draw on population-wide linked administrative data to identify newborn children who are at high priority for intensive preventive services. Data analysis was conducted in 2013 based on data collected in 2000-2012. A PRM was developed using data for children born in 2010 and externally validated for children born in 2007, examining outcomes to age 5 years. Performance of the PRM in predicting administratively recorded substantiations of maltreatment was good compared to the performance of other tools reviewed in the literature, both overall, and for indigenous Māori children. Some, but not all, of the children who go on to have recorded substantiations of maltreatment could be identified early using PRMs. PRMs should be considered as a potential complement to, rather than a replacement for, professional judgment. Trials are needed to establish whether risks can be mitigated and PRMs can make a positive contribution to frontline practice, engagement in preventive services, and outcomes for children. Deciding whether to proceed to trial requires balancing a range of considerations, including ethical and privacy risks and the risk of compounding surveillance bias. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

  4. Model Fusion Tool - the Open Environmental Modelling Platform Concept

    Science.gov (United States)

    Kessler, H.; Giles, J. R.

    2010-12-01

    The vision of an Open Environmental Modelling Platform - seamlessly linking geoscience data, concepts and models to aid decision making in times of environmental change. Governments and their executive agencies across the world are facing increasing pressure to make decisions about the management of resources in light of population growth and environmental change. In the UK for example, groundwater is becoming a scarce resource for large parts of its most densely populated areas. At the same time river and groundwater flooding resulting from high rainfall events are increasing in scale and frequency and sea level rise is threatening the defences of coastal cities. There is also a need for affordable housing, improved transport infrastructure and waste disposal as well as sources of renewable energy and sustainable food production. These challenges can only be resolved if solutions are based on sound scientific evidence. Although we have knowledge and understanding of many individual processes in the natural sciences it is clear that a single science discipline is unable to answer the questions and their inter-relationships. Modern science increasingly employs computer models to simulate the natural, economic and human system. Management and planning requires scenario modelling, forecasts and ‘predictions’. Although the outputs are often impressive in terms of apparent accuracy and visualisation, they are inherently not suited to simulate the response to feedbacks from other models of the earth system, such as the impact of human actions. Geological Survey Organisations (GSO) are increasingly employing advances in Information Technology to visualise and improve their understanding of geological systems. Instead of 2 dimensional paper maps and reports many GSOs now produce 3 dimensional geological framework models and groundwater flow models as their standard output. Additionally the British Geological Survey have developed standard routines to link geological

  5. Heuristic Modeling for TRMM Lifetime Predictions

    Science.gov (United States)

    Jordan, P. S.; Sharer, P. J.; DeFazio, R. L.

    1996-01-01

    Analysis time for computing the expected mission lifetimes of proposed frequently maneuvering, tightly altitude constrained, Earth orbiting spacecraft have been significantly reduced by means of a heuristic modeling method implemented in a commercial-off-the-shelf spreadsheet product (QuattroPro) running on a personal computer (PC). The method uses a look-up table to estimate the maneuver frequency per month as a function of the spacecraft ballistic coefficient and the solar flux index, then computes the associated fuel use by a simple engine model. Maneuver frequency data points are produced by means of a single 1-month run of traditional mission analysis software for each of the 12 to 25 data points required for the table. As the data point computations are required only a mission design start-up and on the occasion of significant mission redesigns, the dependence on time consuming traditional modeling methods is dramatically reduced. Results to date have agreed with traditional methods to within 1 to 1.5 percent. The spreadsheet approach is applicable to a wide variety of Earth orbiting spacecraft with tight altitude constraints. It will be particularly useful to such missions as the Tropical Rainfall Measurement Mission scheduled for launch in 1997, whose mission lifetime calculations are heavily dependent on frequently revised solar flux predictions.

  6. A Computational Model for Predicting Gas Breakdown

    Science.gov (United States)

    Gill, Zachary

    2017-10-01

    Pulsed-inductive discharges are a common method of producing a plasma. They provide a mechanism for quickly and efficiently generating a large volume of plasma for rapid use and are seen in applications including propulsion, fusion power, and high-power lasers. However, some common designs see a delayed response time due to the plasma forming when the magnitude of the magnetic field in the thruster is at a minimum. New designs are difficult to evaluate due to the amount of time needed to construct a new geometry and the high monetary cost of changing the power generation circuit. To more quickly evaluate new designs and better understand the shortcomings of existing designs, a computational model is developed. This model uses a modified single-electron model as the basis for a Mathematica code to determine how the energy distribution in a system changes with regards to time and location. By analyzing this energy distribution, the approximate time and location of initial plasma breakdown can be predicted. The results from this code are then compared to existing data to show its validity and shortcomings. Missouri S&T APLab.

  7. Distributed model predictive control made easy

    CERN Document Server

    Negenborn, Rudy

    2014-01-01

    The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems.   This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those ...

  8. Which method predicts recidivism best?: A comparison of statistical, machine learning, and data mining predictive models

    OpenAIRE

    Tollenaar, N.; van der Heijden, P.G.M.

    2012-01-01

    Using criminal population conviction histories of recent offenders, prediction mod els are developed that predict three types of criminal recidivism: general recidivism, violent recidivism and sexual recidivism. The research question is whether prediction techniques from modern statistics, data mining and machine learning provide an improvement in predictive performance over classical statistical methods, namely logistic regression and linear discrim inant analysis. These models are compared ...

  9. A model evaluation checklist for process-based environmental models

    Science.gov (United States)

    Jackson-Blake, Leah

    2015-04-01

    the conceptual model on which it is based. In this study, a number of model structural shortcomings were identified, such as a lack of dissolved phosphorus transport via infiltration excess overland flow, potential discrepancies in the particulate phosphorus simulation and a lack of spatial granularity. (4) Conceptual challenges, as conceptual models on which predictive models are built are often outdated, having not kept up with new insights from monitoring and experiments. For example, soil solution dissolved phosphorus concentration in INCA-P is determined by the Freundlich adsorption isotherm, which could potentially be replaced using more recently-developed adsorption models that take additional soil properties into account. This checklist could be used to assist in identifying why model performance may be poor or unreliable. By providing a model evaluation framework, it could help prioritise which areas should be targeted to improve model performance or model credibility, whether that be through using alternative calibration techniques and statistics, improved data collection, improving or simplifying the model structure or updating the model to better represent current understanding of catchment processes.

  10. To predict the niche, model colonization and extinction

    Science.gov (United States)

    Charles B. Yackulic; James D. Nichols; Janice Reid; Ricky Der

    2015-01-01

    Ecologists frequently try to predict the future geographic distributions of species. Most studies assume that the current distribution of a species reflects its environmental requirements (i.e., the species’ niche). However, the current distributions of many species are unlikely to be at equilibrium with the current distribution of environmental conditions, both...

  11. Fuzzy predictive filtering in nonlinear economic model predictive control for demand response

    DEFF Research Database (Denmark)

    Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.

    2016-01-01

    The performance of a model predictive controller (MPC) is highly correlated with the model's accuracy. This paper introduces an economic model predictive control (EMPC) scheme based on a nonlinear model, which uses a branch-and-bound tree search for solving the inherent non-convex optimization...

  12. Computer model for estimating electric utility environmental noise

    International Nuclear Information System (INIS)

    Teplitzky, A.M.; Hahn, K.J.

    1991-01-01

    This paper reports on a computer code for estimating environmental noise emissions from the operation and the construction of electric power plants that was developed based on algorithms. The computer code (Model) is used to predict octave band sound power levels for power plant operation and construction activities on the basis of the equipment operating characteristics and calculates off-site sound levels for each noise source and for an entire plant. Estimated noise levels are presented either as A-weighted sound level contours around the power plant or as octave band levels at user defined receptor locations. Calculated sound levels can be compared with user designated noise criteria, and the program can assist the user in analyzing alternative noise control strategies

  13. Evaluation of Monticello Nuclear Power Plant, Environmental Impact Prediction, based on monitoring programs

    Energy Technology Data Exchange (ETDEWEB)

    Gore, K.L.; Thomas, J.M.; Kannberg, L.D.; Watson, D.G.

    1976-11-01

    This report evaluates quantitatively the nonradiological environmental monitoring programs at Monticello Nuclear Generating Plant. The general objective of the study is to assess the effectiveness of monitoring programs in the measurement of environmental impacts. Specific objectives include the following: (1) Assess the validity of environmental impact predictions made in the Environmental Statement by analysis of nonradiological monitoring data; (2) evaluate the general adequacy of environmental monitoring programs for detecting impacts and their responsiveness to Technical Specifications objectives; (3) assess the adequacy of preoperational monitoring programs in providing a sufficient data base for evaluating operational impacts; (4) identify possible impacts that were not predicted in the environmental statement and identify monitoring activities that need to be added, modified or deleted; and (5) assist in identifying environmental impacts, monitoring methods, and measurement problems that need additional research before quantitative predictions can be attempted. Preoperational as well as operational monitoring data were examined to test the usefulness of baseline information in evaluating impacts. This included an examination of the analytical methods used to measure ecological and physical parameters, and an assessment of sampling periodicity and sensitivity where appropriate data were available.

  14. Environmental niche models for riverine desert fishes and their similarity according to phylogeny and functionality

    Science.gov (United States)

    Whitney, James E.; Whittier, Joanna B.; Paukert, Craig

    2017-01-01

    Environmental filtering and competitive exclusion are hypotheses frequently invoked in explaining species' environmental niches (i.e., geographic distributions). A key assumption in both hypotheses is that the functional niche (i.e., species traits) governs the environmental niche, but few studies have rigorously evaluated this assumption. Furthermore, phylogeny could be associated with these hypotheses if it is predictive of functional niche similarity via phylogenetic signal or convergent evolution, or of environmental niche similarity through phylogenetic attraction or repulsion. The objectives of this study were to investigate relationships between environmental niches, functional niches, and phylogenies of fishes of the Upper (UCRB) and Lower (LCRB) Colorado River Basins of southwestern North America. We predicted that functionally similar species would have similar environmental niches (i.e., environmental filtering) and that closely related species would be functionally similar (i.e., phylogenetic signal) and possess similar environmental niches (i.e., phylogenetic attraction). Environmental niches were quantified using environmental niche modeling, and functional similarity was determined using functional trait data. Nonnatives in the UCRB provided the only support for environmental filtering, which resulted from several warmwater nonnatives having dam number as a common predictor of their distributions, whereas several cool- and coldwater nonnatives shared mean annual air temperature as an important distributional predictor. Phylogenetic signal was supported for both natives and nonnatives in both basins. Lastly, phylogenetic attraction was only supported for native fishes in the LCRB and for nonnative fishes in the UCRB. Our results indicated that functional similarity was heavily influenced by evolutionary history, but that phylogenetic relationships and functional traits may not always predict the environmental distribution of species. However, the

  15. Model for predicting mountain wave field uncertainties

    Science.gov (United States)

    Damiens, Florentin; Lott, François; Millet, Christophe; Plougonven, Riwal

    2017-04-01

    Studying the propagation of acoustic waves throughout troposphere requires knowledge of wind speed and temperature gradients from the ground up to about 10-20 km. Typical planetary boundary layers flows are known to present vertical low level shears that can interact with mountain waves, thereby triggering small-scale disturbances. Resolving these fluctuations for long-range propagation problems is, however, not feasible because of computer memory/time restrictions and thus, they need to be parameterized. When the disturbances are small enough, these fluctuations can be described by linear equations. Previous works by co-authors have shown that the critical layer dynamics that occur near the ground produces large horizontal flows and buoyancy disturbances that result in intense downslope winds and gravity wave breaking. While these phenomena manifest almost systematically for high Richardson numbers and when the boundary layer depth is relatively small compare to the mountain height, the process by which static stability affects downslope winds remains unclear. In the present work, new linear mountain gravity wave solutions are tested against numerical predictions obtained with the Weather Research and Forecasting (WRF) model. For Richardson numbers typically larger than unity, the mesoscale model is used to quantify the effect of neglected nonlinear terms on downslope winds and mountain wave patterns. At these regimes, the large downslope winds transport warm air, a so called "Foehn" effect than can impact sound propagation properties. The sensitivity of small-scale disturbances to Richardson number is quantified using two-dimensional spectral analysis. It is shown through a pilot study of subgrid scale fluctuations of boundary layer flows over realistic mountains that the cross-spectrum of mountain wave field is made up of the same components found in WRF simulations. The impact of each individual component on acoustic wave propagation is discussed in terms of

  16. Environmental prediction, risk assessment and extreme events: adaptation strategies for the developing world.

    Science.gov (United States)

    Webster, Peter J; Jian, Jun

    2011-12-13

    The uncertainty associated with predicting extreme weather events has serious implications for the developing world, owing to the greater societal vulnerability to such events. Continual exposure to unanticipated extreme events is a contributing factor for the descent into perpetual and structural rural poverty. We provide two examples of how probabilistic environmental prediction of extreme weather events can support dynamic adaptation. In the current climate era, we describe how short-term flood forecasts have been developed and implemented in Bangladesh. Forecasts of impending floods with horizons of 10 days are used to change agricultural practices and planning, store food and household items and evacuate those in peril. For the first time in Bangladesh, floods were anticipated in 2007 and 2008, with broad actions taking place in advance of the floods, grossing agricultural and household savings measured in units of annual income. We argue that probabilistic environmental forecasts disseminated to an informed user community can reduce poverty caused by exposure to unanticipated extreme events. Second, it is also realized that not all decisions in the future can be made at the village level and that grand plans for water resource management require extensive planning and funding. Based on imperfect models and scenarios of economic and population growth, we further suggest that flood frequency and intensity will increase in the Ganges, Brahmaputra and Yangtze catchments as greenhouse-gas concentrations increase. However, irrespective of the climate-change scenario chosen, the availability of fresh water in the latter half of the twenty-first century seems to be dominated by population increases that far outweigh climate-change effects. Paradoxically, fresh water availability may become more critical if there is no climate change.

  17. National Environmental Policy Act guidance: A model process

    International Nuclear Information System (INIS)

    Angle, B.M.; Lockhart, V.A.T.; Sema, B.; Tuott, L.C.; Irving, J.S.

    1995-04-01

    The ''Model National Environmental Policy Act (NEPA) Process'' includes: References to regulations, guidance documents, and plans; training programs; procedures; and computer databases. Legislative Acts and reference documents from Congress, US Department of Energy, and Lockheed Idaho Technologies Company provide the bases for conducting NEPA at the Idaho National Engineering Laboratory (INEL). Lockheed Idaho Technologies Company (LITCO) NEPA / Permitting Department, the Contractor Environmental Organization (CEO) is responsible for developing and maintaining LITCO NEPA and permitting policies, guidance, and procedures. The CEO develops procedures to conduct environmental evaluations based on NEPA, Council on Environmental Quality (CEQ) regulations, and DOE guidance. This procedure includes preparation or support of environmental checklists, categorical exclusion determinations, environmental assessment determinations, environmental assessments, and environmental impact statements. In addition, the CEO uses this information to train personnel conducting environmental evaluations at the INEL. Streamlining these procedures fosters efficient use of resources, quality documents, and better decisions on proposed actions

  18. Model Predictive Control for an Industrial SAG Mill

    DEFF Research Database (Denmark)

    Ohan, Valeriu; Steinke, Florian; Metzger, Michael

    2012-01-01

    We discuss Model Predictive Control (MPC) based on ARX models and a simple lower order disturbance model. The advantage of this MPC formulation is that it has few tuning parameters and is based on an ARX prediction model that can readily be identied using standard technologies from system identic...

  19. Uncertainties in spatially aggregated predictions from a logistic regression model

    NARCIS (Netherlands)

    Horssen, P.W. van; Pebesma, E.J.; Schot, P.P.

    2002-01-01

    This paper presents a method to assess the uncertainty of an ecological spatial prediction model which is based on logistic regression models, using data from the interpolation of explanatory predictor variables. The spatial predictions are presented as approximate 95% prediction intervals. The

  20. Dealing with missing predictor values when applying clinical prediction models.

    NARCIS (Netherlands)

    Janssen, K.J.; Vergouwe, Y.; Donders, A.R.T.; Harrell Jr, F.E.; Chen, Q.; Grobbee, D.E.; Moons, K.G.

    2009-01-01

    BACKGROUND: Prediction models combine patient characteristics and test results to predict the presence of a disease or the occurrence of an event in the future. In the event that test results (predictor) are unavailable, a strategy is needed to help users applying a prediction model to deal with

  1. Quantitative Structure-Use Relationship Model Predictions to evaluate Tox21 Chemicals as Functional Substitutes and Candidate Alternatives

    Data.gov (United States)

    U.S. Environmental Protection Agency — This dataset provides a prediction for all Tox21 chemicals with available QSUR descriptors across all 41 valid QSUR models developed with FUse. This dataset is...

  2. Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2014-11-01

    Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

  3. Foundation Settlement Prediction Based on a Novel NGM Model

    Directory of Open Access Journals (Sweden)

    Peng-Yu Chen

    2014-01-01

    Full Text Available Prediction of foundation or subgrade settlement is very important during engineering construction. According to the fact that there are lots of settlement-time sequences with a nonhomogeneous index trend, a novel grey forecasting model called NGM (1,1,k,c model is proposed in this paper. With an optimized whitenization differential equation, the proposed NGM (1,1,k,c model has the property of white exponential law coincidence and can predict a pure nonhomogeneous index sequence precisely. We used two case studies to verify the predictive effect of NGM (1,1,k,c model for settlement prediction. The results show that this model can achieve excellent prediction accuracy; thus, the model is quite suitable for simulation and prediction of approximate nonhomogeneous index sequence and has excellent application value in settlement prediction.

  4. Theoretical predictions of thermodynamic parameters of adsorption of nitrogen containing environmental contaminants on kaolinite.

    Science.gov (United States)

    Scott, Andrea Michalkova; Burns, Elizabeth A; Lafferty, Brandon J; Hill, Frances C

    2015-02-01

    In this study thermodynamic parameters of adsorption of nitrogen containing environmental contaminants (NCCs, 2,4,6, trinitrotoluene (TNT), 2,4-dinitrotoluene (DNT), 2,4-dinitroanisole (DNAN), and 3-one-1,2,4-triazol-5-one (NTO)) interacting with the tetrahedral and octahedral surfaces of kaolinite were predicted. Adsorption complexes were investigated using a density functional theory and both periodic and cluster approach. The complexes, modeled using the periodic boundary conditions approach, were fully optimized at the BLYP-D2 level to obtain the structures and adsorption energies. The relaxed kaolinite-NCCs structures were used to prepare cluster models to calculate thermodynamic parameters and partition coefficients at the M06-2X-D3 and BLYP-D2 levels from the gas phase. The entropy effect on the Gibbs free energies of adsorption of NCCS on kaolinite was also studied and compared with available experimental data. The results showed that in all calculated models, the NCCs molecules are physisorbed and they favor a parallel orientation toward both kaolinite surfaces. It was found that all calculated NCCs compounds are more stable on the octahedral than on the tetrahedral surface of kaolinite. The Gibbs free energies and partition coefficients were also predicted for interactions of NCCs with Na-kaolinite from aqueous solution. Calculations revealed adsorption of NCCs is effective from the gas phase on both cation free kaolinite surfaces and on Na-kaolinite from aqueous solution at room temperature. Theoretical data were validated against experimental results, and the reasons for small differences between calculated and measured partition coefficients are discussed.

  5. Environmental factors predict the severity of delirium symptoms in long-term care residents with and without delirium.

    Science.gov (United States)

    McCusker, Jane; Cole, Martin G; Voyer, Philippe; Vu, Minh; Ciampi, Antonio; Monette, Johanne; Champoux, Nathalie; Belzile, Eric; Dyachenko, Alina

    2013-04-01

    To identify potentially modifiable environmental factors (including number of medications) associated with changes over time in the severity of delirium symptoms and to explore the interactions between these factors and resident baseline vulnerability. Prospective, observational cohort study. Seven long-term care (LTC) facilities. Two hundred seventy-two LTC residents aged 65 and older with and without delirium. Weekly assessments (for up to 6 months) of the severity of delirium symptoms using the Delirium Index (DI), environmental risk factors, and number of medications. Baseline vulnerability measures included a diagnosis of dementia and a delirium risk score. Associations between environmental factors, medications, and weekly changes in DI were analyzed using a general linear model with correlated errors. Six potentially modifiable environmental factors predicted weekly changes in DI (absence of reading glasses, aids to orientation, family member, and glass of water and presence of bed rails and other restraints) as did the prescription of two or more new medications. Residents with dementia appeared to be more sensitive to the effects of these factors. Six environmental factors and prescription of two or more new medications predicted changes in the severity of delirium symptoms. These risk factors are potentially modifiable through improved LTC clinical practices. © 2013, Copyright the Authors Journal compilation © 2013, The American Geriatrics Society.

  6. Predictive capabilities of various constitutive models for arterial tissue.

    Science.gov (United States)

    Schroeder, Florian; Polzer, Stanislav; Slažanský, Martin; Man, Vojtěch; Skácel, Pavel

    2018-02-01

    Aim of this study is to validate some constitutive models by assessing their capabilities in describing and predicting uniaxial and biaxial behavior of porcine aortic tissue. 14 samples from porcine aortas were used to perform 2 uniaxial and 5 biaxial tensile tests. Transversal strains were furthermore stored for uniaxial data. The experimental data were fitted by four constitutive models: Holzapfel-Gasser-Ogden model (HGO), model based on generalized structure tensor (GST), Four-Fiber-Family model (FFF) and Microfiber model. Fitting was performed to uniaxial and biaxial data sets separately and descriptive capabilities of the models were compared. Their predictive capabilities were assessed in two ways. Firstly each model was fitted to biaxial data and its accuracy (in term of R 2 and NRMSE) in prediction of both uniaxial responses was evaluated. Then this procedure was performed conversely: each model was fitted to both uniaxial tests and its accuracy in prediction of 5 biaxial responses was observed. Descriptive capabilities of all models were excellent. In predicting uniaxial response from biaxial data, microfiber model was the most accurate while the other models showed also reasonable accuracy. Microfiber and FFF models were capable to reasonably predict biaxial responses from uniaxial data while HGO and GST models failed completely in this task. HGO and GST models are not capable to predict biaxial arterial wall behavior while FFF model is the most robust of the investigated constitutive models. Knowledge of transversal strains in uniaxial tests improves robustness of constitutive models. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Comparing National Water Model Inundation Predictions with Hydrodynamic Modeling

    Science.gov (United States)

    Egbert, R. J.; Shastry, A.; Aristizabal, F.; Luo, C.

    2017-12-01

    The National Water Model (NWM) simulates the hydrologic cycle and produces streamflow forecasts, runoff, and other variables for 2.7 million reaches along the National Hydrography Dataset for the continental United States. NWM applies Muskingum-Cunge channel routing which is based on the continuity equation. However, the momentum equation also needs to be considered to obtain better estimates of streamflow and stage in rivers especially for applications such as flood inundation mapping. Simulation Program for River NeTworks (SPRNT) is a fully dynamic model for large scale river networks that solves the full nonlinear Saint-Venant equations for 1D flow and stage height in river channel networks with non-uniform bathymetry. For the current work, the steady-state version of the SPRNT model was leveraged. An evaluation on SPRNT's and NWM's abilities to predict inundation was conducted for the record flood of Hurricane Matthew in October 2016 along the Neuse River in North Carolina. This event was known to have been influenced by backwater effects from the Hurricane's storm surge. Retrospective NWM discharge predictions were converted to stage using synthetic rating curves. The stages from both models were utilized to produce flood inundation maps using the Height Above Nearest Drainage (HAND) method which uses the local relative heights to provide a spatial representation of inundation depths. In order to validate the inundation produced by the models, Sentinel-1A synthetic aperture radar data in the VV and VH polarizations along with auxiliary data was used to produce a reference inundation map. A preliminary, binary comparison of the inundation maps to the reference, limited to the five HUC-12 areas of Goldsboro, NC, yielded that the flood inundation accuracies for NWM and SPRNT were 74.68% and 78.37%, respectively. The differences for all the relevant test statistics including accuracy, true positive rate, true negative rate, and positive predictive value were found

  8. Towards a Stochastic Predictive Understanding of Ecosystem Functioning and Resilience to Environmental Changes

    Science.gov (United States)

    Pappas, C.

    2017-12-01

    Terrestrial ecosystem processes respond differently to hydrometeorological variability across timescales, and so does our scientific understanding of the underlying mechanisms. Process-based modeling of ecosystem functioning is therefore challenging, especially when long-term predictions are envisioned. Here we analyze the statistical properties of hydrometeorological and ecosystem variability, i.e., the variability of ecosystem process related to vegetation carbon dynamics, from hourly to decadal timescales. 23 extra-tropical forest sites, covering different climatic zones and vegetation characteristics, are examined. Micrometeorological and reanalysis data of precipitation, air temperature, shortwave radiation and vapor pressure deficit are used to describe hydrometeorological variability. Ecosystem variability is quantified using long-term eddy covariance flux data of hourly net ecosystem exchange of CO2 between land surface and atmosphere, monthly remote sensing vegetation indices, annual tree-ring widths and above-ground biomass increment estimates. We find that across sites and timescales ecosystem variability is confined within a hydrometeorological envelope that describes the range of variability of the available resources, i.e., water and energy. Furthermore, ecosystem variability demonstrates long-term persistence, highlighting ecological memory and slow ecosystem recovery rates after disturbances. We derive an analytical model, combining deterministic harmonics and stochastic processes, that represents major mechanisms and uncertainties and mimics the observed pattern of hydrometeorological and ecosystem variability. This stochastic framework offers a parsimonious and mathematically tractable approach for modelling ecosystem functioning and for understanding its response and resilience to environmental changes. Furthermore, this framework reflects well the observed ecological memory, an inherent property of ecosystem functioning that is currently not

  9. Predictive models for moving contact line flows

    Science.gov (United States)

    Rame, Enrique; Garoff, Stephen

    2003-01-01

    Modeling flows with moving contact lines poses the formidable challenge that the usual assumptions of Newtonian fluid and no-slip condition give rise to a well-known singularity. This singularity prevents one from satisfying the contact angle condition to compute the shape of the fluid-fluid interface, a crucial calculation without which design parameters such as the pressure drop needed to move an immiscible 2-fluid system through a solid matrix cannot be evaluated. Some progress has been made for low Capillary number spreading flows. Combining experimental measurements of fluid-fluid interfaces very near the moving contact line with an analytical expression for the interface shape, we can determine a parameter that forms a boundary condition for the macroscopic interface shape when Ca much les than l. This parameter, which plays the role of an "apparent" or macroscopic dynamic contact angle, is shown by the theory to depend on the system geometry through the macroscopic length scale. This theoretically established dependence on geometry allows this parameter to be "transferable" from the geometry of the measurement to any other geometry involving the same material system. Unfortunately this prediction of the theory cannot be tested on Earth.

  10. Developmental prediction model for early alcohol initiation in Dutch adolescents

    NARCIS (Netherlands)

    Geels, L.M.; Vink, J.M.; Beijsterveldt, C.E.M. van; Bartels, M.; Boomsma, D.I.

    2013-01-01

    Objective: Multiple factors predict early alcohol initiation in teenagers. Among these are genetic risk factors, childhood behavioral problems, life events, lifestyle, and family environment. We constructed a developmental prediction model for alcohol initiation below the Dutch legal drinking age

  11. Environmental Impacts of Large Scale Biochar Application Through Spatial Modeling

    Science.gov (United States)

    Huber, I.; Archontoulis, S.

    2017-12-01

    In an effort to study the environmental (emissions, soil quality) and production (yield) impacts of biochar application at regional scales we coupled the APSIM-Biochar model with the pSIMS parallel platform. So far the majority of biochar research has been concentrated on lab to field studies to advance scientific knowledge. Regional scale assessments are highly needed to assist decision making. The overall objective of this simulation study was to identify areas in the USA that have the most gain environmentally from biochar's application, as well as areas which our model predicts a notable yield increase due to the addition of biochar. We present the modifications in both APSIM biochar and pSIMS components that were necessary to facilitate these large scale model runs across several regions in the United States at a resolution of 5 arcminutes. This study uses the AgMERRA global climate data set (1980-2010) and the Global Soil Dataset for Earth Systems modeling as a basis for creating its simulations, as well as local management operations for maize and soybean cropping systems and different biochar application rates. The regional scale simulation analysis is in progress. Preliminary results showed that the model predicts that high quality soils (particularly those common to Iowa cropping systems) do not receive much, if any, production benefit from biochar. However, soils with low soil organic matter ( 0.5%) do get a noteworthy yield increase of around 5-10% in the best cases. We also found N2O emissions to be spatial and temporal specific; increase in some areas and decrease in some other areas due to biochar application. In contrast, we found increases in soil organic carbon and plant available water in all soils (top 30 cm) due to biochar application. The magnitude of these increases (% change from the control) were larger in soil with low organic matter (below 1.5%) and smaller in soils with high organic matter (above 3%) and also dependent on biochar

  12. Proposing an Environmental Excellence Self-Assessment Model

    DEFF Research Database (Denmark)

    Meulengracht Jensen, Peter; Johansen, John; Wæhrens, Brian Vejrum

    2013-01-01

    This paper presents an Environmental Excellence Self-Assessment (EEA) model based on the structure of the European Foundation of Quality Management Business Excellence Framework. Four theoretical scenarios for deploying the model are presented as well as managerial implications, suggesting...

  13. Seasonal predictability of Kiremt rainfall in coupled general circulation models

    Science.gov (United States)

    Gleixner, Stephanie; Keenlyside, Noel S.; Demissie, Teferi D.; Counillon, François; Wang, Yiguo; Viste, Ellen

    2017-11-01

    The Ethiopian economy and population is strongly dependent on rainfall. Operational seasonal predictions for the main rainy season (Kiremt, June-September) are based on statistical approaches with Pacific sea surface temperatures (SST) as the main predictor. Here we analyse dynamical predictions from 11 coupled general circulation models for the Kiremt seasons from 1985-2005 with the forecasts starting from the beginning of May. We find skillful predictions from three of the 11 models, but no model beats a simple linear prediction model based on the predicted Niño3.4 indices. The skill of the individual models for dynamically predicting Kiremt rainfall depends on the strength of the teleconnection between Kiremt rainfall and concurrent Pacific SST in the models. Models that do not simulate this teleconnection fail to capture the observed relationship between Kiremt rainfall and the large-scale Walker circulation.

  14. Modelling consumers' preferences for Novel Protein Foods and environmental quality

    NARCIS (Netherlands)

    Zhu, X.; Ierland, van E.C.

    2005-01-01

    We develop a theoretical Applied General Equilibrium (AGE) model that explicitly includes the environmental input in production functions and the consumers' preferences for environmental quality in utility functions. We empirically apply the model to provide some insights into the effects of the

  15. MODELLING OF DYNAMIC SPEED LIMITS USING THE MODEL PREDICTIVE CONTROL

    Directory of Open Access Journals (Sweden)

    Andrey Borisovich Nikolaev

    2017-09-01

    Full Text Available The article considers the issues of traffic management using intelligent system “Car-Road” (IVHS, which consist of interacting intelligent vehicles (IV and intelligent roadside controllers. Vehicles are organized in convoy with small distances between them. All vehicles are assumed to be fully automated (throttle control, braking, steering. Proposed approaches for determining speed limits for traffic cars on the motorway using a model predictive control (MPC. The article proposes an approach to dynamic speed limit to minimize the downtime of vehicles in traffic.

  16. Environmental noise prediction, noise mapping and GIS integration : the case of inner Dublin, Ireland

    OpenAIRE

    Murphy, Enda; Rice, Henry J.; Meskell, Craig

    2006-01-01

    The recent Environmental Noise Directive (END) of the European Union (EU) requires that noise maps and action plans are compiled for agglomerations with a population greater than 250,000 individuals. This paper reports on research conducted to predict and map road transport noise for a study area in central Dublin. Noise emission levels were calculated for Lden and Lnight using the Harmonoise prediction method as recommended by the European Union. Emphasis was placed on integratin...

  17. Climate modelling, uncertainty and responses to predictions of change

    International Nuclear Information System (INIS)

    Henderson-Sellers, A.

    1996-01-01

    Article 4.1(F) of the Framework Convention on Climate Change commits all parties to take climate change considerations into account, to the extent feasible, in relevant social, economic and environmental policies and actions and to employ methods such as impact assessments to minimize adverse effects of climate change. This could be achieved by, inter alia, incorporating climate change risk assessment into development planning processes, i.e. relating climatic change to issues of habitability and sustainability. Adaptation is an ubiquitous and beneficial natural and human strategy. Future adaptation (adjustment) to climate is inevitable at the least to decrease the vulnerability to current climatic impacts. An urgent issue is the mismatch between the predictions of global climatic change and the need for information on local to regional change in order to develop adaptation strategies. Mitigation efforts are essential since the more successful mitigation activities are, the less need there will be for adaptation responses. And, mitigation responses can be global (e.g. a uniform percentage reduction in greenhouse gas emissions) while adaptation responses will be local to regional in character and therefore depend upon confident predictions of regional climatic change. The dilemma facing policymakers is that scientists have considerable confidence in likely global climatic changes but virtually zero confidence in regional changes. Mitigation and adaptation strategies relevant to climatic change can most usefully be developed in the context of sound understanding of climate, especially the near-surface continental climate, permitting discussion of societally relevant issues. But, climate models can't yet deliver this type of regionally and locationally specific prediction and some aspects of current research even seem to indicate increased uncertainty. These topics are explored in this paper using the specific example of the prediction of land-surface climate changes

  18. Predicting the profile of nutrients available for absorption: from nutrient requirement to animal response and environmental impact.

    Science.gov (United States)

    Dijkstra, J; Kebreab, E; Mills, J A N; Pellikaan, W F; López, S; Bannink, A; France, J

    2007-02-01

    Current feed evaluation systems for dairy cattle aim to match nutrient requirements with nutrient intake at pre-defined production levels. These systems were not developed to address, and are not suitable to predict, the responses to dietary changes in terms of production level and product composition, excretion of nutrients to the environment, and nutrition related disorders. The change from a requirement to a response system to meet the needs of various stakeholders requires prediction of the profile of absorbed nutrients and its subsequent utilisation for various purposes. This contribution examines the challenges to predicting the profile of nutrients available for absorption in dairy cattle and provides guidelines for further improved prediction with regard to animal production responses and environmental pollution.The profile of nutrients available for absorption comprises volatile fatty acids, long-chain fatty acids, amino acids and glucose. Thus the importance of processes in the reticulo-rumen is obvious. Much research into rumen fermentation is aimed at determination of substrate degradation rates. Quantitative knowledge on rates of passage of nutrients out of the rumen is rather limited compared with that on degradation rates, and thus should be an important theme in future research. Current systems largely ignore microbial metabolic variation, and extant mechanistic models of rumen fermentation give only limited attention to explicit representation of microbial metabolic activity. Recent molecular techniques indicate that knowledge on the presence and activity of various microbial species is far from complete. Such techniques may give a wealth of information, but to include such findings in systems predicting the nutrient profile requires close collaboration between molecular scientists and mathematical modellers on interpreting and evaluating quantitative data. Protozoal metabolism is of particular interest here given the paucity of quantitative data

  19. Accommodating environmental variation in population models: metaphysiological biomass loss accounting.

    Science.gov (United States)

    Owen-Smith, Norman

    2011-07-01

    1. There is a pressing need for population models that can reliably predict responses to changing environmental conditions and diagnose the causes of variation in abundance in space as well as through time. In this 'how to' article, it is outlined how standard population models can be modified to accommodate environmental variation in a heuristically conducive way. This approach is based on metaphysiological modelling concepts linking populations within food web contexts and underlying behaviour governing resource selection. Using population biomass as the currency, population changes can be considered at fine temporal scales taking into account seasonal variation. Density feedbacks are generated through the seasonal depression of resources even in the absence of interference competition. 2. Examples described include (i) metaphysiological modifications of Lotka-Volterra equations for coupled consumer-resource dynamics, accommodating seasonal variation in resource quality as well as availability, resource-dependent mortality and additive predation, (ii) spatial variation in habitat suitability evident from the population abundance attained, taking into account resource heterogeneity and consumer choice using empirical data, (iii) accommodating population structure through the variable sensitivity of life-history stages to resource deficiencies, affecting susceptibility to oscillatory dynamics and (iv) expansion of density-dependent equations to accommodate various biomass losses reducing population growth rate below its potential, including reductions in reproductive outputs. Supporting computational code and parameter values are provided. 3. The essential features of metaphysiological population models include (i) the biomass currency enabling within-year dynamics to be represented appropriately, (ii) distinguishing various processes reducing population growth below its potential, (iii) structural consistency in the representation of interacting populations and

  20. Interpreting predictive maps of disease: highlighting the pitfalls of distribution models in epidemiology

    Directory of Open Access Journals (Sweden)

    Nicola A. Wardrop

    2014-11-01

    Full Text Available The application of spatial modelling to epidemiology has increased significantly over the past decade, delivering enhanced understanding of the environmental and climatic factors affecting disease distributions and providing spatially continuous representations of disease risk (predictive maps. These outputs provide significant information for disease control programmes, allowing spatial targeting and tailored interventions. However, several factors (e.g. sampling protocols or temporal disease spread can influence predictive mapping outputs. This paper proposes a conceptual framework which defines several scenarios and their potential impact on resulting predictive outputs, using simulated data to provide an exemplar. It is vital that researchers recognise these scenarios and their influence on predictive models and their outputs, as a failure to do so may lead to inaccurate interpretation of predictive maps. As long as these considerations are kept in mind, predictive mapping will continue to contribute significantly to epidemiological research and disease control planning.

  1. Predictability in models of the atmospheric circulation

    NARCIS (Netherlands)

    Houtekamer, P.L.

    1992-01-01

    It will be clear from the above discussions that skill forecasts are still in their infancy. Operational skill predictions do not exist. One is still struggling to prove that skill predictions, at any range, have any quality at all. It is not clear what the statistics of the analysis error

  2. Predictive models for Escherichia coli concentrations at inland lake beaches and relationship of model variables to pathogen detection

    Science.gov (United States)

    Methods are needed improve the timeliness and accuracy of recreational water‐quality assessments. Traditional culture methods require 18–24 h to obtain results and may not reflect current conditions. Predictive models, based on environmental and water quality variables, have been...

  3. Research on System Coherence Evolution of Different Environmental Models

    Science.gov (United States)

    Zhang, Si-Qi; Lu, Jing-Bin; Li, Hong; Liu, Ji-Ping; Zhang, Xiao-Ru; Liu, Han; Liang, Yu; Ma, Ji; Liu, Xiao-Jing; Wu, Xiang-Yao

    2017-12-01

    In this paper, we have studied the evolution curve of two-level atomic system that the initial state is excited state. At the different of environmental reservoir models, which include the single Lorentzian, ideal photon band-gap, double Lorentzian and square Lorentzian reservoir, we researched the influence of these environmental reservoir models on the evolution of energy level population. At static no modulation, comparing the four environmental models, the atomic energy level population oscillation of square Lorentzian reservoir model is fastest, and the atomic system decoherence is slowest. Under dynamic modulation, comparing the photon band-gap model with the single Lorentzian reservoir model, no matter what form of dynamic modulation, the time of atoms decay to the ground state is longer for the photonic band-gap model. These conclusions make the idea of using the environmental change to modulate the coherent evolution of atomic system become true.

  4. Environmental fate of rice paddy pesticides in a model ecosystem.

    Science.gov (United States)

    Tomizawa, C; Kazano, H

    1979-01-01

    The distribution and metabolic fate of several rice paddy pesticides were evaluated in a modified model ecosystem. Among the three BHC isomers, beta-isomer was the most stable and bioconcentrated in all of the organisms. Alpha- and gamma-isomers were moderately persistent and degraded to some extent during the 33 day period. Disulfoton was relatively persistent due to the transformation to its oxidation products. Pyridaphenthion was fairly biodegradable. N-Phenyl maleic hydrazide derived from the hydrolysis of pyridaphenthion was not detected in the organisms though it was found in the aquarium water after 33 days. Cartap and edifenphos were considerably biodegradable, and the ratio of the conversion to water soluble metabolites was very high. There was a distinct difference in the persistence of Kitazin P and edifenphos in the aquarium water. It appeared that the hydrolysis rate of the pesticides affected their fate in the organisms. PCP appeared to be moderately biodegradable. CNP was considerably stable and stored in the organisms though the concentration in the aquarium water was relatively low. The persistence and distribution of the pesticides in the model ecosystem were dependent on their chemical structures. In spite of the limitation derived from short experimental period, the model ecosystem may be applicable for predicting the environmental fate of pesticides.

  5. Required Collaborative Work in Online Courses: A Predictive Modeling Approach

    Science.gov (United States)

    Smith, Marlene A.; Kellogg, Deborah L.

    2015-01-01

    This article describes a predictive model that assesses whether a student will have greater perceived learning in group assignments or in individual work. The model produces correct classifications 87.5% of the time. The research is notable in that it is the first in the education literature to adopt a predictive modeling methodology using data…

  6. Models for predicting compressive strength and water absorption of ...

    African Journals Online (AJOL)

    This work presents a mathematical model for predicting the compressive strength and water absorption of laterite-quarry dust cement block using augmented Scheffe's simplex lattice design. The statistical models developed can predict the mix proportion that will yield the desired property. The models were tested for lack of ...

  7. Environmental concentrations of engineered nanomaterials: Review of modeling and analytical studies

    International Nuclear Information System (INIS)

    Gottschalk, Fadri; Sun, TianYin; Nowack, Bernd

    2013-01-01

    Scientific consensus predicts that the worldwide use of engineered nanomaterials (ENM) leads to their release into the environment. We reviewed the available literature concerning environmental concentrations of six ENMs (TiO 2 , ZnO, Ag, fullerenes, CNT and CeO 2 ) in surface waters, wastewater treatment plant effluents, biosolids, sediments, soils and air. Presently, a dozen modeling studies provide environmental concentrations for ENM and a handful of analytical works can be used as basis for a preliminary validation. There are still major knowledge gaps (e.g. on ENM production, application and release) that affect the modeled values, but over all an agreement on the order of magnitude of the environmental concentrations can be reached. True validation of the modeled values is difficult because trace analytical methods that are specific for ENM detection and quantification are not available. The modeled and measured results are not always comparable due to the different forms and sizes of particles that these two approaches target. -- Highlights: •Modeled environmental concentrations of engineered nanomaterials are reviewed. •Measured environmental concentrations of engineered nanomaterials are reviewed. •Possible validation of modeled data by measurements is critically evaluated. •Different approaches in modeling and measurement methods complicate validation. -- Modeled and measured environmental concentrations of engineered nanomaterials are reviewed and critically discussed

  8. A review of mathematical models in economic environmental problems

    DEFF Research Database (Denmark)

    Nahorski, Z.; Ravn, H.F.

    2000-01-01

    The paper presents a review of mathematical models used,in economic analysis of environmental problems. This area of research combines macroeconomic models of growth, as dependent on capital, labour, resources, etc., with environmental models describing such phenomena like natural resources...... exhaustion or pollution accumulation and degradation. In simpler cases the models can be treated analytically and the utility function can be optimized using, e.g., such tools as the maximum principle. In more complicated cases calculation of the optimal environmental policies requires a computer solution....

  9. Models of Eucalypt phenology predict bat population flux.

    Science.gov (United States)

    Giles, John R; Plowright, Raina K; Eby, Peggy; Peel, Alison J; McCallum, Hamish

    2016-10-01

    Fruit bats (Pteropodidae) have received increased attention after the recent emergence of notable viral pathogens of bat origin. Their vagility hinders data collection on abundance and distribution, which constrains modeling efforts and our understanding of bat ecology, viral dynamics, and spillover. We addressed this knowledge gap with models and data on the occurrence and abundance of nectarivorous fruit bat populations at 3 day roosts in southeast Queensland. We used environmental drivers of nectar production as predictors and explored relationships between bat abundance and virus spillover. Specifically, we developed several novel modeling tools motivated by complexities of fruit bat foraging ecology, including: (1) a dataset of spatial variables comprising Eucalypt-focused vegetation indices, cumulative precipitation, and temperature anomaly; (2) an algorithm that associated bat population response with spatial covariates in a spatially and temporally relevant way given our current understanding of bat foraging behavior; and (3) a thorough statistical learning approach to finding optimal covariate combinations. We identified covariates that classify fruit bat occupancy at each of our three study roosts with 86-93% accuracy. Negative binomial models explained 43-53% of the variation in observed abundance across roosts. Our models suggest that spatiotemporal heterogeneity in Eucalypt-based food resources could drive at least 50% of bat population behavior at the landscape scale. We found that 13 spillover events were observed within the foraging range of our study roosts, and they occurred during times when models predicted low population abundance. Our results suggest that, in southeast Queensland, spillover may not be driven by large aggregations of fruit bats attracted by nectar-based resources, but rather by behavior of smaller resident subpopulations. Our models and data integrated remote sensing and statistical learning to make inferences on bat ecology

  10. Neighbourhood, Route and Workplace-Related Environmental Characteristics Predict Adults' Mode of Travel to Work.

    Directory of Open Access Journals (Sweden)

    Alice M Dalton

    Full Text Available Commuting provides opportunities for regular physical activity which can reduce the risk of chronic disease. Commuters' mode of travel may be shaped by their environment, but understanding of which specific environmental characteristics are most important and might form targets for intervention is limited. This study investigated associations between mode choice and a range of objectively assessed environmental characteristics.Participants in the Commuting and Health in Cambridge study reported where they lived and worked, their usual mode of travel to work and a variety of socio-demographic characteristics. Using geographic information system (GIS software, 30 exposure variables were produced capturing characteristics of areas around participants' homes and workplaces and their shortest modelled routes to work. Associations between usual mode of travel to work and personal and environmental characteristics were investigated using multinomial logistic regression.Of the 1124 respondents, 50% reported cycling or walking as their usual mode of travel to work. In adjusted analyses, home-work distance was strongly associated with mode choice, particularly for walking. Lower odds of walking or cycling rather than driving were associated with a less frequent bus service (highest versus lowest tertile: walking OR 0.61 [95% CI 0.20-1.85]; cycling OR 0.43 [95% CI 0.23-0.83], low street connectivity (OR 0.22, [0.07-0.67]; OR 0.48 [0.26-0.90] and free car parking at work (OR 0.24 [0.10-0.59]; OR 0.55 [0.32-0.95]. Participants were less likely to cycle if they had access to fewer destinations (leisure facilities, shops and schools close to work (OR 0.36 [0.21-0.62] and a railway station further from home (OR 0.53 [0.30-0.93]. Covariates strongly predicted travel mode (pseudo r-squared 0.74.Potentially modifiable environmental characteristics, including workplace car parking, street connectivity and access to public transport, are associated with travel mode

  11. Neighbourhood, Route and Workplace-Related Environmental Characteristics Predict Adults' Mode of Travel to Work.

    Science.gov (United States)

    Dalton, Alice M; Jones, Andrew P; Panter, Jenna R; Ogilvie, David

    2013-01-01

    Commuting provides opportunities for regular physical activity which can reduce the risk of chronic disease. Commuters' mode of travel may be shaped by their environment, but understanding of which specific environmental characteristics are most important and might form targets for intervention is limited. This study investigated associations between mode choice and a range of objectively assessed environmental characteristics. Participants in the Commuting and Health in Cambridge study reported where they lived and worked, their usual mode of travel to work and a variety of socio-demographic characteristics. Using geographic information system (GIS) software, 30 exposure variables were produced capturing characteristics of areas around participants' homes and workplaces and their shortest modelled routes to work. Associations between usual mode of travel to work and personal and environmental characteristics were investigated using multinomial logistic regression. Of the 1124 respondents, 50% reported cycling or walking as their usual mode of travel to work. In adjusted analyses, home-work distance was strongly associated with mode choice, particularly for walking. Lower odds of walking or cycling rather than driving were associated with a less frequent bus service (highest versus lowest tertile: walking OR 0.61 [95% CI 0.20-1.85]; cycling OR 0.43 [95% CI 0.23-0.83]), low street connectivity (OR 0.22, [0.07-0.67]; OR 0.48 [0.26-0.90]) and free car parking at work (OR 0.24 [0.10-0.59]; OR 0.55 [0.32-0.95]). Participants were less likely to cycle if they had access to fewer destinations (leisure facilities, shops and schools) close to work (OR 0.36 [0.21-0.62]) and a railway station further from home (OR 0.53 [0.30-0.93]). Covariates strongly predicted travel mode (pseudo r-squared 0.74). Potentially modifiable environmental characteristics, including workplace car parking, street connectivity and access to public transport, are associated with travel mode choice

  12. Implementing algorithms for modelling and prediction of sea level change using threshold models

    Science.gov (United States)

    Hewelt, M.; Miziński, B.; Niedzielski, T.

    2012-04-01

    The aim of this work is to present how threshold time series models can be used to model sea level change recorded in gridded time series data and to predict such time-varying maps. This task is carried out mostly in R, the Language and Environment, using satellite altimetric gridded time series from the Archiving, Validation and Interpretation of Satellite Oceanographic data (AVISO). During El Niño/Southern Oscillation (ENSO) warm and cold episodes sea level anomalies exceed certain thresholds, principally in the equatorial Pacific and in the tropical Indian Ocean. This encourages to use threshold autoregressive models to predict sea level change, particularly in the aforementioned locations. It is likely, however, that during the ENSO mode one should use the models which differ from those suitable for normal environmental conditions. Associated with this is a notion of threshold that allows one to determine various models if a certain limit value is attained or exceeded. Firstly, having the global mean sea level anomaly data spanning the time interval from 1992 onwards, available courtesy of AVISO, the autoregressive threshold model is fitted in R. Subsequently, the global mean sea level change univariate time series is forecasted, and various lead times are adopted. Secondly, based on the gridded delayed-time data as well as their near-real time equivalents provided by AVISO, predictions of sea level change determined as a function of latitude and longitude, and with various lead times, are produced. Due to the fact, that the near-real time data are being automatically updated at the local server in Wroclaw, Poland, it is possible to generate new predictions every day automatically. Such a forecasting process, which intrinsically involves the automated verification and quality control modules, is based on the above-mentioned threshold models as well as polynomial-harmonic deterministic empirical functions.

  13. MEASURED CONCENTRATIONS OF HERBICIDES AND MODEL PREDICTIONS OF ATRAZINE FATE IN THE PATUXENT RIVER ESTUARY

    Science.gov (United States)

    McConnell, Laura L., Jennifer A. Harman-Fetcho and James D. Hagy, III. 2004. Measured Concentrations of Herbicides and Model Predictions of Atrazine Fate in the Patuxent River Estuary. J. Environ. Qual. 33(2):594-604. (ERL,GB X1051). The environmental fate of herbicides i...

  14. Sexual Recruitment in Zostera marina: Progress toward a Predictive Model.

    Science.gov (United States)

    Furman, Bradley T; Peterson, Bradley J

    2015-01-01

    Ecophysiological stress and physical disturbance are capable of structuring meadows through a combination of direct biomass removal and recruitment limitation; however, predicting these effects at landscape scales has rarely been successful. To model environmental influence on sexual recruitment in perennial Zostera marina, we selected a sub-tidal, light-replete study site with seasonal extremes in temperature and wave energy. During an 8-year observation period, areal coverage increased from 4.8 to 42.7%. Gains were stepwise in pattern, attributable to annual recruitment of patches followed by centrifugal growth and coalescence. Recruitment varied from 13 to 4,894 patches per year. Using a multiple linear regression approach, we examined the association between patch appearance and relative wave energy, atmospheric condition and water temperature. Two models were developed, one appropriate for the dispersal of naked seeds, and another for rafted flowers. Results indicated that both modes of sexual recruitment varied as functions of wind, temperature, rainfall and wave energy, with a regime shift in wind-wave energy corresponding to periods of rapid colonization within our site. Temporal correlations between sexual recruitment and time-lagged climatic summaries highlighted floral induction, seed bank and small patch development as periods of vulnerability. Given global losses in seagrass coverage, regions of recovery and re-colonization will become increasingly important. Lacking landscape-scale process models for seagrass recruitment, temporally explicit statistical approaches presented here could be used to forecast colonization trajectories and to provide managers with real-time estimates of future meadow performance; i.e., when to expect a good year in terms of seagrass expansion. To facilitate use as forecasting tools, we did not use statistical composites or normalized variables as our predictors. This study, therefore, represents a first step toward linking

  15. Thermodynamic network model for predicting effects of substrate addition and other perturbations on subsurface microbial communities

    Energy Technology Data Exchange (ETDEWEB)

    Jack Istok; Melora Park; James McKinley; Chongxuan Liu; Lee Krumholz; Anne Spain; Aaron Peacock; Brett Baldwin

    2007-04-19

    The overall goal of this project is to develop and test a thermodynamic network model for predicting the effects of substrate additions and environmental perturbations on microbial growth, community composition and system geochemistry. The hypothesis is that a thermodynamic analysis of the energy-yielding growth reactions performed by defined groups of microorganisms can be used to make quantitative and testable predictions of the change in microbial community composition that will occur when a substrate is added to the subsurface or when environmental conditions change.

  16. Studying the time scale dependence of environmental variables predictability using fractal analysis.

    Science.gov (United States)

    Yuval; Broday, David M

    2010-06-15

    Prediction of meteorological and air quality variables motivates a lot of research in the atmospheric sciences and exposure assessment communities. An interesting related issue regards the relative predictive power that can be expected at different time scales, and whether it vanishes altogether at certain ranges. An improved understanding of our predictive powers enables better environmental management and more efficient decision making processes. Fractal analysis is commonly used to characterize the self-affinity of time series. This work introduces the Continuous Wavelet Transform (CWT) fractal analysis method as a tool for assessing environmental time series predictability. The high temporal scale resolution of the CWT enables detailed information about the Hurst parameter, a common temporal fractality measure, and thus about time scale variations in predictability. We analyzed a few years records of half-hourly air pollution and meteorological time series from which the trivial seasonal and daily cycles were removed. We encountered a general trend of decreasing Hurst values from about 1.4 (good autocorrelation and predictability), in the sub-daily time scale to 0.5 (which implies complete randomness) in the monthly to seasonal scales. The air pollutants predictability follows that of the meteorological variables in the short time scales but is better at longer scales.

  17. Uncertainty in Discount Models and Environmental Accounting

    Directory of Open Access Journals (Sweden)

    Donald Ludwig

    2005-12-01

    Full Text Available Cost-benefit analysis (CBA is controversial for environmental issues, but is nevertheless employed by many governments and private organizations for making environmental decisions. Controversy centers on the practice of economic discounting in CBA for decisions that have substantial long-term consequences, as do most environmental decisions. Customarily, economic discounting has been calculated at a constant exponential rate, a practice that weights the present heavily in comparison with the future. Recent analyses of economic data show that the assumption of constant exponential discounting should be modified to take into account large uncertainties in long-term discount rates. A proper treatment of this uncertainty requires that we consider returns over a plausible range of assumptions about future discounting rates. When returns are averaged in this way, the schemes with the most severe discounting have a negligible effect on the average after a long period of time has elapsed. This re-examination of economic uncertainty provides support for policies that prevent or mitigate environmental damage. We examine these effects for three examples: a stylized renewable resource, management of a long-lived species (Atlantic Right Whales, and lake eutrophication.

  18. Regression models for predicting anthropometric measurements of ...

    African Journals Online (AJOL)

    measure anthropometric dimensions to predict difficult-to-measure dimensions required for ergonomic design of school furniture. A total of 143 students aged between 16 and 18 years from eight public secondary schools in Ogbomoso, Nigeria ...

  19. FINITE ELEMENT MODEL FOR PREDICTING RESIDUAL ...

    African Journals Online (AJOL)

    direction (σx) had a maximum value of 375MPa (tensile) and minimum value of ... These results shows that the residual stresses obtained by prediction from the finite element method are in fair agreement with the experimental results.

  20. Probabilistic Modeling and Visualization for Bankruptcy Prediction

    DEFF Research Database (Denmark)

    Antunes, Francisco; Ribeiro, Bernardete; Pereira, Francisco Camara

    2017-01-01

    In accounting and finance domains, bankruptcy prediction is of great utility for all of the economic stakeholders. The challenge of accurate assessment of business failure prediction, specially under scenarios of financial crisis, is known to be complicated. Although there have been many successful......). Using real-world bankruptcy data, an in-depth analysis is conducted showing that, in addition to a probabilistic interpretation, the GP can effectively improve the bankruptcy prediction performance with high accuracy when compared to the other approaches. We additionally generate a complete graphical...... visualization to improve our understanding of the different attained performances, effectively compiling all the conducted experiments in a meaningful way. We complete our study with an entropy-based analysis that highlights the uncertainty handling properties provided by the GP, crucial for prediction tasks...

  1. Prediction for Major Adverse Outcomes in Cardiac Surgery: Comparison of Three Prediction Models

    Directory of Open Access Journals (Sweden)

    Cheng-Hung Hsieh

    2007-09-01

    Conclusion: The Parsonnet score performed as well as the logistic regression models in predicting major adverse outcomes. The Parsonnet score appears to be a very suitable model for clinicians to use in risk stratification of cardiac surgery.

  2. A hybrid model for predicting carbon monoxide from vehicular exhausts in urban environments

    Science.gov (United States)

    Gokhale, Sharad; Khare, Mukesh

    Several deterministic-based air quality models evaluate and predict the frequently occurring pollutant concentration well but, in general, are incapable of predicting the 'extreme' concentrations. In contrast, the statistical distribution models overcome the above limitation of the deterministic models and predict the 'extreme' concentrations. However, the environmental damages are caused by both extremes as well as by the sustained average concentration of pollutants. Hence, the model should predict not only 'extreme' ranges but also the 'middle' ranges of pollutant concentrations, i.e. the entire range. Hybrid modelling is one of the techniques that estimates/predicts the 'entire range' of the distribution of pollutant concentrations by combining the deterministic based models with suitable statistical distribution models ( Jakeman, et al., 1988). In the present paper, a hybrid model has been developed to predict the carbon monoxide (CO) concentration distributions at one of the traffic intersections, Income Tax Office (ITO), in the Delhi city, where the traffic is heterogeneous in nature and meteorology is 'tropical'. The model combines the general finite line source model (GFLSM) as its deterministic, and log logistic distribution (LLD) model, as its statistical components. The hybrid (GFLSM-LLD) model is then applied at the ITO intersection. The results show that the hybrid model predictions match with that of the observed CO concentration data within the 5-99 percentiles range. The model is further validated at different street location, i.e. Sirifort roadway. The validation results show that the model predicts CO concentrations fairly well ( d=0.91) in 10-95 percentiles range. The regulatory compliance is also developed to estimate the probability of exceedance of hourly CO concentration beyond the National Ambient Air Quality Standards (NAAQS) of India. It consists of light vehicles, heavy vehicles, three- wheelers (auto rickshaws) and two

  3. Predictive models of choroidal neovascularization and geographic atrophy incidence applied to clinical trial design.

    Science.gov (United States)

    McCarthy, Linda C; Newcombe, Paul J; Whittaker, John C; Wurzelmann, John I; Fries, Michael A; Burnham, Nancy R; Cai, Gengqian; Stinnett, Sandra W; Trivedi, Trupti M; Xu, Chun-Fang

    2012-09-01

    To develop comprehensive predictive models for choroidal neovascularization (CNV) and geographic atrophy (GA) incidence within 3 years that can be applied realistically to clinical practice. Retrospective evaluation of data from a longitudinal study to develop and validate predictive models of CNV and GA. The predictive performance of clinical, environmental, demographic, and genetic risk factors was explored in regression models, using data from both eyes of 2011 subjects from the Age-Related Eye Disease Study (AREDS). The performance of predictive models was compared using 10-fold cross-validated receiver operating characteristic curves in the training data, followed by comparisons in an independent validation dataset (1410 AREDS subjects). Bayesian trial simulations were used to compare the usefulness of predictive models to screen patients for inclusion in prevention clinical trials. Logistic regression models that included clinical, demographic, and environmental factors had better predictive performance for 3-year CNV and GA incidence (area under the receiver operating characteristic curve of 0.87 and 0.89, respectively), compared with simple clinical criteria (AREDS simplified severity scale). Although genetic markers were associated significantly with 3-year CNV (CFH: Y402H; ARMS2: A69S) and GA incidence (CFH: Y402H), the inclusion of genetic factors in the models provided only marginal improvements in predictive performance. The logistic regression models combine good predictive performance with greater flexibility to optimize clinical trial design compared with simple clinical models (AREDS simplified severity scale). The benefit of including genetic factors to screen patients for recruitment to CNV prevention studies is marginal and is dependent on individual clinical trial economics. Copyright © 2012 Elsevier Inc. All rights reserved.

  4. Evaluation of wavelet performance via an ANN-based electrical conductivity prediction model.

    Science.gov (United States)

    Ravansalar, Masoud; Rajaee, Taher

    2015-06-01

    The prediction of water quality parameters plays an important role in water resources and environmental systems. The use of electrical conductivity (EC) as a water quality indicator is one of the important parameters for estimating the amount of mineralization. This study describes the application of artificial neural network (ANN) and wavelet-neural network hybrid (WANN) models to predict the monthly EC of the Asi River at the Demirköprü gauging station, Turkey. In the proposed hybrid WANN model, the discrete wavelet transform (DWT) was linked to the ANN model for EC prediction using a feed-forward back propagation (FFBP) training algorithm. For this purpose, the original time series of monthly EC and discharge (Q) values were decomposed to several sub-time series by DWT, and these sub-time series were then presented to the ANN model as an input dataset to predict the monthly EC. Comparing the values predicted by the models indicated that the performance of the proposed WANN model was better than the conventional ANN model. The correlation of determination (R (2)) were 0.949 and 0.381 for the WANN and ANN models, respectively. The results indicate that the peak EC values predicted by the WANN model are closer to the observed values, and this model simulates the hysteresis phenomena at an acceptable level as well.

  5. Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods.

    Science.gov (United States)

    Girela, Jose L; Gil, David; Johnsson, Magnus; Gomez-Torres, María José; De Juan, Joaquín

    2013-04-01

    Fertility rates have dramatically decreased in the last two decades, especially in men. It has been described that environmental factors as well as life habits may affect semen quality. In this paper we use artificial intelligence techniques in order to predict semen characteristics resulting from environmental factors, life habits, and health status, with these techniques constituting a possible decision support system that can help in the study of male fertility potential. A total of 123 young, healthy volunteers provided a semen sample that was analyzed according to the World Health Organization 2010 criteria. They also were asked to complete a validated questionnaire about life habits and health status. Sperm concentration and percentage of motile sperm were related to sociodemographic data, environmental factors, health status, and life habits in order to determine the predictive accuracy of a multilayer perceptron network, a type of artificial neural network. In conclusion, we have developed an artificial neural network that can predict the results of the semen analysis based on the data collected by the questionnaire. The semen parameter that is best predicted using this methodology is the sperm concentration. Although the accuracy for motility is slightly lower than that for concentration, it is possible to predict it with a significant degree of accuracy. This methodology can be a useful tool in early diagnosis of patients with seminal disorders or in the selection of candidates to become semen donors.

  6. From Predictive Models to Instructional Policies

    Science.gov (United States)

    Rollinson, Joseph; Brunskill, Emma

    2015-01-01

    At their core, Intelligent Tutoring Systems consist of a student model and a policy. The student model captures the state of the student and the policy uses the student model to individualize instruction. Policies require different properties from the student model. For example, a mastery threshold policy requires the student model to have a way…

  7. Validation of models using Chernobyl fallout data from the Central Bohemia region of the Czech Republic. Scenario CB. First report of the VAMP Multiple Pathways Assessment Working Group. Part of the IAEA/CEC Co-ordinated Research Programme on the Validation of Environmental Model Predictions (VAMP)

    International Nuclear Information System (INIS)

    1995-04-01

    The VAMP Multiple Pathways Assessment Working Group is an international forum for the testing and comparison of model predictions. The emphasis is on evaluating transfer from the environment to human via all pathways which are relevant in the environment being considered. This document is the first report of the Group and contains the results of the first test exercise on the validation of multiple pathways assessment models using Chernobyl fallout data obtained from the Central Bohemia (CB) region of the Czech Republic (Scenario CB). The report includes the following three appendixes: Documentation and evaluation of model validation data used in scenario CB (3 papers), Description of models used in scenario CB (1 paper), Individual evaluations of model predictions for scenario CB (13 papers). A separate abstract was prepared for each paper. Refs, figs and tabs

  8. The "polyenviromic risk score": Aggregating environmental risk factors predicts conversion to psychosis in familial high-risk subjects.

    Science.gov (United States)

    Padmanabhan, Jaya L; Shah, Jai L; Tandon, Neeraj; Keshavan, Matcheri S

    2017-03-01

    Young relatives of individuals with schizophrenia (i.e. youth at familial high-risk, FHR) are at increased risk of developing psychotic disorders, and show higher rates of psychiatric symptoms, cognitive and neurobiological abnormalities than non-relatives. It is not known whether overall exposure to environmental risk factors increases risk of conversion to psychosis in FHR subjects. Subjects consisted of a pilot longitudinal sample of 83 young FHR subjects. As a proof of principle, we examined whether an aggregate score of exposure to environmental risk factors, which we term a 'polyenviromic risk score' (PERS), could predict conversion to psychosis. The PERS combines known environmental risk factors including cannabis use, urbanicity, season of birth, paternal age, obstetric and perinatal complications, and various types of childhood adversity, each weighted by its odds ratio for association with psychosis in the literature. A higher PERS was significantly associated with conversion to psychosis in young, familial high-risk subjects (OR=1.97, p=0.009). A model combining the PERS and clinical predictors had a sensitivity of 27% and specificity of 96%. An aggregate index of environmental risk may help predict conversion to psychosis in FHR subjects. Copyright © 2016 Elsevier B.V. All rights reserved.

  9. Predicted distribution of major malaria vectors belonging to the Anopheles dirus complex in Asia: ecological niche and environmental influences.

    Science.gov (United States)

    Obsomer, Valerie; Defourny, Pierre; Coosemans, Marc

    2012-01-01

    Methods derived from ecological niche modeling allow to define species distribution based on presence-only data. This is particularly useful to develop models from literature records such as available for the Anopheles dirus complex, a major group of malaria mosquito vectors in Asia. This research defines an innovative modeling design based on presence-only model and hierarchical framework to define the distribution of the complex and attempt to delineate sibling species distribution and environmental preferences. At coarse resolution, the potential distribution was defined using slow changing abiotic factors such as topography and climate representative for the timescale covered by literature records of the species. The distribution area was then refined in a second step using a mask of current suitable land cover. Distribution area and ecological niche were compared between species and environmental factors tested for relevance. Alternatively, extreme values at occurrence points were used to delimit environmental envelopes. The spatial distribution for the complex was broadly consistent with its known distribution and influencing factors included temperature and rainfall. If maps developed from environmental envelopes gave similar results to modeling when the number of sites was high, the results were less similar for species with low number of recorded presences. Using presence-only models and hierarchical framework this study not only predicts the distribution of a major malaria vector, but also improved ecological modeling analysis design and proposed final products better adapted to malaria control decision makers. The resulting maps can help prioritizing areas which need further investigation and help simulate distribution under changing conditions such as climate change or reforestation. The hierarchical framework results in two products one abiotic based model describes the potential maximal distribution and remains valid for decades and the other

  10. Predicted distribution of major malaria vectors belonging to the Anopheles dirus complex in Asia: ecological niche and environmental influences.

    Directory of Open Access Journals (Sweden)

    Valerie Obsomer

    Full Text Available Methods derived from ecological niche modeling allow to define species distribution based on presence-only data. This is particularly useful to develop models from literature records such as available for the Anopheles dirus complex, a major group of malaria mosquito vectors in Asia. This research defines an innovative modeling design based on presence-only model and hierarchical framework to define the distribution of the complex and attempt to delineate sibling species distribution and environmental preferences. At coarse resolution, the potential distribution was defined using slow changing abiotic factors such as topography and climate representative for the timescale covered by literature records of the species. The distribution area was then refined in a second step using a mask of current suitable land cover. Distribution area and ecological niche were compared between species and environmental factors tested for relevance. Alternatively, extreme values at occurrence points were used to delimit environmental envelopes. The spatial distribution for the complex was broadly consistent with its known distribution and influencing factors included temperature and rainfall. If maps developed from environmental envelopes gave similar results to modeling when the number of sites was high, the results were less similar for species with low number of recorded presences. Using presence-only models and hierarchical framework this study not only predicts the distribution of a major malaria vector, but also improved ecological modeling analysis design and proposed final products better adapted to malaria control decision makers. The resulting maps can help prioritizing areas which need further investigation and help simulate distribution under changing conditions such as climate change or reforestation. The hierarchical framework results in two products one abiotic based model describes the potential maximal distribution and remains valid for decades

  11. The application of DEA model in enterprise environmental performance auditing

    Science.gov (United States)

    Li, F.; Zhu, L. Y.; Zhang, J. D.; Liu, C. Y.; Qu, Z. G.; Xiao, M. S.

    2017-01-01

    As a part of society, enterprises have an inescapable responsibility for environmental protection and governance. This article discusses the feasibility and necessity of enterprises environmental performance auditing and uses DEA model calculate the environmental performance of Haier for example. The most of reference data are selected and sorted from Haier’s environmental reportspublished in 2008, 2009, 2011 and 2015, and some of the data from some published articles and fieldwork. All the calculation results are calculated by DEAP software andhave a high credibility. The analysis results of this article can give corporate managements an idea about using environmental performance auditing to adjust their corporate environmental investments capital quota and change their company’s environmental strategies.

  12. Comparisons of Faulting-Based Pavement Performance Prediction Models

    Directory of Open Access Journals (Sweden)

    Weina Wang

    2017-01-01

    Full Text Available Faulting prediction is the core of concrete pavement maintenance and design. Highway agencies are always faced with the problem of lower accuracy for the prediction which causes costly maintenance. Although many researchers have developed some performance prediction models, the accuracy of prediction has remained a challenge. This paper reviews performance prediction models and JPCP faulting models that have been used in past research. Then three models including multivariate nonlinear regression (MNLR model, artificial neural network (ANN model, and Markov Chain (MC model are tested and compared using a set of actual pavement survey data taken on interstate highway with varying design features, traffic, and climate data. It is found that MNLR model needs further recalibration, while the ANN model needs more data for training the network. MC model seems a good tool for pavement performance prediction when the data is limited, but it is based on visual inspections and not explicitly related to quantitative physical parameters. This paper then suggests that the further direction for developing the performance prediction model is incorporating the advantages and disadvantages of different models to obtain better accuracy.

  13. Uncertainty in the response of terrestrial carbon sink to environmental drivers undermines carbon-climate feedback predictions.

    Science.gov (United States)

    Huntzinger, D N; Michalak, A M; Schwalm, C; Ciais, P; King, A W; Fang, Y; Schaefer, K; Wei, Y; Cook, R B; Fisher, J B; Hayes, D; Huang, M; Ito, A; Jain, A K; Lei, H; Lu, C; Maignan, F; Mao, J; Parazoo, N; Peng, S; Poulter, B; Ricciuto, D; Shi, X; Tian, H; Wang, W; Zeng, N; Zhao, F

    2017-07-06

    Terrestrial ecosystems play a vital role in regulating the accumulation of carbon (C) in the atmosphere. Understanding the factors controlling land C uptake is critical for reducing uncertainties in projections of future climate. The relative importance of changing climate, rising atmospheric CO 2 , and other factors, however, remains unclear despite decades of research. Here, we use an ensemble of land models to show that models disagree on the primary driver of cumulative C uptake for 85% of vegetated land area. Disagreement is largest in model sensitivity to rising atmospheric CO 2 which shows almost twice the variability in cumulative land uptake since 1901 (1 s.d. of 212.8 PgC vs. 138.5 PgC, respectively). We find that variability in CO 2 and temperature sensitivity is attributable, in part, to their compensatory effects on C uptake, whereby comparable estimates of C uptake can arise by invoking different sensitivities to key environmental conditions. Conversely, divergent estimates of C uptake can occur despite being based on the same environmental sensitivities. Together, these findings imply an important limitation to the predictability of C cycling and climate under unprecedented environmental conditions. We suggest that the carbon modeling community prioritize a probabilistic multi-model approach to generate more robust C cycle projections.

  14. Uncertainty in the response of terrestrial carbon sink to environmental drivers undermines carbon-climate feedback predictions

    Energy Technology Data Exchange (ETDEWEB)

    Huntzinger, D. N.; Michalak, A. M.; Schwalm, C.; Ciais, P.; King, A. W.; Fang, Y.; Schaefer, K.; Wei, Y.; Cook, R. B.; Fisher, J. B.; Hayes, D.; Huang, M.; Ito, A.; Jain, A. K.; Lei, H.; Lu, C.; Maignan, F.; Mao, J.; Parazoo, N.; Peng, S.; Poulter, B.; Ricciuto, D.; Shi, X.; Tian, H.; Wang, W.; Zeng, N.; Zhao, F.

    2017-07-06

    Terrestrial ecosystems play a vital role in regulating the accumulation of carbon (C) in the atmosphere. Understanding the factors controlling land C uptake is critical for reducing uncertainties in projections of future climate. The relative importance of changing climate, rising atmospheric CO2, and other factors, however, remains unclear despite decades of research. Here, we use an ensemble of land models to show that models disagree on the primary driver of cumulative C uptake for 85% of vegetated land area. Disagreement is largest in model sensitivity to rising atmospheric CO2 which shows almost twice the variability in cumulative land uptake since 1901 (1 s.d. of 212.8 PgC vs. 138.5 PgC, respectively). We find that variability in CO2 and temperature sensitivity is attributable, in part, to their compensatory effects on C uptake, whereby comparable estimates of C uptake can arise by invoking different sensitivities to key environmental conditions. Conversely, divergent estimates of C uptake can occur despite being based on the same environmental sensitivities. Together, these findings imply an important limitation to the predictability of C cycling and climate under unprecedented environmental conditions. We suggest that the carbon modeling community prioritize a probabilistic multi-model approach to generate more robust C cycle projections.

  15. Modeling of environmental adaptation versus pollution mitigation

    OpenAIRE

    YATSENKO, Yuri; HRITONENKO, Natali; BRECHET, Thierry

    2014-01-01

    The paper combines analytic and numeric tools to investigate a nonlinear optimal control problem relevant to the economics of climate change. The problem describes optimal investments into pollution mitigation and environmental adaptation at a macroeconomic level. The steady-state analysis of this problem focuses on the optimal ratio between adaptation and mitigation. In particular, we analytically prove that the long- term investments into adaptation are profitable only for economies above c...

  16. Environmental fate model for ultra-low-volume insecticide applications used for adult mosquito management

    Science.gov (United States)

    Schleier, Jerome J.; Peterson, Robert K.D.; Irvine, Kathryn M.; Marshall, Lucy M.; Weaver, David K.; Preftakes, Collin J.

    2012-01-01

    One of the more effective ways of managing high densities of adult mosquitoes that vector human and animal pathogens is ultra-low-volume (ULV) aerosol applications of insecticides. The U.S. Environmental Protection Agency uses models that are not validated for ULV insecticide applications and exposure assumptions to perform their human and ecological risk assessments. Currently, there is no validated model that can accurately predict deposition of insecticides applied using ULV technology for adult mosquito management. In addition, little is known about the deposition and drift of small droplets like those used under conditions encountered during ULV applications. The objective of this study was to perform field studies to measure environmental concentrations of insecticides and to develop a validated model to predict the deposition of ULV insecticides. The final regression model was selected by minimizing the Bayesian Information Criterion and its prediction performance was evaluated using k-fold cross validation. Density of the formulation and the density and CMD interaction coefficients were the largest in the model. The results showed that as density of the formulation decreases, deposition increases. The interaction of density and CMD showed that higher density formulations and larger droplets resulted in greater deposition. These results are supported by the aerosol physics literature. A k-fold cross validation demonstrated that the mean square error of the selected regression model is not biased, and the mean square error and mean square prediction error indicated good predictive ability.

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

    Science.gov (United States)

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

    2017-04-01

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

  18. Evaluation of the US Army fallout prediction model

    International Nuclear Information System (INIS)

    Pernick, A.; Levanon, I.

    1987-01-01

    The US Army fallout prediction method was evaluated against an advanced fallout prediction model--SIMFIC (Simplified Fallout Interpretive Code). The danger zone areas of the US Army method were found to be significantly greater (up to a factor of 8) than the areas of corresponding radiation hazard as predicted by SIMFIC. Nonetheless, because the US Army's method predicts danger zone lengths that are commonly shorter than the corresponding hot line distances of SIMFIC, the US Army's method is not reliably conservative

  19. Comparative Evaluation of Some Crop Yield Prediction Models ...

    African Journals Online (AJOL)

    A computer program was adopted from the work of Hill et al. (1982) to calibrate and test three of the existing yield prediction models using tropical cowpea yieldÐweather data. The models tested were Hanks Model (first and second versions). Stewart Model (first and second versions) and HallÐButcher Model. Three sets of ...

  20. Comparative Evaluation of Some Crop Yield Prediction Models ...

    African Journals Online (AJOL)

    (1982) to calibrate and test three of the existing yield prediction models using tropical cowpea yieldÐweather data. The models tested were Hanks Model (first and second versions). Stewart Model (first and second versions) and HallÐButcher Model. Three sets of cowpea yield-water use and weather data were collected.

  1. Environmental Management Model for Road Maintenance Operation Involving Community Participation

    Science.gov (United States)

    Triyono, A. R. H.; Setyawan, A.; Sobriyah; Setiono, P.

    2017-07-01

    Public expectations of Central Java, which is very high on demand fulfillment, especially road infrastructure as outlined in the number of complaints and community expectations tweeter, Short Mail Massage (SMS), e-mail and public reports from various media, Highways Department of Central Java province requires development model of environmental management in the implementation of a routine way by involving the community in order to fulfill the conditions of a representative, may serve road users safely and comfortably. This study used survey method with SEM analysis and SWOT with Latent Independent Variable (X), namely; Public Participation in the regulation, development, construction and supervision of road (PSM); Public behavior in the utilization of the road (PMJ) Provincial Road Service (PJP); Safety in the Provincial Road (KJP); Integrated Management System (SMT) and latent dependent variable (Y) routine maintenance of the provincial road that is integrated with the environmental management system and involve the participation of the community (MML). The result showed the implementation of routine maintenance of road conditions in Central Java province has yet to implement an environmental management by involving the community; Therefore developed environmental management model with the results of H1: Community Participation (PSM) has positive influence on the Model of Environmental Management (MML); H2: Behavior Society in Jalan Utilization (PMJ) positive effect on Model Environmental Management (MML); H3: Provincial Road Service (PJP) positive effect on Model Environmental Management (MML); H4: Safety in the Provincial Road (KJP) positive effect on Model Environmental Management (MML); H5: Integrated Management System (SMT) has positive influence on the Model of Environmental Management (MML). From the analysis obtained formulation model describing the relationship / influence of the independent variables PSM, PMJ, PJP, KJP, and SMT on the dependent variable

  2. Prediction of speech intelligibility based on an auditory preprocessing model

    DEFF Research Database (Denmark)

    Christiansen, Claus Forup Corlin; Pedersen, Michael Syskind; Dau, Torsten

    2010-01-01

    Classical speech intelligibility models, such as the speech transmission index (STI) and the speech intelligibility index (SII) are based on calculations on the physical acoustic signals. The present study predicts speech intelligibility by combining a psychoacoustically validated model of auditory...

  3. Modelling microbial interactions and food structure in predictive microbiology

    NARCIS (Netherlands)

    Malakar, P.K.

    2002-01-01

    Keywords: modelling, dynamic models, microbial interactions, diffusion, microgradients, colony growth, predictive microbiology.

    Growth response of microorganisms in foods is a complex process. Innovations in food production and preservation techniques have resulted in adoption of

  4. Ocean wave prediction using numerical and neural network models

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Prabaharan, N.

    This paper presents an overview of the development of the numerical wave prediction models and recently used neural networks for ocean wave hindcasting and forecasting. The numerical wave models express the physical concepts of the phenomena...

  5. A Prediction Model of the Capillary Pressure J-Function.

    Directory of Open Access Journals (Sweden)

    W S Xu

    Full Text Available The capillary pressure J-function is a dimensionless measure of the capillary pressure of a fluid in a porous medium. The function was derived based on a capillary bundle model. However, the dependence of the J-function on the saturation Sw is not well understood. A prediction model for it is presented based on capillary pressure model, and the J-function prediction model is a power function instead of an exponential or polynomial function. Relative permeability is calculated with the J-function prediction model, resulting in an easier calculation and results that are more representative.

  6. Numerical Modeling of Hydrokinetic Turbines and their Environmental Effects

    Science.gov (United States)

    Javaherchi, T.; Seydel, J.; Aliseda, A.

    2010-12-01

    The search for predictable renewable energy has led research into marine hydrokinetic energy. Electricity can be generated from tidally-induced currents through turbines located in regions of high current speed and relatively low secondary flow intensity. Although significant technological challenges exist, the main obstacle in the development and commercial deployment of marine hydrokinetic (MHK) turbines is the uncertainty in the environmental effect of devices. The velocity deficit in the turbulent wake of the turbine might enhance the sedimentation process of suspended particles in the water column and lead to deposition into artificial patterns that alter the benthic ecosystem. Pressure fluctuations across turbine blades and in blade tip vortices can damage internal organs of marine species as they swim through the device. These are just a few examples of the important potential environmental effects of MHK turbines that need to be addressed and investigated a priori before pilot and large scale deployment. We have developed a hierarchy of numerical models to simulate the turbulent wake behind a well characterized two bladed turbine. The results from these models (Sliding Mesh, Rotating Reference Frame, Virtual Blade Model and Actuator Disk Model) have been validated and are been used to investigate the efficiency and physical changes introduced in the environment by single or multiple MHK turbines. We will present results from sedimenting particles and model juvenile fish, with relative densities of 1.2 and 0.95, respectively. The settling velocity and terminal location on the bottom of the tidal channel is computed and compared to the simulated flow in a channel without turbines. We have observed an enhanced sedimentation, and we will quantify the degree of enhancement and the parameter range within which it is significant. For the slightly buoyant particles representing fish, the pressure history is studied statistically with particular attention to the

  7. Modeling Environmental Literacy of Malaysian Pre-University Students

    Science.gov (United States)

    Shamuganathan, Sheila; Karpudewan, Mageswary

    2015-01-01

    In this study attempt was made to model the environmental literacy of Malaysian pre-university students enrolled in a matriculation college. Students enrolled in the matriculation colleges in Malaysia are the top notch students in the country. Environmental literacy of this group is perceived important because in the future these students will be…

  8. 1987 Oak Ridge model conference: Proceedings: Volume 2, Environmental protection

    International Nuclear Information System (INIS)

    1987-01-01

    See the abstract for Volume I for general information on the conference. Topics discussed in Volume II include data management techiques for environmental protection efforts, the use of models in environmental auditing, in emergency plans, chemical accident emergency response, risk assessment, monitoring of waste sites, air and water monitoring of waste sites, and in training programs

  9. Frontier models for evaluating environmental efficiency: an overview

    NARCIS (Netherlands)

    Oude Lansink, A.G.J.M.; Wall, A.

    2014-01-01

    Our aim in this paper is to provide a succinct overview of frontier-based models used to evaluate environmental efficiency, with a special emphasis on agricultural activity. We begin by providing a brief, up-to-date review of the main approaches used to measure environmental efficiency, with

  10. Gaussian copula as a likelihood function for environmental models

    Science.gov (United States)

    Wani, O.; Espadas, G.; Cecinati, F.; Rieckermann, J.

    2017-12-01

    Parameter estimation of environmental models always comes with uncertainty. To formally quantify this parametric uncertainty, a likelihood function needs to be formulated, which is defined as the probability of observations given fixed values of the parameter set. A likelihood function allows us to infer parameter values from observations using Bayes' theorem. The challenge is to formulate a likelihood function that reliably describes the error generating processes which lead to the observed monitoring data, such as rainfall and runoff. If the likelihood function is not representative of the error statistics, the parameter inference will give biased parameter values. Several uncertainty estimation methods that are currently being used employ Gaussian processes as a likelihood function, because of their favourable analytical properties. Box-Cox transformation is suggested to deal with non-symmetric and heteroscedastic errors e.g. for flow data which are typically more uncertain in high flows than in periods with low flows. Problem with transformations is that the results are conditional on hyper-parameters, for which it is difficult to formulate the analyst's belief a priori. In an attempt to address this problem, in this research work we suggest learning the nature of the error distribution from the errors made by the model in the "past" forecasts. We use a Gaussian copula to generate semiparametric error distributions . 1) We show that this copula can be then used as a likelihood function to infer parameters, breaking away from the practice of using multivariate normal distributions. Based on the results from a didactical example of predicting rainfall runoff, 2) we demonstrate that the copula captures the predictive uncertainty of the model. 3) Finally, we find that the properties of autocorrelation and heteroscedasticity of errors are captured well by the copula, eliminating the need to use transforms. In summary, our findings suggest that copulas are an

  11. A new approach to predicting environmental transfer of radionuclides to wildlife: A demonstration for freshwater fish and caesium

    International Nuclear Information System (INIS)

    Beresford, N.A.; Yankovich, T.L.; Wood, M.D.; Fesenko, S.; Andersson, P.; Muikku, M.; Willey, N.J.

    2013-01-01

    The application of the concentration ratio (CR) to predict radionuclide activity concentrations in wildlife from those in soil or water has become the widely accepted approach for environmental assessments. Recently both the ICRP and IAEA have produced compilations of CR values for application in environmental assessment. However, the CR approach has many limitations, most notably, that the transfer of most radionuclides is largely determined by site-specific factors (e.g. water or soil chemistry). Furthermore, there are few, if any, CR values for many radionuclide-organism combinations. In this paper, we propose an alternative approach and, as an example, demonstrate and test this for caesium and freshwater fish. Using a Residual Maximum Likelihood (REML) mixed-model regression we analysed a dataset comprising 597 entries for 53 freshwater fish species from 67 sites. The REML analysis generated a mean value for each species on a common scale after REML adjustment taking account of the effect of the inter-site variation. Using an independent dataset, we subsequently test the hypothesis that the REML model outputs can be used to predict radionuclide, in this case radiocaesium, activity concentrations in unknown species from the results of a species which has been sampled at a specific site. The outputs of the REML analysis accurately predicted 137 Cs activity concentrations in different species of fish from 27 Finnish lakes; these data had not been used in our initial analyses. We recommend that this alternative approach be further investigated for other radionuclides and ecosystems. - Highlights: • An alternative approach to estimating radionuclide transfer to wildlife is presented. • Analysed a dataset comprising 53 freshwater fish species collected from 67 sites. • Residual Maximum Likelihood mixed model regression is used. • Model output takes account of the effect of inter-site variation. • Successfully predicted 137 Cs concentrations in different

  12. Environmental sub models for a macroeconomic model: Agricultural contribution to climate change and acidification in Denmark

    DEFF Research Database (Denmark)

    Jensen, T.S.; Jensen, J.D.; Hasler, B.

    2007-01-01

    economic model, environmental satellite models of energy and waste related emissions contributing to climate change and acidification. The model extension allows the main Danish contribution to climate change and acidification to be modelled. The existing model system is extended by environmental satellite...... for changes in the husbandry sector within the agricultural sector....

  13. Addressing the complexity of water chemistry in environmental fate modeling for engineered nanoparticles.

    Science.gov (United States)

    Sani-Kast, Nicole; Scheringer, Martin; Slomberg, Danielle; Labille, Jérôme; Praetorius, Antonia; Ollivier, Patrick; Hungerbühler, Konrad

    2015-12-01

    Engineered nanoparticle (ENP) fate models developed to date - aimed at predicting ENP concentration in the aqueous environment - have limited applicability because they employ constant environmental conditions along the modeled system or a highly specific environmental representation; both approaches do not show the effects of spatial and/or temporal variability. To address this conceptual gap, we developed a novel modeling strategy that: 1) incorporates spatial variability in environmental conditions in an existing ENP fate model; and 2) analyzes the effect of a wide range of randomly sampled environmental conditions (representing variations in water chemistry). This approach was employed to investigate the transport of nano-TiO2 in the Lower Rhône River (France) under numerous sets of environmental conditions. The predicted spatial concentration profiles of nano-TiO2 were then grouped according to their similarity by using cluster analysis. The analysis resulted in a small number of clusters representing groups of spatial concentration profiles. All clusters show nano-TiO2 accumulation in the sediment layer, supporting results from previous studies. Analysis of the characteristic features of each cluster demonstrated a strong association between the water conditions in regions close to the ENP emission source and the cluster membership of the corresponding spatial concentration profiles. In particular, water compositions favoring heteroaggregation between the ENPs and suspended particulate matter resulted in clusters of low variability. These conditions are, therefore, reliable predictors of the eventual fate of the modeled ENPs. The conclusions from this study are also valid for ENP fate in other large river systems. Our results, therefore, shift the focus of future modeling and experimental research of ENP environmental fate to the water characteristic in regions near the expected ENP emission sources. Under conditions favoring heteroaggregation in these

  14. 'spup' - an R package for uncertainty propagation analysis in spatial environmental modelling

    Science.gov (United States)

    Sawicka, Kasia; Heuvelink, Gerard

    2017-04-01

    Computer models have become a crucial tool in engineering and environmental sciences for simulating the behaviour of complex static and dynamic systems. However, while many models are deterministic, the uncertainty in their predictions needs to be estimated before they are used for decision support. Currently, advances in uncertainty propagation and assessment have been paralleled by a growing number of software tools for uncertainty analysis, but none has gained recognition for a universal applicability and being able to deal with case studies with spatial models and spatial model inputs. Due to the growing popularity and applicability of the open source R programming language we undertook a project to develop an R package that facilitates uncertainty propagation analysis in spatial environmental modelling. In particular, the 'spup' package provides functions for examining the uncertainty propagation starting from input data and model parameters, via the environmental model onto model predictions. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques, as well as several uncertainty visualization functions. Uncertain environmental variables are represented in the package as objects whose attribute values may be uncertain and described by probability distributions. Both numerical and categorical data types are handled. Spatial auto-correlation within an attribute and cross-correlation between attributes is also accommodated for. For uncertainty propagation the package has implemented the MC approach with efficient sampling algorithms, i.e. stratified random sampling and Latin hypercube sampling. The design includes facilitation of parallel computing to speed up MC computation. The MC realizations may be used as an input to the environmental models called from R, or externally. Selected visualization methods that are understandable by non-experts with limited background in

  15. Statistical model based gender prediction for targeted NGS clinical panels

    Directory of Open Access Journals (Sweden)

    Palani Kannan Kandavel

    2017-12-01

    The reference test dataset are being used to test the model. The sensitivity on predicting the gender has been increased from the current “genotype composition in ChrX” based approach. In addition, the prediction score given by the model can be used to evaluate the quality of clinical dataset. The higher prediction score towards its respective gender indicates the higher quality of sequenced data.

  16. comparative analysis of two mathematical models for prediction

    African Journals Online (AJOL)

    Abstract. A mathematical modeling for prediction of compressive strength of sandcrete blocks was performed using statistical analysis for the sandcrete block data ob- tained from experimental work done in this study. The models used are Scheffes and Osadebes optimization theories to predict the compressive strength of ...

  17. Comparison of predictive models for the early diagnosis of diabetes

    NARCIS (Netherlands)

    M. Jahani (Meysam); M. Mahdavi (Mahdi)

    2016-01-01

    textabstractObjectives: This study develops neural network models to improve the prediction of diabetes using clinical and lifestyle characteristics. Prediction models were developed using a combination of approaches and concepts. Methods: We used memetic algorithms to update weights and to improve

  18. Testing and analysis of internal hardwood log defect prediction models

    Science.gov (United States)

    R. Edward. Thomas

    2011-01-01

    The severity and location of internal defects determine the quality and value of lumber sawn from hardwood logs. Models have been developed to predict the size and position of internal defects based on external defect indicator measurements. These models were shown to predict approximately 80% of all internal knots based on external knot indicators. However, the size...

  19. Hidden Markov Model for quantitative prediction of snowfall

    Indian Academy of Sciences (India)

    A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There are six ...

  20. Bayesian variable order Markov models: Towards Bayesian predictive state representations

    NARCIS (Netherlands)

    Dimitrakakis, C.

    2009-01-01

    We present a Bayesian variable order Markov model that shares many similarities with predictive state representations. The resulting models are compact and much easier to specify and learn than classical predictive state representations. Moreover, we show that they significantly outperform a more

  1. Demonstrating the improvement of predictive maturity of a computational model

    Energy Technology Data Exchange (ETDEWEB)

    Hemez, Francois M [Los Alamos National Laboratory; Unal, Cetin [Los Alamos National Laboratory; Atamturktur, Huriye S [CLEMSON UNIV.

    2010-01-01

    We demonstrate an improvement of predictive capability brought to a non-linear material model using a combination of test data, sensitivity analysis, uncertainty quantification, and calibration. A model that captures increasingly complicated phenomena, such as plasticity, temperature and strain rate effects, is analyzed. Predictive maturity is defined, here, as the accuracy of the model to predict multiple Hopkinson bar experiments. A statistical discrepancy quantifies the systematic disagreement (bias) between measurements and predictions. Our hypothesis is that improving the predictive capability of a model should translate into better agreement between measurements and predictions. This agreement, in turn, should lead to a smaller discrepancy. We have recently proposed to use discrepancy and coverage, that is, the extent to which the physical experiments used for calibration populate the regime of applicability of the model, as basis to define a Predictive Maturity Index (PMI). It was shown that predictive maturity could be improved when additional physical tests are made available to increase coverage of the regime of applicability. This contribution illustrates how the PMI changes as 'better' physics are implemented in the model. The application is the non-linear Preston-Tonks-Wallace (PTW) strength model applied to Beryllium metal. We demonstrate that our framework tracks the evolution of maturity of the PTW model. Robustness of the PMI with respect to the selection of coefficients needed in its definition is also studied.

  2. Refining the Committee Approach and Uncertainty Prediction in Hydrological Modelling

    NARCIS (Netherlands)

    Kayastha, N.

    2014-01-01

    Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of

  3. Refining the committee approach and uncertainty prediction in hydrological modelling

    NARCIS (Netherlands)

    Kayastha, N.

    2014-01-01

    Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of

  4. Wind turbine control and model predictive control for uncertain systems

    DEFF Research Database (Denmark)

    Thomsen, Sven Creutz

    as disturbance models for controller design. The theoretical study deals with Model Predictive Control (MPC). MPC is an optimal control method which is characterized by the use of a receding prediction horizon. MPC has risen in popularity due to its inherent ability to systematically account for time...

  5. Hidden Markov Model for quantitative prediction of snowfall and ...

    Indian Academy of Sciences (India)

    A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There are six ...

  6. Model predictive control of a 3-DOF helicopter system using ...

    African Journals Online (AJOL)

    ... by simulation, and its performance is compared with that achieved by linear model predictive control (LMPC). Keywords: nonlinear systems, helicopter dynamics, MIMO systems, model predictive control, successive linearization. International Journal of Engineering, Science and Technology, Vol. 2, No. 10, 2010, pp. 9-19 ...

  7. Comparative Analysis of Two Mathematical Models for Prediction of ...

    African Journals Online (AJOL)

    A mathematical modeling for prediction of compressive strength of sandcrete blocks was performed using statistical analysis for the sandcrete block data obtained from experimental work done in this study. The models used are Scheffe's and Osadebe's optimization theories to predict the compressive strength of sandcrete ...

  8. A mathematical model for predicting earthquake occurrence ...

    African Journals Online (AJOL)

    We consider the continental crust under damage. We use the observed results of microseism in many seismic stations of the world which was established to study the time series of the activities of the continental crust with a view to predicting possible time of occurrence of earthquake. We consider microseism time series ...

  9. Model for predicting the injury severity score.

    Science.gov (United States)

    Hagiwara, Shuichi; Oshima, Kiyohiro; Murata, Masato; Kaneko, Minoru; Aoki, Makoto; Kanbe, Masahiko; Nakamura, Takuro; Ohyama, Yoshio; Tamura, Jun'ichi

    2015-07-01

    To determine the formula that predicts the injury severity score from parameters that are obtained in the emergency department at arrival. We reviewed the medical records of trauma patients who were transferred to the emergency department of Gunma University Hospital between January 2010 and December 2010. The injury severity score, age, mean blood pressure, heart rate, Glasgow coma scale, hemoglobin, hematocrit, red blood cell count, platelet count, fibrinogen, international normalized ratio of prothrombin time, activated partial thromboplastin time, and fibrin degradation products, were examined in those patients on arrival. To determine the formula that predicts the injury severity score, multiple linear regression analysis was carried out. The injury severity score was set as the dependent variable, and the other parameters were set as candidate objective variables. IBM spss Statistics 20 was used for the statistical analysis. Statistical significance was set at P  Watson ratio was 2.200. A formula for predicting the injury severity score in trauma patients was developed with ordinary parameters such as fibrin degradation products and mean blood pressure. This formula is useful because we can predict the injury severity score easily in the emergency department.

  10. Study on noise prediction model and control schemes for substation.

    Science.gov (United States)

    Chen, Chuanmin; Gao, Yang; Liu, Songtao

    2014-01-01

    With the government's emphasis on environmental issues of power transmission and transformation project, noise pollution has become a prominent problem now. The noise from the working transformer, reactor, and other electrical equipment in the substation will bring negative effect to the ambient environment. This paper focuses on using acoustic software for the simulation and calculation method to control substation noise. According to the characteristics of the substation noise and the techniques of noise reduction, a substation's acoustic field model was established with the SoundPLAN software to predict the scope of substation noise. On this basis, 4 reasonable noise control schemes were advanced to provide some helpful references for noise control during the new substation's design and construction process. And the feasibility and application effect of these control schemes can be verified by using the method of simulation modeling. The simulation results show that the substation always has the problem of excessive noise at boundary under the conventional measures. The excess noise can be efficiently reduced by taking the corresponding noise reduction methods.

  11. Study on Noise Prediction Model and Control Schemes for Substation

    Science.gov (United States)

    Gao, Yang; Liu, Songtao

    2014-01-01

    With the government's emphasis on environmental issues of power transmission and transformation project, noise pollution has become a prominent problem now. The noise from the working transformer, reactor, and other electrical equipment in the substation will bring negative effect to the ambient environment. This paper focuses on using acoustic software for the simulation and calculation method to control substation noise. According to the characteristics of the substation noise and the techniques of noise reduction, a substation's acoustic field model was established with the SoundPLAN software to predict the scope of substation noise. On this basis, 4 reasonable noise control schemes were advanced to provide some helpful references for noise control during the new substation's design and construction process. And the feasibility and application effect of these control schemes can be verified by using the method of simulation modeling. The simulation results show that the substation always has the problem of excessive noise at boundary under the conventional measures. The excess noise can be efficiently reduced by taking the corresponding noise reduction methods. PMID:24672356

  12. Modelling of an industrial NGL-Recovery unit considering environmental and economic impacts

    International Nuclear Information System (INIS)

    Sharratt, P. N.; Hernandez-Enriquez, A.; Flores-Tlacuahuac, A.

    2009-01-01

    In this work, an integrated model is presented that identifies key areas in the operation of a cryogenic NGL-recovery unit. This methodology sets out to provide deep understanding of various interrelationship across multiple plant operating factors including reliability, which could be essential for substantial improvement of process performance. The integrated model has been developed to predict the economic and environmental impacts of a real cryogenic unit (600 MMCUF/D) during normal operation, and has been built in Aspen TM. (Author)

  13. Econometric models for predicting confusion crop ratios

    Science.gov (United States)

    Umberger, D. E.; Proctor, M. H.; Clark, J. E.; Eisgruber, L. M.; Braschler, C. B. (Principal Investigator)

    1979-01-01

    Results for both the United States and Canada show that econometric models can provide estimates of confusion crop ratios that are more accurate than historical ratios. Whether these models can support the LACIE 90/90 accuracy criterion is uncertain. In the United States, experimenting with additional model formulations could provide improved methods models in some CRD's, particularly in winter wheat. Improved models may also be possible for the Canadian CD's. The more aggressive province/state models outperformed individual CD/CRD models. This result was expected partly because acreage statistics are based on sampling procedures, and the sampling precision declines from the province/state to the CD/CRD level. Declining sampling precision and the need to substitute province/state data for the CD/CRD data introduced measurement error into the CD/CRD models.

  14. Erratum: Probabilistic application of a fugacity model to predict triclosan fate during wastewater treatment.

    Science.gov (United States)

    Bock, Michael; Lyndall, Jennifer; Barber, Timothy; Fuchsman, Phyllis; Perruchon, Elyse; Capdevielle, Marie

    2010-10-01

    The fate and partitioning of the antimicrobial compound, triclosan, in wastewater treatment plants (WWTPs) is evaluated using a probabilistic fugacity model to predict the range of triclosan concentrations in effluent and secondary biosolids. The WWTP model predicts 84% to 92% triclosan removal, which is within the range of measured removal efficiencies (typically 70% to 98%). Triclosan is predominantly removed by sorption and subsequent settling of organic particulates during primary treatment and by aerobic biodegradation during secondary treatment. Median modeled removal efficiency due to sorption is 40% for all treatment phases and 31% in the primary treatment phase. Median modeled removal efficiency due to biodegradation is 48% for all treatment phases and 44% in the secondary treatment phase. Important factors contributing to variation in predicted triclosan concentrations in effluent and biosolids include influent concentrations, solids concentrations in settling tanks, and factors related to solids retention time. Measured triclosan concentrations in biosolids and non-United States (US) effluent are consistent with model predictions. However, median concentrations in US effluent are over-predicted with this model, suggesting that differences in some aspect of treatment practices not incorporated in the model (e.g., disinfection methods) may affect triclosan removal from effluent. Model applications include predicting changes in environmental loadings associated with new triclosan applications and supporting risk analyses for biosolids-amended land and effluent receiving waters. © 2010 SETAC.

  15. Probabilistic application of a fugacity model to predict triclosan fate during wastewater treatment.

    Science.gov (United States)

    Bock, Michael; Lyndall, Jennifer; Barber, Timothy; Fuchsman, Phyllis; Perruchon, Elyse; Capdevielle, Marie

    2010-07-01

    The fate and partitioning of the antimicrobial compound, triclosan, in wastewater treatment plants (WWTPs) is evaluated using a probabilistic fugacity model to predict the range of triclosan concentrations in effluent and secondary biosolids. The WWTP model predicts 84% to 92% triclosan removal, which is within the range of measured removal efficiencies (typically 70% to 98%). Triclosan is predominantly removed by sorption and subsequent settling of organic particulates during primary treatment and by aerobic biodegradation during secondary treatment. Median modeled removal efficiency due to sorption is 40% for all treatment phases and 31% in the primary treatment phase. Median modeled removal efficiency due to biodegradation is 48% for all treatment phases and 44% in the secondary treatment phase. Important factors contributing to variation in predicted triclosan concentrations in effluent and biosolids include influent concentrations, solids concentrations in settling tanks, and factors related to solids retention time. Measured triclosan concentrations in biosolids and non-United States (US) effluent are consistent with model predictions. However, median concentrations in US effluent are over-predicted with this model, suggesting that differences in some aspect of treatment practices not incorporated in the model (e.g., disinfection methods) may affect triclosan removal from effluent. Model applications include predicting changes in environmental loadings associated with new triclosan applications and supporting risk analyses for biosolids-amended land and effluent receiving waters. (c) 2010 SETAC.

  16. Fixed recurrence and slip models better predict earthquake behavior than the time- and slip-predictable models 1: repeating earthquakes

    Science.gov (United States)

    Rubinstein, Justin L.; Ellsworth, William L.; Chen, Kate Huihsuan; Uchida, Naoki

    2012-01-01

    The behavior of individual events in repeating earthquake sequences in California, Taiwan and Japan is better predicted by a model with fixed inter-event time or fixed slip than it is by the time- and slip-predictable models for earthquake occurrence. Given that repeating earthquakes are highly regular in both inter-event time and seismic moment, the time- and slip-predictable models seem ideally suited to explain their behavior. Taken together with evidence from the companion manuscript that shows similar results for laboratory experiments we conclude that the short-term predictions of the time- and slip-predictable models should be rejected in favor of earthquake models that assume either fixed slip or fixed recurrence interval. This implies that the elastic rebound model underlying the time- and slip-predictable models offers no additional value in describing earthquake behavior in an event-to-event sense, but its value in a long-term sense cannot be determined. These models likely fail because they rely on assumptions that oversimplify the earthquake cycle. We note that the time and slip of these events is predicted quite well by fixed slip and fixed recurrence models, so in some sense they are time- and slip-predictable. While fixed recurrence and slip models better predict repeating earthquake behavior than the time- and slip-predictable models, we observe a correlation between slip and the preceding recurrence time for many repeating earthquake sequences in Parkfield, California. This correlation is not found in other regions, and the sequences with the correlative slip-predictable behavior are not distinguishable from nearby earthquake sequences that do not exhibit this behavior.

  17. Ensemble modeling to predict habitat suitability for a large-scale disturbance specialist.

    Science.gov (United States)

    Latif, Quresh S; Saab, Victoria A; Dudley, Jonathan G; Hollenbeck, Jeff P

    2013-11-01

    To conserve habitat for disturbance specialist species, ecologists must identify where individuals will likely settle in newly disturbed areas. Habitat suitability models can predict which sites at new disturbances will most likely attract specialists. Without validation data from newly disturbed areas, however, the best approach for maximizing predictive accuracy can be unclear (Northwestern U.S.A.). We predicted habitat suitability for nesting Black-backed Woodpeckers (Picoides arcticus; a burned-forest specialist) at 20 recently (≤6 years postwildfire) burned locations in Montana using models calibrated with data from three locations in Washington, Oregon, and Idaho. We developed 8 models using three techniques (weighted logistic regression, Maxent, and Mahalanobis D (2) models) and various combinations of four environmental variables describing burn severity, the north-south orientation of topographic slope, and prefire canopy cover. After translating model predictions into binary classifications (0 = low suitability to unsuitable, 1 = high to moderate suitability), we compiled "ensemble predictions," consisting of the number of models (0-8) predicting any given site as highly suitable. The suitability status for 40% of the area burned by eastside Montana wildfires was consistent across models and therefore robust to uncertainty in the relative accuracy of particular models and in alternative ecological hypotheses they described. Ensemble predictions exhibited two desirable properties: (1) a positive relationship with apparent rates of nest occurrence at calibration locations and (2) declining model agreement outside surveyed environments consistent with our reduced confidence in novel (i.e., "no-analogue") environments. Areas of disagreement among models suggested where future surveys could help validate and refine models for an improved understanding of Black-backed Woodpecker nesting habitat relationships. Ensemble predictions presented here can help guide

  18. Adding propensity scores to pure prediction models fails to improve predictive performance

    Directory of Open Access Journals (Sweden)

    Amy S. Nowacki

    2013-08-01

    Full Text Available Background. Propensity score usage seems to be growing in popularity leading researchers to question the possible role of propensity scores in prediction modeling, despite the lack of a theoretical rationale. It is suspected that such requests are due to the lack of differentiation regarding the goals of predictive modeling versus causal inference modeling. Therefore, the purpose of this study is to formally examine the effect of propensity scores on predictive performance. Our hypothesis is that a multivariable regression model that adjusts for all covariates will perform as well as or better than those models utilizing propensity scores with respect to model discrimination and calibration.Methods. The most commonly encountered statistical scenarios for medical prediction (logistic and proportional hazards regression were used to investigate this research question. Random cross-validation was performed 500 times to correct for optimism. The multivariable regression models adjusting for all covariates were compared with models that included adjustment for or weighting with the propensity scores. The methods were compared based on three predictive performance measures: (1 concordance indices; (2 Brier scores; and (3 calibration curves.Results. Multivariable models adjusting for all covariates had the highest average concordance index, the lowest average Brier score, and the best calibration. Propensity score adjustment and inverse probability weighting models without adjustment for all covariates performed worse than full models and failed to improve predictive performance with full covariate adjustment.Conclusion. Propensity score techniques did not improve prediction performance measures beyond multivariable adjustment. Propensity scores are not recommended if the analytical goal is pure prediction modeling.

  19. Multiwalled Carbon Nanotube Deposition on Model Environmental Surfaces

    Science.gov (United States)

    Deposition of multiwalled carbon nanotubes (MWNTs) on model environmental surfaces was investigated using a quartz crystal microbalance with dissipation monitoring (QCM-D). Deposition behaviors of MWNTs on positively and negatively charged surfaces were in good agreement with Der...

  20. Assaying environmental nickel toxicity using model nematodes

    Science.gov (United States)

    Rudel, David; Douglas, Chandler; Huffnagle, Ian; Besser, John M.; Ingersoll, Christopher G.

    2013-01-01

    Although nickel exposure results in allergic reactions, respiratory conditions, and cancer in humans and rodents, the ramifications of excess nickel in the environment for animal and human health remain largely undescribed. Nickel and other cationic metals travel through waterways and bind to soils and sediments. To evaluate the potential toxic effects of nickel at environmental contaminant levels (8.9-7,600 µg Ni/g dry weight of sediment and 50-800 µg NiCl2/L of water), we conducted assays using two cosmopolitan nematodes, Caenorhabditis elegans and Pristionchus pacificus. We assayed the effects of both sediment-bound and aqueous nickel upon animal growth, developmental survival, lifespan, and fecundity. Uncontaminated sediments were collected from sites in the Midwestern United States and spiked with a range of nickel concentrations. We found that nickel-spiked sediment substantially impairs both survival from larval to adult stages and adult longevity in a concentration-dependent manner. Further, while aqueous nickel showed no adverse effects on either survivorship or longevity, we observed a significant decrease in fecundity, indicating that aqueous nickel could have a negative impact on nematode physiology. Intriguingly, C. elegansand P. pacificus exhibit similar, but not identical, responses to nickel exposure. Moreover, P. pacificus could be tested successfully in sediments inhospitable to C. elegans. Our results add to a growing body of literature documenting the impact of nickel on animal physiology, and suggest that environmental toxicological studies could gain an advantage by widening their repertoire of nematode species.

  1. Assaying environmental nickel toxicity using model nematodes.

    Directory of Open Access Journals (Sweden)

    David Rudel

    Full Text Available Although nickel exposure results in allergic reactions, respiratory conditions, and cancer in humans and rodents, the ramifications of excess nickel in the environment for animal and human health remain largely undescribed. Nickel and other cationic metals travel through waterways and bind to soils and sediments. To evaluate the potential toxic effects of nickel at environmental contaminant levels (8.9-7,600 µg Ni/g dry weight of sediment and 50-800 µg NiCl2/L of water, we conducted assays using two cosmopolitan nematodes, Caenorhabditis elegans and Pristionchus pacificus. We assayed the effects of both sediment-bound and aqueous nickel upon animal growth, developmental survival, lifespan, and fecundity. Uncontaminated sediments were collected from sites in the Midwestern United States and spiked with a range of nickel concentrations. We found that nickel-spiked sediment substantially impairs both survival from larval to adult stages and adult longevity in a concentration-dependent manner. Further, while aqueous nickel showed no adverse effects on either survivorship or longevity, we observed a significant decrease in fecundity, indicating that aqueous nickel could have a negative impact on nematode physiology. Intriguingly, C. elegans and P. pacificus exhibit similar, but not identical, responses to nickel exposure. Moreover, P. pacificus could be tested successfully in sediments inhospitable to C. elegans. Our results add to a growing body of literature documenting the impact of nickel on animal physiology, and suggest that environmental toxicological studies could gain an advantage by widening their repertoire of nematode species.

  2. Assaying environmental nickel toxicity using model nematodes.

    Science.gov (United States)

    Rudel, David; Douglas, Chandler D; Huffnagle, Ian M; Besser, John M; Ingersoll, Christopher G

    2013-01-01

    Although nickel exposure results in allergic reactions, respiratory conditions, and cancer in humans and rodents, the ramifications of excess nickel in the environment for animal and human health remain largely undescribed. Nickel and other cationic metals travel through waterways and bind to soils and sediments. To evaluate the potential toxic effects of nickel at environmental contaminant levels (8.9-7,600 µg Ni/g dry weight of sediment and 50-800 µg NiCl2/L of water), we conducted assays using two cosmopolitan nematodes, Caenorhabditis elegans and Pristionchus pacificus. We assayed the effects of both sediment-bound and aqueous nickel upon animal growth, developmental survival, lifespan, and fecundity. Uncontaminated sediments were collected from sites in the Midwestern United States and spiked with a range of nickel concentrations. We found that nickel-spiked sediment substantially impairs both survival from larval to adult stages and adult longevity in a concentration-dependent manner. Further, while aqueous nickel showed no adverse effects on either survivorship or longevity, we observed a significant decrease in fecundity, indicating that aqueous nickel could have a negative impact on nematode physiology. Intriguingly, C. elegans and P. pacificus exhibit similar, but not identical, responses to nickel exposure. Moreover, P. pacificus could be tested successfully in sediments inhospitable to C. elegans. Our results add to a growing body of literature documenting the impact of nickel on animal physiology, and suggest that environmental toxicological studies could gain an advantage by widening their repertoire of nematode species.

  3. Models Predicting Success of Infertility Treatment: A Systematic Review

    Science.gov (United States)

    Zarinara, Alireza; Zeraati, Hojjat; Kamali, Koorosh; Mohammad, Kazem; Shahnazari, Parisa; Akhondi, Mohammad Mehdi

    2016-01-01

    Background: Infertile couples are faced with problems that affect their marital life. Infertility treatment is expensive and time consuming and occasionally isn’t simply possible. Prediction models for infertility treatment have been proposed and prediction of treatment success is a new field in infertility treatment. Because prediction of treatment success is a new need for infertile couples, this paper reviewed previous studies for catching a general concept in applicability of the models. Methods: This study was conducted as a systematic review at Avicenna Research Institute in 2015. Six data bases were searched based on WHO definitions and MESH key words. Papers about prediction models in infertility were evaluated. Results: Eighty one papers were eligible for the study. Papers covered years after 1986 and studies were designed retrospectively and prospectively. IVF prediction models have more shares in papers. Most common predictors were age, duration of infertility, ovarian and tubal problems. Conclusion: Prediction model can be clinically applied if the model can be statistically evaluated and has a good validation for treatment success. To achieve better results, the physician and the couples’ needs estimation for treatment success rate were based on history, the examination and clinical tests. Models must be checked for theoretical approach and appropriate validation. The privileges for applying the prediction models are the decrease in the cost and time, avoiding painful treatment of patients, assessment of treatment approach for physicians and decision making for health managers. The selection of the approach for designing and using these models is inevitable. PMID:27141461

  4. Long-term prediction of fish growth under varying ambient temperature using a multiscale dynamic model

    Directory of Open Access Journals (Sweden)

    Radde Nicole

    2009-11-01

    Full Text Available Abstract Background Feed composition has a large impact on the growth of animals, particularly marine fish. We have developed a quantitative dynamic model that can predict the growth and body composition of marine fish for a given feed composition over a timespan of several months. The model takes into consideration the effects of environmental factors, particularly temperature, on growth, and it incorporates detailed kinetics describing the main metabolic processes (protein, lipid, and central metabolism known to play major roles in growth and body composition. Results For validation, we compared our model's predictions with the results of several experimental studies. We showed that the model gives reliable predictions of growth, nutrient utilization (including amino acid retention, and body composition over a timespan of several months, longer than most of the previously developed predictive models. Conclusion We demonstrate that, despite the difficulties involved, multiscale models in biology can yield reasonable and useful results. The model predictions are reliable over several timescales and in the presence of strong temperature fluctuations, which are crucial factors for modeling marine organism growth. The model provides important improvements over existing models.

  5. Towards a generalized energy prediction model for machine tools.

    Science.gov (United States)

    Bhinge, Raunak; Park, Jinkyoo; Law, Kincho H; Dornfeld, David A; Helu, Moneer; Rachuri, Sudarsan

    2017-04-01

    Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.

  6. EcoMark: Evaluating Models of Vehicular Environmental Impact

    DEFF Research Database (Denmark)

    Guo, Chenjuan; Ma, Mike; Yang, Bin

    2012-01-01

    the vehicle travels in. We develop an evaluation framework, called EcoMark, for such environmental impact models. In addition, we survey all eleven state-of-the-art impact models known to us. To gain insight into the capabilities of the models and to understand the effectiveness of the EcoMark, we apply...

  7. Externalizing problems in childhood and adolescence predict subsequent educational achievement but for different genetic and environmental reasons

    OpenAIRE

    Lewis, Gary; Asbury, Kathryn; Plomin, Robert

    2016-01-01

    Background: Childhood behavior problems predict subsequent educational achievement; however, little research has examined the etiology of these links using a longitudinal twin design. Moreover, it is unknown whether genetic and environmental innovations provide incremental prediction for educational achievement from childhood to adolescence. Methods: We examined genetic and environmental influences on parental ratings of behavior problems across childhood (age 4) and adolescence (ages 12 and ...

  8. ENVIRONMENTAL RESPONSIBILITY MODEL BASED ON ISO 14000 MANAGEMENT SYSTEMS

    Directory of Open Access Journals (Sweden)

    Catalina SITNIKOV

    2012-01-01

    Full Text Available Worldwide corporations, as well as their stakeholders, are more conscious of the need for environmental management, SR behaviour, and sustainable growth and development. International Standards are becoming more significant for corporations to work towards common environmental management practices. ISO 14001 is the first and the broadest standard intended at a more responsible approach of corporations and the world’s most acknowledged framework for environmental management systems that assists corporations to better manage the effect of their activities on the environment. This article aims to study ISO 14001 implementation and its effects on the environmental responsibility. A model will be built, which covers the environmental management system, the components of organizational culture, being able to influence environmental standards implementation.

  9. Poisson Mixture Regression Models for Heart Disease Prediction

    Science.gov (United States)

    Erol, Hamza

    2016-01-01

    Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

  10. Comparison of Predictive Models for the Early Diagnosis of Diabetes.

    Science.gov (United States)

    Jahani, Meysam; Mahdavi, Mahdi

    2016-04-01

    This study develops neural network models to improve the prediction of diabetes using clinical and lifestyle characteristics. Prediction models were developed using a combination of approaches and concepts. We used memetic algorithms to update weights and to improve prediction accuracy of models. In the first step, the optimum amount for neural network parameters such as momentum rate, transfer function, and error function were obtained through trial and error and based on the results of previous studies. In the second step, optimum parameters were applied to memetic algorithms in order to improve the accuracy of prediction. This preliminary analysis showed that the accuracy of neural networks is 88%. In the third step, the accuracy of neural network models was improved using a memetic algorithm and resulted model was compared with a logistic regression model using a confusion matrix and receiver operating characteristic curve (ROC). The memetic algorithm improved the accuracy from 88.0% to 93.2%. We also found that memetic algorithm had a higher accuracy than the model from the genetic algorithm and a regression model. Among models, the regression model has the least accuracy. For the memetic algorithm model the amount of sensitivity, specificity, positive predictive value, negative predictive value, and ROC are 96.2, 95.3, 93.8, 92.4, and 0.958 respectively. The results of this study provide a basis to design a Decision Support System for risk management and planning of care for individuals at risk of diabetes.

  11. Contrasting Water-Use Efficiency (WUE) Responses of a Potato Mapping Population and Capability of Modified Ball-Berry Model to Predict Stomatal Conductance and WUE Measured at Different Environmental Conditions

    DEFF Research Database (Denmark)

    Kaminski, K. P.; Sørensen, Kirsten Kørup; Kristensen, Kristian

    2015-01-01

    experiments in 2010. Two offspring groups according to pWUE and wpWUE were identified on the basis of experiments conducted in 2010, which in experiments in 2011 again showed significant differences in pWUE (46 %, P rate...... (34 %) and dry matter accumulation (55 %, P rate (-4 %, no significant difference) or whole-plant water use (16 %). The pWUE correlated negatively to the ratio between leaf-internal and leaf-external [CO2] (R2 = -0.86 in 2010 and R2 = -0.83 in 2011, P ....001). The leaf chlorophyll content was lower in the high-WUE group indicating that the higher net photosynthesis rate was not due to higher leaf-N status. Less negative value of carbon isotope discrimination (δ13C) in the high-WUE group was only found in 2011. A modified Ball-Berry model was fitted to measured...

  12. Applications of modeling in polymer-property prediction

    Science.gov (United States)

    Case, F. H.

    1996-08-01

    A number of molecular modeling techniques have been applied for the prediction of polymer properties and behavior. Five examples illustrate the range of methodologies used. A simple atomistic simulation of small polymer fragments is used to estimate drug compatibility with a polymer matrix. The analysis of molecular dynamics results from a more complex model of a swollen hydrogel system is used to study gas diffusion in contact lenses. Statistical mechanics are used to predict conformation dependent properties — an example is the prediction of liquid-crystal formation. The effect of the molecular weight distribution on phase separation in polyalkanes is predicted using thermodynamic models. In some cases, the properties of interest cannot be directly predicted using simulation methods or polymer theory. Correlation methods may be used to bridge the gap between molecular structure and macroscopic properties. The final example shows how connectivity-indices-based quantitative structure-property relationships were used to predict properties for candidate polyimids in an electronics application.

  13. Artificial Neural Network Model for Predicting Compressive

    OpenAIRE

    Salim T. Yousif; Salwa M. Abdullah

    2013-01-01

      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...

  14. Prediction of hourly solar radiation with multi-model framework

    International Nuclear Information System (INIS)

    Wu, Ji; Chan, Chee Keong

    2013-01-01

    Highlights: • A novel approach to predict solar radiation through the use of clustering paradigms. • Development of prediction models based on the intrinsic pattern observed in each cluster. • Prediction based on proper clustering and selection of model on current time provides better results than other methods. • Experiments were conducted on actual solar radiation data obtained from a weather station in Singapore. - Abstract: In this paper, a novel multi-model prediction framework for prediction of solar radiation is proposed. The framework started with the assumption that there are several patterns embedded in the solar radiation series. To extract the underlying pattern, the solar radiation series is first segmented into smaller subsequences, and the subsequences are further grouped into different clusters. For each cluster, an appropriate prediction model is trained. Hence a procedure for pattern identification is developed to identify the proper pattern that fits the current period. Based on this pattern, the corresponding prediction model is applied to obtain the prediction value. The prediction result of the proposed framework is then compared to other techniques. It is shown that the proposed framework provides superior performance as compared to others

  15. Site-specific data confirm arsenic exposure predicted by the U.S. Environmental Protection Agency.

    OpenAIRE

    Walker, S; Griffin, S

    1998-01-01

    The EPA uses an exposure assessment model to estimate daily intake to chemicals of potential concern. At the Anaconda Superfund site in Montana, the EPA exposure assessment model was used to predict total and speciated urinary arsenic concentrations. Predicted concentrations were then compared to concentrations measured in children living near the site. When site-specific information on concentrations of arsenic in soil, interior dust, and diet, site-specific ingestion rates, and arsenic abso...

  16. Predictive Modeling of Spinner Dolphin (Stenella longirostris) Resting Habitat in the Main Hawaiian Islands

    Science.gov (United States)

    Thorne, Lesley H.; Johnston, David W.; Urban, Dean L.; Tyne, Julian; Bejder, Lars; Baird, Robin W.; Yin, Suzanne; Rickards, Susan H.; Deakos, Mark H.; Mobley, Joseph R.; Pack, Adam A.; Chapla Hill, Marie

    2012-01-01

    Predictive habitat models can provide critical information that is necessary in many conservation applications. Using Maximum Entropy modeling, we characterized habitat relationships and generated spatial predictions of spinner dolphin (Stenella longirostris) resting habitat in the main Hawaiian Islands. Spinner dolphins in Hawai'i exhibit predictable daily movements, using inshore bays as resting habitat during daylight hours and foraging in offshore waters at night. There are growing concerns regarding the effects of human activities on spinner dolphins resting in coastal areas. However, the environmental factors that define suitable resting habitat remain unclear and must be assessed and quantified in order to properly address interactions between humans and spinner dolphins. We used a series of dolphin sightings from recent surveys in the main Hawaiian Islands and a suite of environmental variables hypothesized as being important to resting habitat to model spinner dolphin resting habitat. The model performed well in predicting resting habitat and indicated that proximity to deep water foraging areas, depth, the proportion of bays with shallow depths, and rugosity were important predictors of spinner dolphin habitat. Predicted locations of suitable spinner dolphin resting habitat provided in this study indicate areas where future survey efforts should be focused and highlight potential areas of conflict with human activities. This study provides an example of a presence-only habitat model used to inform the management of a species for which patterns of habitat availability are poorly understood. PMID:22937022

  17. Predictive modeling of spinner dolphin (Stenella longirostris resting habitat in the main Hawaiian Islands.

    Directory of Open Access Journals (Sweden)

    Lesley H Thorne

    Full Text Available Predictive habitat models can provide critical information that is necessary in many conservation applications. Using Maximum Entropy modeling, we characterized habitat relationships and generated spatial predictions of spinner dolphin (Stenella longirostris resting habitat in the main Hawaiian Islands. Spinner dolphins in Hawai'i exhibit predictable daily movements, using inshore bays as resting habitat during daylight hours and foraging in offshore waters at night. There are growing concerns regarding the effects of human activities on spinner dolphins resting in coastal areas. However, the environmental factors that define suitable resting habitat remain unclear and must be assessed and quantified in order to properly address interactions between humans and spinner dolphins. We used a series of dolphin sightings from recent surveys in the main Hawaiian Islands and a suite of environmental variables hypothesized as being important to resting habitat to model spinner dolphin resting habitat. The model performed well in predicting resting habitat and indicated that proximity to deep water foraging areas, depth, the proportion of bays with shallow depths, and rugosity were important predictors of spinner dolphin habitat. Predicted locations of suitable spinner dolphin resting habitat provided in this study indicate areas where future survey efforts should be focused and highlight potential areas of conflict with human activities. This study provides an example of a presence-only habitat model used to inform the management of a species for which patterns of habitat availability are poorly understood.

  18. Predicting photosynthesis and transpiration responses to ozone: decoupling modeled photosynthesis and stomatal conductance

    Directory of Open Access Journals (Sweden)

    D. Lombardozzi

    2012-08-01

    Full Text Available Plants exchange greenhouse gases carbon dioxide and water with the atmosphere through the processes of photosynthesis and transpiration, making them essential in climate regulation. Carbon dioxide and water exchange are typically coupled through the control of stomatal conductance, and the parameterization in many models often predict conductance based on photosynthesis values. Some environmental conditions, like exposure to high ozone (O3 concentrations, alter photosynthesis independent of stomatal conductance, so models that couple these processes cannot accurately predict both. The goals of this study were to test direct and indirect photosynthesis and stomatal conductance modifications based on O3 damage to tulip poplar (Liriodendron tulipifera in a coupled Farquhar/Ball-Berry model. The same modifications were then tested in the Community Land Model (CLM to determine the impacts on gross primary productivity (GPP and transpiration at a constant O3 concentration of 100 parts per billion (ppb. Modifying the Vcmax parameter and directly modifying stomatal conductance best predicts photosynthesis and stomatal conductance responses to chronic O3 over a range of environmental conditions. On a global scale, directly modifying conductance reduces the effect of O3 on both transpiration and GPP compared to indirectly modifying conductance, particularly in the tropics. The results of this study suggest that independently modifying stomatal conductance can improve the ability of models to predict hydrologic cycling, and therefore improve future climate predictions.

  19. Mathematical model in economic environmental problems

    Energy Technology Data Exchange (ETDEWEB)

    Nahorski, Z. [Polish Academy of Sciences, Systems Research Inst. (Poland); Ravn, H.F. [Risoe National Lab. (Denmark)

    1996-12-31

    The report contains a review of basic models and mathematical tools used in economic regulation problems. It starts with presentation of basic models of capital accumulation, resource depletion, pollution accumulation, and population growth, as well as construction of utility functions. Then the one-state variable model is discussed in details. The basic mathematical methods used consist of application of the maximum principle and phase plane analysis of the differential equations obtained as the necessary conditions of optimality. A summary of basic results connected with these methods is given in appendices. (au) 13 ills.; 17 refs.

  20. Posterior Predictive Model Checking for Multidimensionality in Item Response Theory

    Science.gov (United States)

    Levy, Roy; Mislevy, Robert J.; Sinharay, Sandip

    2009-01-01

    If data exhibit multidimensionality, key conditional independence assumptions of unidimensional models do not hold. The current work pursues posterior predictive model checking, a flexible family of model-checking procedures, as a tool for criticizing models due to unaccounted for dimensions in the context of item response theory. Factors…

  1. Model predictive control of a crude oil distillation column

    Directory of Open Access Journals (Sweden)

    Morten Hovd

    1999-04-01

    Full Text Available The project of designing and implementing model based predictive control on the vacuum distillation column at the Nynäshamn Refinery of Nynäs AB is described in this paper. The paper describes in detail the modeling for the model based control, covers the controller implementation, and documents the benefits gained from the model based controller.

  2. Environmental Modeling of Storm Water Channels

    OpenAIRE

    L. Grinis

    2014-01-01

    Turbulent flow in complex geometries receives considerable attention due to its importance in many engineering applications. It has been the subject of interest for many researchers. Some of these interests include the design of storm water channels. The design of these channels requires testing through physical models. The main practical limitation of physical models is the so called “scale effect”, that is, the fact that in many cases only primary physical mechanisms can be correctly repres...

  3. Agricultural and Environmental Input Parameters for the Biosphere Model

    International Nuclear Information System (INIS)

    K. Rasmuson; K. Rautenstrauch

    2004-01-01

    This analysis is one of 10 technical reports that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN) (i.e., the biosphere model). It documents development of agricultural and environmental input parameters for the biosphere model, and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the total system performance assessment (TSPA) for the repository at Yucca Mountain. The ERMYN provides the TSPA with the capability to perform dose assessments. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships between the major activities and their products (the analysis and model reports) that were planned in ''Technical Work Plan for Biosphere Modeling and Expert Support'' (BSC 2004 [DIRS 169573]). The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the ERMYN and its input parameters

  4. Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models

    Science.gov (United States)

    Spiliopoulou, Athina; Nagy, Reka; Bermingham, Mairead L.; Huffman, Jennifer E.; Hayward, Caroline; Vitart, Veronique; Rudan, Igor; Campbell, Harry; Wright, Alan F.; Wilson, James F.; Pong-Wong, Ricardo; Agakov, Felix; Navarro, Pau; Haley, Chris S.

    2015-01-01

    We explore the prediction of individuals' phenotypes for complex traits using genomic data. We compare several widely used prediction models, including Ridge Regression, LASSO and Elastic Nets estimated from cohort data, and polygenic risk scores constructed using published summary statistics from genome-wide association meta-analyses (GWAMA). We evaluate the interplay between relatedness, trait architecture and optimal marker density, by predicting height, body mass index (BMI) and high-density lipoprotein level (HDL) in two data cohorts, originating from Croatia and Scotland. We empirically demonstrate that dense models are better when all genetic effects are small (height and BMI) and target individuals are related to the training samples, while sparse models predict better in unrelated individuals and when some effects have moderate size (HDL). For HDL sparse models achieved good across-cohort prediction, performing similarly to the GWAMA risk score and to models trained within the same cohort, which indicates that, for predicting traits with moderately sized effects, large sample sizes and familial structure become less important, though still potentially useful. Finally, we propose a novel ensemble of whole-genome predictors with GWAMA risk scores and demonstrate that the resulting meta-model achieves higher prediction accuracy than either model on its own. We conclude that although current genomic predictors are not accurate enough for diagnostic purposes, performance can be improved without requiring access to large-scale individual-level data. Our methodologically simple meta-model is a means of performing predictive meta-analysis for optimizing genomic predictions and can be easily extended to incorporate multiple population-level summary statistics or other domain knowledge. PMID:25918167

  5. Measuring physical activity-related environmental factors: reliability and predictive validity of the European environmental questionnaire ALPHA

    Directory of Open Access Journals (Sweden)

    Oppert Jean-Michel

    2010-05-01

    Full Text Available Abstract Background A questionnaire to assess physical activity related environmental factors in the European population (a 49-item and an 11-item version was created as part of the framework of the EU-funded project "Instruments for Assessing Levels of PHysical Activity and fitness (ALPHA". This paper reports on the development and assessment of the questionnaire's test-retest stability, predictive validity, and applicability to European adults. Methods The first pilot test was conducted in Belgium, France and the UK. In total 190 adults completed both forms of the ALPHA questionnaire twice with a one-week interval. Physical activity was concurrently measured (i by administration of the long version of the International Physical Activity Questionnaire (IPAQ by interview and (ii by accelerometry (Actigraph™ device. After adaptations, the second field test took place in Belgium, the UK and Austria; 166 adults completed the adapted questionnaire at two time points, with minimum one-week interval. In both field studies intraclass correlation coefficients (ICC and proportion of agreement were computed to assess the stability of the two test scores. Predictive validity was examined in the first field test by correlating the results of the questionnaires with physical activity data from accelerometry and long IPAQ-last 7 days. Results The reliability scores of the ALPHA questionnaire were moderate-to good in the first field testing (ICC range 0.66 - 0.86 and good in the second field testing (ICC range 0.71 - 0.87. The proportion of agreement for the ALPHA short increased significantly from the first (range 50 - 83% to the second field testing (range 85 - 95%. Environmental scales from both versions of the ALPHA questionnaire were significantly associated with self-reported minutes of transport-related walking, and objectively measured low intensity physical activity levels, particularly in women. Both versions were easily administered with an

  6. Modelling an environmental pollutant transport from the stacks to and through the soil

    Directory of Open Access Journals (Sweden)

    Rushdi M.M. El-Kilani

    2010-07-01

    Full Text Available In this paper, a model is presented for predicting the transport of an environmental pollutant from the source to and through the soil. The model can predict the deposition of an environmental pollutant on the soil surface due to the pollutant being loaded on dust particles, which are later deposited on the soil surface. The model is a coupling of three models: a model for predicting the cumulative dust deposition from near and far field sources on a certain area; a canopy microclimate model for solving the energy partition within the canopy elements and so predicting the water convection stream for pollutant transport through the soil; and coupling the deposition of these pollutants on the soil surface to a model for its transport through the soil. The air pollution model uses the Gaussian model approach, superimposed for multiple emission sources, to elucidate the deposition of pollutant laden airborne particulates on the soil surface. A complete canopy layer model is used to calculate within the canopy energy fluxes. The retardation factor for the pollutant is calculated from an adsorption batch experiment. The model was used to predict the deposition of lead laden dust particles on the soil surface and lead's transport through the soil layers inside a metropolitan region for: (1 three large cement factories and (2 a large number of smelters. The results show that, due to the very high retardation values for lead movement through the soil, i.e. ranging from 4371 to 53,793 from previous data and 234 from the adsorption experiment in this paper, lead is immobile and all the lead added to the soil surface via deposited dust or otherwise, even if it is totally soluble, will remain mostly on the soil surface and not move downwards due to high affinity with the soil.

  7. Environmental Modeling and Bayesian Analysis for Assessing Human Health Impacts from Radioactive Waste Disposal

    Science.gov (United States)

    Stockton, T.; Black, P.; Tauxe, J.; Catlett, K.

    2004-12-01

    Bayesian decision analysis provides a unified framework for coherent decision-making. Two key components of Bayesian decision analysis are probability distributions and utility functions. Calculating posterior distributions and performing decision analysis can be computationally challenging, especially for complex environmental models. In addition, probability distributions and utility functions for environmental models must be specified through expert elicitation, stakeholder consensus, or data collection, all of which have their own set of technical and political challenges. Nevertheless, a grand appeal of the Bayesian approach for environmental decision- making is the explicit treatment of uncertainty, including expert judgment. The impact of expert judgment on the environmental decision process, though integral, goes largely unassessed. Regulations and orders of the Environmental Protection Agency, Department Of Energy, and Nuclear Regulatory Agency orders require assessing the impact on human health of radioactive waste contamination over periods of up to ten thousand years. Towards this end complex environmental simulation models are used to assess "risk" to human and ecological health from migration of radioactive waste. As the computational burden of environmental modeling is continually reduced probabilistic process modeling using Monte Carlo simulation is becoming routinely used to propagate uncertainty from model inputs through model predictions. The utility of a Bayesian approach to environmental decision-making is discussed within the context of a buried radioactive waste example. This example highlights the desirability and difficulties of merging the cost of monitoring, the cost of the decision analysis, the cost and viability of clean up, and the probability of human health impacts within a rigorous decision framework.

  8. Predictive models for acute kidney injury following cardiac surgery.

    Science.gov (United States)

    Demirjian, Sevag; Schold, Jesse D; Navia, Jose; Mastracci, Tara M; Paganini, Emil P; Yared, Jean-Pierre; Bashour, Charles A

    2012-03-01

    Accurate prediction of cardiac surgery-associated acute kidney injury (AKI) would improve clinical decision making and facilitate timely diagnosis and treatment. The aim of the study was to develop predictive models for cardiac surgery-associated AKI using presurgical and combined pre- and intrasurgical variables. Prospective observational cohort. 25,898 patients who underwent cardiac surgery at Cleveland Clinic in 2000-2008. Presurgical and combined pre- and intrasurgical variables were used to develop predictive models. Dialysis therapy and a composite of doubling of serum creatinine level or dialysis therapy within 2 weeks (or discharge if sooner) after cardiac surgery. Incidences of dialysis therapy and the composite of doubling of serum creatinine level or dialysis therapy were 1.7% and 4.3%, respectively. Kidney function parameters were strong independent predictors in all 4 models. Surgical complexity reflected by type and history of previous cardiac surgery were robust predictors in models based on presurgical variables. However, the inclusion of intrasurgical variables accounted for all explained variance by procedure-related information. Models predictive of dialysis therapy showed good calibration and superb discrimination; a combined (pre- and intrasurgical) model performed better than the presurgical model alone (C statistics, 0.910 and 0.875, respectively). Models predictive of the composite end point also had excellent discrimination with both presurgical and combined (pre- and intrasurgical) variables (C statistics, 0.797 and 0.825, respectively). However, the presurgical model predictive of the composite end point showed suboptimal calibration (P predictive models in other cohorts is required before wide-scale application. We developed and internally validated 4 new models that accurately predict cardiac surgery-associated AKI. These models are based on readily available clinical information and can be used for patient counseling, clinical

  9. Lumped parameter models for the interpretation of environmental tracer data

    International Nuclear Information System (INIS)

    Maloszewski, P.; Zuber, A.

    1996-01-01

    Principles of the lumped-parameter approach to the interpretation of environmental tracer data are given. The following models are considered: the piston flow model (PFM), exponential flow model (EM), linear model (LM), combined piston flow and exponential flow model (EPM), combined linear flow and piston flow model (LPM), and dispersion model (DM). The applicability of these models for the interpretation of different tracer data is discussed for a steady state flow approximation. Case studies are given to exemplify the applicability of the lumped-parameter approach. Description of a user-friendly computer program is given. (author). 68 refs, 25 figs, 4 tabs

  10. Modeling number of claims and prediction of total claim amount

    Science.gov (United States)

    Acar, Aslıhan Şentürk; Karabey, Uǧur

    2017-07-01

    In this study we focus on annual number of claims of a private health insurance data set which belongs to a local insurance company in Turkey. In addition to Poisson model and negative binomial model, zero-inflated Poisson model and zero-inflated negative binomial model are used to model the number of claims in order to take into account excess zeros. To investigate the impact of different distributional assumptions for the number of claims on the prediction of total claim amount, predictive performances of candidate models are compared by using root mean square error (RMSE) and mean absolute error (MAE) criteria.

  11. Assessment of performance of survival prediction models for cancer prognosis

    Directory of Open Access Journals (Sweden)

    Chen Hung-Chia

    2012-07-01

    Full Text Available Abstract Background Cancer survival studies are commonly analyzed using survival-time prediction models for cancer prognosis. A number of different performance metrics are used to ascertain the concordance between the predicted risk score of each patient and the actual survival time, but these metrics can sometimes conflict. Alternatively, patients are sometimes divided into two classes according to a survival-time threshold, and binary classifiers are applied to predict each patient’s class. Although this approach has several drawbacks, it does provide natural performance metrics such as positive and negative predictive values to enable unambiguous assessments. Methods We compare the survival-time prediction and survival-time threshold approaches to analyzing cancer survival studies. We review and compare common performance metrics for the two approaches. We present new randomization tests and cross-validation methods to enable unambiguous statistical inferences for several performance metrics used with the survival-time prediction approach. We consider five survival prediction models consisting of one clinical model, two gene expression models, and two models from combinations of clinical and gene expression models. Results A public breast cancer dataset was used to compare several performance metrics using five prediction models. 1 For some prediction models, the hazard ratio from fitting a Cox proportional hazards model was significant, but the two-group comparison was insignificant, and vice versa. 2 The randomization test and cross-validation were generally consistent with the p-values obtained from the standard performance metrics. 3 Binary classifiers highly depended on how the risk groups were defined; a slight change of the survival threshold for assignment of classes led to very different prediction results. Conclusions 1 Different performance metrics for evaluation of a survival prediction model may give different conclusions in

  12. Modelling techniques for predicting the long term consequences of radiation on natural aquatic populations

    International Nuclear Information System (INIS)

    Wallis, I.G.

    1978-01-01

    The purpose of this working paper is to describe modelling techniques for predicting the long term consequences of radiation on natural aquatic populations. Ideally, it would be possible to use aquatic population models: (1) to predict changes in the health and well-being of all aquatic populations as a result of changing the composition, amount and location of radionuclide discharges; (2) to compare the effects of steady, fluctuating and accidental releases of radionuclides; and (3) to evaluate the combined impact of the discharge of radionuclides and other wastes, and natural environmental stresses on aquatic populations. At the onset it should be stated that there is no existing model which can achieve this ideal performance. However, modelling skills and techniques are available to develop useful aquatic population models. This paper discusses the considerations involved in developing these models and briefly describes the various types of population models which have been developed to date

  13. Use of Plant Hydraulic Theory to Predict Ecosystem Fluxes Across Mountainous Gradients in Environmental Controls and Insect Disturbances

    Science.gov (United States)

    Ewers, B. E.; Pendall, E.; Reed, D. E.; Barnard, H. R.; Whitehouse, F.; Frank, J. M.; Massman, W. J.; Brooks, P. D.; Biederman, J. A.; Harpold, A. A.; Naithani, K. J.; Mitra, B.; Mackay, D. S.; Norton, U.; Borkhuu, B.

    2011-12-01

    While mountainous areas are critical for providing numerous ecosystem benefits at the regional scale, the strong gradients in environmental controls make predictions difficult. A key part of the problem is quantifying and predicting the feedback between mountain gradients and plant function which then controls ecosystem cycling. The emerging theory of plant hydraulics provides a rigorous yet simple platform from which to generate testable hypotheses and predictions of ecosystem pools and fluxes. Plant hydraulic theory predicts that plant controls over carbon, water, energy and nutrient fluxes can be derived from the limitation of plant water transport from the soil through xylem and out of stomata. In addition, the limit to plant water transport can be predicted by combining plant structure (e.g. xylem diameters or root-to-shoot ratios) and plant function (response of stomatal conductance to vapor pressure deficit or root vulnerability to cavitation). We evaluate the predictions of the plant hydraulic theory by testing it against data from a mountain gradient encompassing sagebrush steppe through subalpine forests (2700 to 3400 m). We further test the theory by predicting the carbon, water and nutrient exchanges from several coniferous trees in the same gradient that are dying from xylem dysfunction caused by blue-stain fungi carried by bark beetles. The common theme of both of these data sets is a change in water limitation caused by either changing precipitation along the mountainous gradient or lack of access to soil water from xylem-occluding fungi. Across all of the data sets which range in scale from individual plants to hillslopes, the data fit the predictions of plant hydraulic theory. Namely, there was a proportional tradeoff between the reference canopy stomatal conductance to water vapor and the sensitivity of that conductance to vapor pressure deficit that quantitatively fits the predictions of plant hydraulic theory. Incorporating this result into

  14. Dispersal and extrapolation on the accuracy of temporal predictions from distribution models for the Darwin's frog.

    Science.gov (United States)

    Uribe-Rivera, David E; Soto-Azat, Claudio; Valenzuela-Sánchez, Andrés; Bizama, Gustavo; Simonetti, Javier A; Pliscoff, Patricio

    2017-07-01

    Climate change is a major threat to biodiversity; the development of models that reliably predict its effects on species distributions is a priority for conservation biogeography. Two of the main issues for accurate temporal predictions from Species Distribution Models (SDM) are model extrapolation and unrealistic dispersal scenarios. We assessed the consequences of these issues on the accuracy of climate-driven SDM predictions for the dispersal-limited Darwin's frog Rhinoderma darwinii in South America. We calibrated models using historical data (1950-1975) and projected them across 40 yr to predict distribution under current climatic conditions, assessing predictive accuracy through the area under the ROC curve (AUC) and True Skill Statistics (TSS), contrasting binary model predictions against temporal-independent validation data set (i.e., current presences/absences). To assess the effects of incorporating dispersal processes we compared the predictive accuracy of dispersal constrained models with no dispersal limited SDMs; and to assess the effects of model extrapolation on the predictive accuracy of SDMs, we compared this between extrapolated and no extrapolated areas. The incorporation of dispersal processes enhanced predictive accuracy, mainly due to a decrease in the false presence rate of model predictions, which is consistent with discrimination of suitable but inaccessible habitat. This also had consequences on range size changes over time, which is the most used proxy for extinction risk from climate change. The area of current climatic conditions that was absent in the baseline conditions (i.e., extrapolated areas) represents 39% of the study area, leading to a significant decrease in predictive accuracy of model predictions for those areas. Our results highlight (1) incorporating dispersal processes can improve predictive accuracy of temporal transference of SDMs and reduce uncertainties of extinction risk assessments from global change; (2) as

  15. Explicit prediction of ice clouds in general circulation models

    Science.gov (United States)

    Kohler, Martin

    1999-11-01

    Although clouds play extremely important roles in the radiation budget and hydrological cycle of the Earth, there are large quantitative uncertainties in our understanding of their generation, maintenance and decay mechanisms, representing major obstacles in the development of reliable prognostic cloud water schemes for General Circulation Models (GCMs). Recognizing their relative neglect in the past, both observationally and theoretically, this work places special focus on ice clouds. A recent version of the UCLA - University of Utah Cloud Resolving Model (CRM) that includes interactive radiation is used to perform idealized experiments to study ice cloud maintenance and decay mechanisms under various conditions in term of: (1) background static stability, (2) background relative humidity, (3) rate of cloud ice addition over a fixed initial time-period and (4) radiation: daytime, nighttime and no-radiation. Radiation is found to have major effects on the life-time of layer-clouds. Optically thick ice clouds decay significantly slower than expected from pure microphysical crystal fall-out (taucld = 0.9--1.4 h as opposed to no-motion taumicro = 0.5--0.7 h). This is explained by the upward turbulent fluxes of water induced by IR destabilization, which partially balance the downward transport of water by snowfall. Solar radiation further slows the ice-water decay by destruction of the inversion above cloud-top and the resulting upward transport of water. Optically thin ice clouds, on the other hand, may exhibit even longer life-times (>1 day) in the presence of radiational cooling. The resulting saturation mixing ratio reduction provides for a constant cloud ice source. These CRM results are used to develop a prognostic cloud water scheme for the UCLA-GCM. The framework is based on the bulk water phase model of Ose (1993). The model predicts cloud liquid water and cloud ice separately, and which is extended to split the ice phase into suspended cloud ice (predicted

  16. Environmental indicators and international models for making decision

    International Nuclear Information System (INIS)

    Polanco, Camilo

    2006-01-01

    The last international features proposed by the Organization for Economic Cooperation Development (OECD) and United Nations (UN) are analyzed in the use of the environmental indicators, in typology, selection criteria, and models, for organizing the information for management, environmental performance, and decision making. The advantages and disadvantages of each model are analyzed, as well as their environmental index characteristics. The analyzed models are Pressure - State - Response (PSR) and its conceptual developments: Driving Force - State Response (DSR), Driving Force - Pressure - State - Impact - Response (DPSIR), Model- Flow-Quality (MFQ), Pressure - State - Impact - Effect - Response (PSIER), and, finally, Pressure-State - Impact - Effect - Response - Management (PSIERM). The use of one or another model will depend on the quality of the available information, as well as on the proposed objectives

  17. Prediction Model for Gastric Cancer Incidence in Korean Population.

    Science.gov (United States)

    Eom, Bang Wool; Joo, Jungnam; Kim, Sohee; Shin, Aesun; Yang, Hye-Ryung; Park, Junghyun; Choi, Il Ju; Kim, Young-Woo; Kim, Jeongseon; Nam, Byung-Ho

    2015-01-01

    Predicting high risk groups for gastric cancer and motivating these groups to receive regular checkups is required for the early detection of gastric cancer. The aim of this study is was to develop a prediction model for gastric cancer incidence based on a large population-based cohort in Korea. Based on the National Health Insurance Corporation data, we analyzed 10 major risk factors for gastric cancer. The Cox proportional hazards model was used to develop gender specific prediction models for gastric cancer development, and the performance of the developed model in terms of discrimination and calibration was also validated using an independent cohort. Discrimination ability was evaluated using Harrell's C-statistics, and the calibration was evaluated using a calibration plot and slope. During a median of 11.4 years of follow-up, 19,465 (1.4%) and 5,579 (0.7%) newly developed gastric cancer cases were observed among 1,372,424 men and 804,077 women, respectively. The prediction models included age, BMI, family history, meal regularity, salt preference, alcohol consumption, smoking and physical activity for men, and age, BMI, family history, salt preference, alcohol consumption, and smoking for women. This prediction model showed good accuracy and predictability in both the developing and validation cohorts (C-statistics: 0.764 for men, 0.706 for women). In this study, a prediction model for gastric cancer incidence was developed that displayed a good performance.

  18. AN EFFICIENT PATIENT INFLOW PREDICTION MODEL FOR HOSPITAL RESOURCE MANAGEMENT

    Directory of Open Access Journals (Sweden)

    Kottalanka Srikanth

    2017-07-01

    Full Text Available There has been increasing demand in improving service provisioning in hospital resources management. Hospital industries work with strict budget constraint at the same time assures quality care. To achieve quality care with budget constraint an efficient prediction model is required. Recently there has been various time series based prediction model has been proposed to manage hospital resources such ambulance monitoring, emergency care and so on. These models are not efficient as they do not consider the nature of scenario such climate condition etc. To address this artificial intelligence is adopted. The issues with existing prediction are that the training suffers from local optima error. This induces overhead and affects the accuracy in prediction. To overcome the local minima error, this work presents a patient inflow prediction model by adopting resilient backpropagation neural network. Experiment are conducted to evaluate the performance of proposed model inter of RMSE and MAPE. The outcome shows the proposed model reduces RMSE and MAPE over existing back propagation based artificial neural network. The overall outcomes show the proposed prediction model improves the accuracy of prediction which aid in improving the quality of health care management.

  19. Risk Prediction Model for Severe Postoperative Complication in Bariatric Surgery.

    Science.gov (United States)

    Stenberg, Erik; Cao, Yang; Szabo, Eva; Näslund, Erik; Näslund, Ingmar; Ottosson, Johan

    2018-01-12

    Factors associated with risk for adverse outcome are important considerations in the preoperative assessment of patients for bariatric surgery. As yet, prediction models based on preoperative risk factors have not been able to predict adverse outcome sufficiently. This study aimed to identify preoperative risk factors and to construct a risk prediction model based on these. Patients who underwent a bariatric surgical procedure in Sweden between 2010 and 2014 were identified from the Scandinavian Obesity Surgery Registry (SOReg). Associations between preoperative potential risk factors and severe postoperative complications were analysed using a logistic regression model. A multivariate model for risk prediction was created and validated in the SOReg for patients who underwent