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- W1997029598 abstract "The overall goal of this study was to determine if it is beneficial (computationally efficient)and appropriate to utilize artificial neural network (ANN) models in place of nonpoint source pollution(NPS) models for some water quality applications, e.g., pesticide leaching. The first objective was todetermine the predictive capability of the ANN model in replacing the NPS model (Pesticide RootZone Model, PRZM-3). A combination of 55 soil, crop, chemical and environmental input parametersof PRZM-3 were inputs to the network. Solution concentrations and average annual fluxes of atrazinewere simulated in Hagerstown silt loam soil at a depth of 1.57 m. The ANN models were trainedusing 6,000 stochastically generated sets of input and output parameters of PRZM-3 usingLevenberg-Marquardt learning algorithm in back propagation technique. The developed ANN modelswere more computationally efficient than PRZM-3.<br><br>The second objective was to determine if cause-effect relationships represented by the ANN modelwere equivalent to those in the NPS model. The sensitivity analysis results of the ANNs were compared with linear regression analysis of PRZM-3. The ranking of the input parameters to whichthe outputs were most sensitive were similar for neural network models and linear regressionmodels." @default.
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- W1997029598 date "2004-01-01" @default.
- W1997029598 modified "2023-09-26" @default.
- W1997029598 title "Sensitivity Analysis in Nonpoint Source Pollution Using Neural Network Models" @default.
- W1997029598 doi "https://doi.org/10.13031/2013.16208" @default.
- W1997029598 hasPublicationYear "2004" @default.
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