Journal
WATER RESOURCES MANAGEMENT
Volume 26, Issue 10, Pages 2781-2797Publisher
SPRINGER
DOI: 10.1007/s11269-012-0045-x
Keywords
Artificial Neural Networks; Feed-forward network; Watershed model; Runoff; GWLF; SWAT; West Basin Delaware River watershed
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This paper illustrates the use of artificial neural networks (ANNs) as predictors of the nutrient load from a watershed. Accurate prediction of pollutant loadings has been recognized as important for determining effective water management strategies. This study compares Haith's Generalized Watershed Loading Function (GWLF) and Arnold's Soil and Water Assessment Tool (SWAT) to multilayer artificial neural networks for monthly watershed load modeling. The modeling results indicate that calibrated feed-forward ANN models provide prediction which are always essentially as accurate as those obtained with GWLF and the SWAT, and some times much more accurate. With its flexibility and computation efficiency, the ANN should be a useful tool to obtain a quick simulation assessment of nutrient loading for various management strategies.
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