Artificial Neural Network Models of Watershed Nutrient Loading

Raymond Kim (), Daniel Loucks () and Jery Stedinger ()

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2012, vol. 26, issue 10, 2797 pages

Abstract: 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. Copyright Springer Science+Business Media B.V. 2012

Keywords: Artificial Neural Networks; Feed-forward network; Watershed model; Runoff; GWLF; SWAT; West Basin Delaware River watershed (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s11269-012-0045-x

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