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.
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Acknowledgment
The authors thank Aris Georgakakos, Steve Pacenka, and New York State Department of Environmental Conservation, Division of Water for providing the loading data and suggestions.
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Kim, R.J., Loucks, D.P. & Stedinger, J.R. Artificial Neural Network Models of Watershed Nutrient Loading. Water Resour Manage 26, 2781–2797 (2012). https://doi.org/10.1007/s11269-012-0045-x
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DOI: https://doi.org/10.1007/s11269-012-0045-x