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Water Resources Management
Published in: Water Resources Management 10/2012

01-08-2012

Artificial Neural Network Models of Watershed Nutrient Loading

Authors: Raymond J. Kim, Daniel P. Loucks, Jery R. Stedinger

Published in: Water Resources Management | Issue 10/2012

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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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Metadata
Title
Artificial Neural Network Models of Watershed Nutrient Loading
Authors
Raymond J. Kim
Daniel P. Loucks
Jery R. Stedinger
Publication date
01-08-2012
Publisher
Springer Netherlands
Published in
Water Resources Management / Issue 10/2012
Print ISSN: 0920-4741
Electronic ISSN: 1573-1650
DOI
https://doi.org/10.1007/s11269-012-0045-x

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