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Artificial Neural Network Models of Watershed Nutrient Loading

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  • Raymond Kim
  • Daniel Loucks
  • Jery Stedinger

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

Suggested Citation

  • Raymond Kim & Daniel Loucks & Jery Stedinger, 2012. "Artificial Neural Network Models of Watershed Nutrient Loading," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(10), pages 2781-2797, August.
    • Handle: RePEc:spr:waterr:v:26:y:2012:i:10:p:2781-2797
    DOI: 10.1007/s11269-012-0045-x
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    1. Purna Nayak & Y. Rao & K. Sudheer, 2006. "Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(1), pages 77-90, February.
    2. Sheelabhadra Mohanty & Madan Jha & Ashwani Kumar & K. Sudheer, 2010. "Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(9), pages 1845-1865, July.
    3. R. Gopakumar & Kaoru Takara & E. James, 2007. "Hydrologic Data Exploration and River Flow Forecasting of a Humid Tropical River Basin Using Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(11), pages 1915-1940, November.
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    Cited by:

    1. Xiao Pu & Hongguang Cheng & Lu Lu & Shengtian Yang & Jing Xie & Fanghua Hao, 2015. "Spatial Profiling and Assessing Dominance of Sources to Water Phosphorus Burden in a Shallow Lake," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(3), pages 715-729, February.
    2. Jenq-Tzong Shiau & Hui-Ting Hsu, 2016. "Suitability of ANN-Based Daily Streamflow Extension Models: a Case Study of Gaoping River Basin, Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1499-1513, March.
    3. Zuoda Qi & Gelin Kang & Minli Shen & Yuqiu Wang & Chunli Chu, 2019. "The Improvement in GWLF Model Simulation Performance in Watershed Hydrology by Changing the Transport Framework," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 923-937, February.
    4. Animesh Debnath & Mrinmoy Majumder & Manish Pal, 2015. "A Cognitive Approach in Selection of Source for Water Treatment Plant based on Climatic Impact," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 1907-1919, April.
    5. Vladimir Nikolic & Slobodan Simonovic & Dragan Milicevic, 2013. "Analytical Support for Integrated Water Resources Management: A New Method for Addressing Spatial and Temporal Variability," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(2), pages 401-417, January.
    6. Jenq-Tzong Shiau & Hui-Ting Hsu, 2016. "Suitability of ANN-Based Daily Streamflow Extension Models: a Case Study of Gaoping River Basin, Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1499-1513, March.

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