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Joint Application of Artificial Neural Networks and Evolutionary Algorithms to Watershed Management

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  • Misgana Muleta
  • John Nicklow

Abstract

Artificial neural networks (ANNs) have become common data driven tools for modeling complex, nonlinear problems in science and engineering. Many previous applications have relied on gradient-based search techniques, such as the back propagation (BP) algorithm, for ANN training. Such techniques, however, are highly susceptible to premature convergence to local optima and require a trial-and-error process for effective design of ANN architecture and connection weights. This paper investigates the use of evolutionary programming (EP), a robust search technique, and a hybrid EP–BP training algorithm for improved ANN design. Application results indicate that the EP–BP algorithm may limit the drawbacks of using local search algorithms alone and that the hybrid performs better than EP from the perspective of both training accuracy and efficiency. In addition, the resulting ANN is used to replace the hydrologic simulation component of a previously developed multiobjective decision support model for watershed management. Due to the efficiency of the trained ANN with respect to the traditional simulation model, the replacement reduced the overall computational time required to generate preferred watershed management policies by 75%. The reduction is likely to improve the practical utility of the management model from a typical user perspective. Moreover, the results reveal the potential role of properly trained ANNs in addressing computational demands of various problems without sacrificing the accuracy of solutions. Copyright Kluwer Academic Publishers 2004

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  • Misgana Muleta & John Nicklow, 2004. "Joint Application of Artificial Neural Networks and Evolutionary Algorithms to Watershed Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(5), pages 459-482, October.
    • Handle: RePEc:spr:waterr:v:18:y:2004:i:5:p:459-482
    DOI: 10.1023/B:WARM.0000049140.64059.d1
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    References listed on IDEAS

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    1. A. Cancelliere & G. Giuliano & A. Ancarani & G. Rossi, 2002. "A Neural Networks Approach for Deriving Irrigation Reservoir Operating Rules," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 16(1), pages 71-88, February.
    2. Ashu Jain & Ashish Kumar Varshney & Umesh Chandra Joshi, 2001. "Short-Term Water Demand Forecast Modelling at IIT Kanpur 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. 15(5), pages 299-321, October.
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    Cited by:

    1. Jonathon Chill & Larry Mays, 2013. "Determination of the Optimal Location for Developments to Minimize Detention Requirements," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(15), pages 5089-5100, December.
    2. Said Jalala & Azzedine Hani & Isam Shahrour, 2011. "Characterizing the Socio-Economic Driving Forces of Groundwater Abstraction with Artificial Neural Networks and Multivariate Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(9), pages 2147-2175, July.
    3. Kwan Lee & Wei-Chiao Hung & Chung-Chieh Meng, 2008. "Deterministic Insight into ANN Model Performance for Storm Runoff Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(1), pages 67-82, January.
    4. Karen Goff & Randall Gentry, 2006. "The Influence of Watershed and Development Characteristics on the Cumulative Impacts of Stormwater Detention Ponds," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(6), pages 829-860, December.

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