Flood Forecasting Using Neural Computing Techniques and Conceptual Class Segregation

Authors

  • Sungwon Kim,

    Associate Professor
    1. Department of Railroad and Civil Engineering, Dongyang University, Yeongju, Republic of Korea
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  • Vijay P. Singh

    Caroline & William N. Lehrer Distinguished Chair and Professor
    1. Water Engineering, Department of Biological and Agricultural Engineering & Department of Civil & Environmental Engineering, Texas A & M University, College Station, Texas
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  • Paper No. JAWRA-12-0261-P of the Journal of the American Water Resources Association (JAWRA).
  • Discussions are open until six months from print publication.

Abstract

Various neural networks models are developed and applied for flood forecasting at Sangye station (no. 1) of the Bocheong Stream catchment, which is one of the International Hydrological Program's representative catchments, Republic of Korea. The neural networks models (NNMs) are multilayer perceptron-neural networks model (MLP-NNM), generalized regression neural networks model (GRNNM), and Kohonen self-organizing feature maps neural networks model (KSOFM-NNM). Data used for model training and testing are divided into two groups: such as floods and typhoon events. Single conventional application and class segregation implementation are applied to evaluate the neural networks models. KSOFM-NNM forecasts flood discharge more accurately than do MLP-NNM and GRNNM for the testing data of Methods I and II for single conventional application and class segregation implementation. This study shows that class segregation can capture the dynamics of different physical processes and overcome the difficulties using single conventional application of neural networks models.

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