Improving peak flow estimates in artificial neural network river flow models

Authors

  • K. P. Sudheer,

    Corresponding author
    1. National Institute of Hydrology, Deltaic Regional Centre, Kakinada (KPS, PCN) and National Institute of Hydrology, Roorkee (KSR), India
    • National Institute of Hydrology, Deltaic Regional Centre, Kakinada (KPS, PCN) and National Institute of Hydrology, Roorkee (KSR), India.
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  • P. C. Nayak,

    1. National Institute of Hydrology, Deltaic Regional Centre, Kakinada (KPS, PCN) and National Institute of Hydrology, Roorkee (KSR), India
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  • K. S. Ramasastri

    1. National Institute of Hydrology, Deltaic Regional Centre, Kakinada (KPS, PCN) and National Institute of Hydrology, Roorkee (KSR), India
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Abstract

In this paper, the concern of accuracy in peak estimation by the artificial neural network (ANN) river flow models is discussed and a suitable statistical procedure to get better estimates from these models is presented. The possible cause for underestimation of peak flow values has been attributed to the local variations in the function being mapped due to varying skewness in the data series, and theoretical considerations of the network functioning confirm this. It is envisaged that an appropriate data transformation will reduce the local variations in the function being mapped, and thus any ANN model built on the transformed series should perform better. This heuristic is illustrated and confirmed by many case studies and the results suggest that the model performance is significantly improved by data transformation. The model built on transformed data outperforms the model built on raw data in terms of various statistical performance indices. The peak estimates are improved significantly by data transformation. Copyright © 2003 John Wiley & Sons, Ltd.

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