Comparing the performance of empirical black-box models for river flow forecasting in the Heihe River Basin, Northwestern China

Authors

  • Zhibin He,

    Corresponding author
    1. Linze Inland River Basin Research Station, Chinese Ecosystem Research Network, Key Laboratory of Ecohydrology of Inland River Basin, Lanzhou, China
    2. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, China
    • Correspondence to: Zhibin He, Linze Inland River Basin Research Station, Chinese Ecosystem Research Network, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China.

      E-mail: hzbmail@lzb.ac.cn

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  • Wenzhi Zhao,

    1. Linze Inland River Basin Research Station, Chinese Ecosystem Research Network, Key Laboratory of Ecohydrology of Inland River Basin, Lanzhou, China
    2. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, China
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  • Hu Liu

    1. Linze Inland River Basin Research Station, Chinese Ecosystem Research Network, Key Laboratory of Ecohydrology of Inland River Basin, Lanzhou, China
    2. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, China
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Abstract

For many practical reasons, the empirical black-box models have become an increasingly popular modelling tool for river flow forecasting, especially in mountainous areas where very few meteorological observatories exist. In this article, precipitation data are used as the only input to estimate river flow. Using five empirical black-box models—the simple linear model, the linear perturbation model, the linearly varying gain factor model, the constrained nonlinear system model and the nonlinear perturbation model–antecedent precipitation index—modelling results are compared with actual results in three catchments within the Heihe River Basin. The linearly varying gain factor model and the nonlinear perturbation model yielded excellent predictions. For better simulation accuracy, a commonly used multilayer feed-forward neural network model (NNM) was applied to incorporate the outputs of the individual models. Comparing the performance of these models, it was found that the best results were obtained from the NNM model. The results also suggest that more reliable and precise predictions of river flow can be obtained by using the NNM model while also incorporating the combined outputs of different empirical black-box models. Copyright © 2012 John Wiley & Sons, Ltd.

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