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Monthly Streamflow Forecasting Using ELM-IPSO Based on Phase Space Reconstruction

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Abstract

We have developed a hybrid model that integrates chaos theory and an extreme learning machine with optimal parameters selected using an improved particle swarm optimization (ELM-IPSO) for monthly runoff analysis and prediction. Monthly streamflow data covering a period of 55 years from Daiying hydrological station in the Chaohe River basin in northern China were used for the study. The Lyapunov exponent, the correlation dimension method, and the nonlinear prediction method were used to characterize the streamflow data. With the time series of the reconstructed phase space matrix as input variables, an improved particle swarm optimization was used to improve the performance of the extreme learning machine. Finally, the optimal chaotic ensemble learning model for monthly streamflow prediction was obtained. The accuracy of the predictions of the streamflow series (linear correlation coefficient of about 0.89 and efficiency coefficient of about 0.78) indicate the validity of our approach for predicting streamflow dynamics. The developed method had a higher prediction accuracy compared with an auto-regression method, an artificial neural network, an extreme learning machine with genetic algorithm and with PSO algorithm, suggesting that ELM-IPSO is an efficient method for monthly streamflow prediction.

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Acknowledgments

This research was supported by the National Key Research and Development Plan (2016YFD0201206).

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Authors and Affiliations

  1. State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China

    Yan Jiang, Xin Bao, Shaonan Hao, Hongtao Zhao & Xuyong Li

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Xin Bao

  3. PowerChina Resources Limited, Beijing, 100044, China

    Xianing Wu

Authors
  1. Yan Jiang
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  2. Xin Bao
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  3. Shaonan Hao
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  4. Hongtao Zhao
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  5. Xuyong Li
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  6. Xianing Wu
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Correspondence to Yan Jiang.

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Jiang, Y., Bao, X., Hao, S. et al. Monthly Streamflow Forecasting Using ELM-IPSO Based on Phase Space Reconstruction. Water Resour Manage 34, 3515–3531 (2020). https://doi.org/10.1007/s11269-020-02631-3

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  • DOI: https://doi.org/10.1007/s11269-020-02631-3

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