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

Author

Listed:
  • Yan Jiang

    (Chinese Academy of Sciences)

  • Xin Bao

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Shaonan Hao

    (Chinese Academy of Sciences)

  • Hongtao Zhao

    (Chinese Academy of Sciences)

  • Xuyong Li

    (Chinese Academy of Sciences)

  • Xianing Wu

    (PowerChina Resources Limited)

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.

Suggested Citation

  • Yan Jiang & Xin Bao & Shaonan Hao & Hongtao Zhao & Xuyong Li & Xianing Wu, 2020. "Monthly Streamflow Forecasting Using ELM-IPSO Based on Phase Space Reconstruction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(11), pages 3515-3531, September.
  • Handle: RePEc:spr:waterr:v:34:y:2020:i:11:d:10.1007_s11269-020-02631-3
    DOI: 10.1007/s11269-020-02631-3
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    References listed on IDEAS

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    1. Milan Palus & L. Pecen & D. Pivka, 1995. "Estimating Predictability: Redundancy and Surrogate Data Method," Working Papers 95-07-060, Santa Fe Institute.
    2. J. Vicente-Guillén & E. Ayuga-Telléz & D. Otero & J. Chávez & F. Ayuga & A. García, 2012. "Performance of a Monthly Streamflow Prediction Model for Ungauged Watersheds in Spain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(13), pages 3767-3784, October.
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    Cited by:

    1. Rana Muhammad Adnan Ikram & Leonardo Goliatt & Ozgur Kisi & Slavisa Trajkovic & Shamsuddin Shahid, 2022. "Covariance Matrix Adaptation Evolution Strategy for Improving Machine Learning Approaches in Streamflow Prediction," Mathematics, MDPI, vol. 10(16), pages 1-30, August.
    2. Jihong Qu & Kun Ren & Xiaoyu Shi, 2021. "Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 1029-1045, February.
    3. Jincheng Zhou & Dan Wang & Shahab S. Band & Changhyun Jun & Sayed M. Bateni & M. Moslehpour & Hao-Ting Pai & Chung-Chian Hsu & Rasoul Ameri, 2023. "Monthly River Discharge Forecasting Using Hybrid Models Based on Extreme Gradient Boosting Coupled with Wavelet Theory and Lévy–Jaya Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 3953-3972, August.

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