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

Yan Jiang (), Xin Bao, Shaonan Hao, Hongtao Zhao, Xuyong Li and Xianing Wu
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Yan Jiang: Chinese Academy of Sciences
Xin Bao: 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

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2020, vol. 34, issue 11, No 6, 3515-3531

Abstract: 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.

Keywords: Streamflow prediction; Chaohe River basin; Chaotic dynamic characteristics; Phase space reconstruction; Extreme learning machine; Improved particle swarm optimization algorithm (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11269-020-02631-3

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