1. Monthly Streamflow Forecasting Using ELM-IPSO Based on Phase Space Reconstruction
- Author
-
Hongtao Zhao, Xin Bao, Xuyong Li, Xianing Wu, Yan Jiang, and Shaonan Hao
- Subjects
Correlation dimension ,010504 meteorology & atmospheric sciences ,Artificial neural network ,0208 environmental biotechnology ,Chaotic ,Particle swarm optimization ,02 engineering and technology ,Lyapunov exponent ,01 natural sciences ,Ensemble learning ,020801 environmental engineering ,symbols.namesake ,Streamflow ,symbols ,Algorithm ,0105 earth and related environmental sciences ,Water Science and Technology ,Civil and Structural Engineering ,Extreme learning machine ,Mathematics - 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.
- Published
- 2020