An Ensemble Hybrid Forecasting Model for Annual Runoff Based on Sample Entropy, Secondary Decomposition, and Long Short-Term Memory Neural Network

Wen-chuan Wang (), Yu-jin Du (), Kwok-wing Chau (), Dong-mei Xu (), Chang-jun Liu () and Qiang Ma ()
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Wen-chuan Wang: North China University of Water Resources and Electric Power
Yu-jin Du: North China University of Water Resources and Electric Power
Kwok-wing Chau: The Hong Kong Polytechnic University
Dong-mei Xu: North China University of Water Resources and Electric Power
Chang-jun Liu: China Institute of Water Resources and Hydropower Research
Qiang Ma: China Institute of Water Resources and Hydropower Research

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2021, vol. 35, issue 14, No 2, 4695-4726

Abstract: Abstract Accurate and consistent annual runoff prediction in a region is a hot topic in management, optimization, and monitoring of water resources. A novel prediction model (ESMD-SE-WPD-LSTM) is presented in this study. Firstly, extreme-point symmetric mode decomposition (ESMD) is used to produce several intrinsic mode functions (IMF) and a residual (Res) by decomposing the original runoff series. Secondly, sample entropy (SE) method is employed to measure the complexity of each IMF. Thirdly, wavelet packet decomposition (WPD) is adopted to further decompose the IMF with the maximum SE into several appropriate components. Then long short-term memory (LSTM) model, a deep learning algorithm based recurrent approach, is employed to predict all components. Finally, forecasting results of all components are aggregated to generate the final prediction. The proposed model, which is applied to seven annual series from different areas in China, is evaluated based on four evaluation indexes (R, MAE, MAPE and RMSE). Results indicate that ESMD-SE-WPD-LSTM outperforms other benchmark models in terms of four evaluation indexes. Hence the proposed model can provide higher accuracy and consistency for annual runoff prediction, rendering it an efficient instrument for scientific management and planning of water resources.

Keywords: Annual runoff prediction; Two-phase decomposition; Long short-term memory; Extreme-point symmetric mode decomposition; Wavelet packet decomposition; Sample entropy (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11269-021-02920-5

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