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

• Published in: Water resources management Vol. 35; no. 14; pp. 4695 - 4726
• Main Authors: Wang, Wen-chuan, Du, Yu-jin, Chau, Kwok-wing, Xu, Dong-mei, Liu, Chang-jun, Ma, Qiang
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.11.2021; Springer Nature B.V
• Subjects: Algorithms; Annual; Annual runoff; Atmospheric Sciences; China; Civil Engineering; Components; Decomposition; Deep learning; Earth and Environmental Science; Earth Sciences; Entropy; Environment; Evaluation; Forecasting; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Hydrology/Water Resources; Long short-term memory; Machine learning; Mathematical models; Neural networks; Optimization; prediction; Prediction models; Runoff; Water monitoring; Water resources; wavelet; China; Long short-term memory; Sample entropy; Extreme-point symmetric mode decomposition; Annual runoff prediction; Two-phase decomposition; Wavelet packet decomposition
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-021-02920-5

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.