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, Qiang Ma

Research output: Journal article publicationJournal articleAcademic researchpeer-review

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.

Original languageEnglish
Pages (from-to)4695-4726
Number of pages32
JournalWater Resources Management
Volume35
Issue number14
DOIs
Publication statusPublished - Nov 2021

Keywords

  • Annual runoff prediction
  • Extreme-point symmetric mode decomposition
  • Long short-term memory
  • Sample entropy
  • Two-phase decomposition
  • Wavelet packet decomposition

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Water Science and Technology

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