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

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 in...

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Bibliographic Details
Published in:Water resources management 2021-11, Vol.35 (14), p.4695-4726
Main Authors: Wang, Wen-chuan, Du, Yu-jin, Chau, Kwok-wing, Xu, Dong-mei, Liu, Chang-jun, Ma, Qiang
Format: Article
Language:English
Subjects:
Algorithms
Annual
Annual runoff
Atmospheric Sciences
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 models
Runoff
Water monitoring
Water resources
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Summary: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.
ISSN:0920-4741
1573-1650
DOI:10.1007/s11269-021-02920-5