期刊
WATER RESOURCES MANAGEMENT
卷 35, 期 14, 页码 4695-4726出版社
SPRINGER
DOI: 10.1007/s11269-021-02920-5
关键词
Annual runoff prediction; Two-phase decomposition; Long short-term memory; Extreme-point symmetric mode decomposition; Wavelet packet decomposition; Sample entropy
资金
- Project of key science and technology of the Henan province [202102310259, 202102310588]
- Henan province university scientific and technological innovation team [18IRTSTHN009]
The ESMD-SE-WPD-LSTM model proposed in this study, applied to seven annual series from different areas in China, outperforms other benchmark models in terms of annual runoff prediction, providing higher accuracy and consistency.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据