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Reservoir water level prediction using combined CEEMDAN-FE and RUN-SVM-RBFNN machine learning algorithms

Lan-ting Zhou Guan-lin Long Can-can Hu Kai Zhang

水科学与水工程2025，Vol.18Issue(2)：177-186,10.

水科学与水工程2025，Vol.18Issue(2)：177-186,10.DOI:10.1016/j.wse.2025.01.002

Reservoir water level prediction using combined CEEMDAN-FE and RUN-SVM-RBFNN machine learning algorithms

Lan-ting Zhou ¹Guan-lin Long ¹Can-can Hu ²Kai Zhang³

作者信息

1. College of Water Conservancy and Hydropower Engineering,Hohai University,Nanjing 210098,China
2. College of Water Conservancy and Hydropower Engineering,Hohai University,Nanjing 210098,China||Jianghe Anlan Engineering Consulting Co.,Ltd.,Zhengzhou 450003,China
3. Nanjing Hydraulic Research Institute,Nanjing 210029,China
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摘要

关键词

Time series/Environmental variable/Reservoir water level/Data decomposition/Optimization/Forecasting

Key words

Time series/Environmental variable/Reservoir water level/Data decomposition/Optimization/Forecasting

引用本文复制引用

Lan-ting Zhou,Guan-lin Long,Can-can Hu,Kai Zhang..Reservoir water level prediction using combined CEEMDAN-FE and RUN-SVM-RBFNN machine learning algorithms[J].水科学与水工程,2025,18(2):177-186,10.

基金项目

This work was supported by the National Key R&D Program of China(Grant No.2022YFC3005401)and the National Natural Science Foundation of China(Grant No.52239009). （Grant No.2022YFC3005401）

水科学与水工程

ISSN：1674-2370

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