Articles | Volume 24, issue 5
https://doi.org/10.5194/hess-24-2343-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-2343-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 May 2020
Research article |
| 08 May 2020
| 08 May 2020
Multistep-ahead daily inflow forecasting using the ERA-Interim reanalysis data set based on gradient-boosting regression trees
Shengli Liao
Institute of Hydropower System and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, Dalian University of Technology, Dalian 116024, China
Zhanwei Liu
CORRESPONDING AUTHOR
Institute of Hydropower System and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, Dalian University of Technology, Dalian 116024, China
Benxi Liu
Institute of Hydropower System and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, Dalian University of Technology, Dalian 116024, China
Chuntian Cheng
Institute of Hydropower System and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, Dalian University of Technology, Dalian 116024, China
Xinfeng Jin
Institute of Hydropower System and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, Dalian University of Technology, Dalian 116024, China
Zhipeng Zhao
Institute of Hydropower System and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, Dalian University of Technology, Dalian 116024, China
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Saved (final revised paper)
Latest update: 06 Nov 2026
Short summary
Inflow forecasting plays an essential role in reservoir management and operation. To improve the accuracy of multistep-ahead daily inflow forecasting, the paper develops a new hybrid inflow forecast framework using ERA-Interim data. We find that the framework significantly enhances the accuracy of inflow forecasting at lead times of 4–10 d compared with widely used and mature methods. This research provides a reference for operational inflow forecasting in remote regions.
Inflow forecasting plays an essential role in reservoir management and operation. To improve the...
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