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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- Monthly streamflow forecasting for the Hunza River Basin using machine learning techniques S. Khan et al. 10.2166/wpt.2023.124
- Exploring the role of the long short‐term memory model in improving multi‐step ahead reservoir inflow forecasting X. Luo et al. 10.1111/jfr3.12854
- Regionalization of hydrological model parameters using gradient boosting machine Z. Song et al. 10.5194/hess-26-505-2022
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- A Hybrid Model of Ensemble Empirical Mode Decomposition and Sparrow Search Algorithm-Based Long Short-Term Memory Neural Networks for Monthly Runoff Forecasting B. Li et al. 10.3389/fenvs.2022.909682
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- Cascade hydropower station risk operation under the condition of inflow uncertainty K. Lei et al. 10.1016/j.energy.2021.122666
- Hydropower Operation Optimization Using Machine Learning: A Systematic Review J. Bernardes et al. 10.3390/ai3010006
- Streamflow Prediction Utilizing Deep Learning and Machine Learning Algorithms for Sustainable Water Supply Management S. Latif & A. Ahmed 10.1007/s11269-023-03499-9
- Climate-driven Model Based on Long Short-Term Memory and Bayesian Optimization for Multi-day-ahead Daily Streamflow Forecasting Y. Lian et al. 10.1007/s11269-021-03002-2
- Recyclable magnetic orange peel residues modified by anionic surfactant for basic blue 9 removal: Experimental study and machine learning modeling H. Khalili et al. 10.1016/j.mtcomm.2022.104222
- Enhancing robustness of monthly streamflow forecasting model using gated recurrent unit based on improved grey wolf optimizer X. Zhao et al. 10.1016/j.jhydrol.2021.126607
- Comparative Study of Coupling Models of Feature Selection Methods and Machine Learning Techniques for Predicting Monthly Reservoir Inflow J. Weekaew et al. 10.3390/w14244029
- An attention-based LSTM model for long-term runoff forecasting and factor recognition D. Han et al. 10.1088/1748-9326/acaedd
- Evaluation of seventeen satellite-, reanalysis-, and gauge-based precipitation products for drought monitoring across mainland China L. Wei et al. 10.1016/j.atmosres.2021.105813
- Application of random forest based on semi-automatic parameter adjustment for optimization of anti-breast cancer drugs J. Liu et al. 10.3389/fonc.2022.956705
- Multi-source precipitation products assessment on drought monitoring across global major river basins X. Wu et al. 10.1016/j.atmosres.2023.106982
- Predicting Inflow Rate of the Soyang River Dam Using Deep Learning Techniques S. Lee & J. Kim 10.3390/w13172447
- Detecting Associations Based on the Multi-Variable Maximum Information Coefficient T. Gu et al. 10.1109/ACCESS.2021.3070925
- Recyclable Magnetic Orange Peel Residues Modified by Anionic Surfactant for Basic Blue 9 Removal: Experimental Study and Machine Learning Modeling H. Khalili et al. 10.2139/ssrn.4148165
- Balancing-oriented hydropower operation makes the clean energy transition more affordable and simultaneously boosts water security Z. Liu & X. He 10.1038/s44221-023-00126-0
- Machine learning aided design of perovskite oxide materials for photocatalytic water splitting Q. Tao et al. 10.1016/j.jechem.2021.01.035
- Cause-driven Streamflow Forecasting Framework Based on Linear Correlation Reconstruction and Long Short-term Memory Y. Lian et al. 10.1007/s11269-022-03097-1
- Runoff Forecast Model Based on an EEMD-ANN and Meteorological Factors Using a Multicore Parallel Algorithm S. Liao et al. 10.1007/s11269-023-03442-y
- Advancing subseasonal reservoir inflow forecasts using an explainable machine learning method M. Fan et al. 10.1016/j.ejrh.2023.101584
- Monthly Runoff Forecasting Using Variational Mode Decomposition Coupled with Gray Wolf Optimizer-Based Long Short-term Memory Neural Networks B. Li et al. 10.1007/s11269-022-03133-0
- Day-ahead inflow forecasting using causal empirical decomposition M. Yousefi et al. 10.1016/j.jhydrol.2022.128265
- Machine learning approaches coupled with variational mode decomposition: a novel method for forecasting monthly reservoir inflows F. Ahmadi et al. 10.1007/s12145-023-01186-2
Latest update: 19 Apr 2024
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...
