Articles | Volume 25, issue 11
https://doi.org/10.5194/hess-25-5951-2021
© Author(s) 2021. 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-25-5951-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment
Yuxue Guo
Institute of Hydrology and Water Resources, Civil Engineering and
Architecture, Zhejiang University, Hangzhou, 310058, China
Xinting Yu
Institute of Hydrology and Water Resources, Civil Engineering and
Architecture, Zhejiang University, Hangzhou, 310058, China
Institute of Hydrology and Water Resources, Civil Engineering and
Architecture, Zhejiang University, Hangzhou, 310058, China
Institute of Hydrology and Water Resources, Civil Engineering and
Architecture, Zhejiang University, Hangzhou, 310058, China
Haiting Gu
Institute of Hydrology and Water Resources, Civil Engineering and
Architecture, Zhejiang University, Hangzhou, 310058, China
Jingkai Xie
Institute of Hydrology and Water Resources, Civil Engineering and
Architecture, Zhejiang University, Hangzhou, 310058, China
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- A dynamic classification-based long short-term memory network model for daily streamflow forecasting in different climate regions H. Chu et al. 10.1016/j.ecolind.2023.110092
- Monthly streamflow prediction using hybrid extreme learning machine optimized by bat algorithm: a case study of Cheliff watershed, Algeria S. Difi et al. 10.1080/02626667.2022.2149334
- Improving long-term streamflow prediction in a poorly gauged basin using geo-spatiotemporal mesoscale data and attention-based deep learning: A comparative study F. Ghobadi & D. Kang 10.1016/j.jhydrol.2022.128608
- Monthly Runoff Prediction for Xijiang River via Gated Recurrent Unit, Discrete Wavelet Transform, and Variational Modal Decomposition Y. Yang et al. 10.3390/w16111552
- Enhancing hydrological modeling with transformers: a case study for 24-h streamflow prediction B. Demiray et al. 10.2166/wst.2024.110
- A spatiotemporal graph convolution-based model for daily runoff prediction in a river network with non-Euclidean topological structure L. Deng et al. 10.1007/s00477-022-02352-6
- Multi-objective robust optimization of reservoir operation for real-time flood control under forecasting uncertainty X. Yu et al. 10.1016/j.jhydrol.2023.129421
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- Multi-objective operation of cascade reservoirs based on short-term ensemble streamflow prediction S. He et al. 10.1016/j.jhydrol.2022.127936
- Integrated machine learning models for enhancing tropical rainfall prediction using NASA POWER meteorological data A. Saleh et al. 10.2166/wcc.2024.719
- Development and Application of Reservoir Operation Method Based on Pre-Release Index for Control of Exceedance Floods C. Huang et al. 10.3390/w16223229
- Multi-Step-Ahead Monthly Streamflow Forecasting Using Convolutional Neural Networks X. Shu et al. 10.1007/s11269-022-03165-6
- A parallel approximate evaluation-based model for multi-objective operation optimization of reservoir group D. Liu et al. 10.1016/j.swevo.2023.101288
- Comparison of strategies for multistep-ahead lake water level forecasting using deep learning models G. Li et al. 10.1016/j.jclepro.2024.141228
- Optimization of Reservoir Level Scheduling Based on InSAR-LSTM Deformation Prediction Model for Rockfill Dams Z. Fang et al. 10.3390/w15193384
- A state-of-the-art review of long short-term memory models with applications in hydrology and water resources Z. Feng et al. 10.1016/j.asoc.2024.112352
- Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method Z. Cui et al. 10.5194/hess-28-2809-2024
- State-of-the-art review: Operation of multi-purpose reservoirs during flood season S. Jain et al. 10.1016/j.jhydrol.2023.129165
- Harvesting Earth's heat: A deep learning Odyssey for reservoir characterization and sustainable geothermal energy management J. Ullah et al. 10.1016/j.geoen.2024.212921
2 citations as recorded by crossref.
Latest update: 13 Dec 2024
Short summary
We developed an AI-based management methodology to assess forecast quality and forecast-informed reservoir operation performance together due to uncertain inflow forecasts. Results showed that higher forecast performance could lead to improved reservoir operation, while uncertain forecasts were more valuable than deterministic forecasts. Moreover, the relationship between the forecast horizon and reservoir operation was complex and depended on operating configurations and performance measures.
We developed an AI-based management methodology to assess forecast quality and forecast-informed...