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.
Research article
| 
18 Nov 2021
Research article |
| 18 Nov 2021
| 18 Nov 2021
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 novel reservoir dispatching rules extraction framework based on hybrid embedding informer S. Liu et al. https://doi.org/10.1016/j.jhydrol.2024.132047
- Development of a Multi-objective Optimal Operation Model of a Dam using Meteorological Ensemble Forecasts for Flood Control M. Tanhapour et al. https://doi.org/10.1007/s11269-024-04089-z
- Advances in land surface forecasting: a comparison of LSTM, gradient boosting, and feed-forward neural networks as prognostic state emulators in a case study with ecLand M. Wesselkamp et al. https://doi.org/10.5194/gmd-18-921-2025
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- A machine learning-based water supply forecasting model to quantify the impact of snow water equivalent on seasonal streamflow variability over the western U.S H. Yue et al. https://doi.org/10.1016/j.jhydrol.2025.133465
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- Multi-Step-Ahead Monthly Streamflow Forecasting Using Convolutional Neural Networks X. Shu et al. https://doi.org/10.1007/s11269-022-03165-6
- Advancing Machine Learning-Based Streamflow Prediction Through Event Greedy Selection, Asymmetric Loss Function, and Rainfall Forecasting Uncertainty S. Tofighi et al. https://doi.org/10.3390/app152111656
- Towards smart water monitoring: IoT-driven LSTM modelling for multi-step-ahead forecasting of river salinity dynamics S. Karahan et al. https://doi.org/10.1016/j.jenvman.2025.127437
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- Comparison of strategies for multistep-ahead lake water level forecasting using deep learning models G. Li et al. https://doi.org/10.1016/j.jclepro.2024.141228
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- Multi-objective optimization of seasonal emergency storage in cascade reservoirs for enhanced drought resilience and hydropower synergy S. Li et al. https://doi.org/10.2166/wcc.2025.816
- Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method Z. Cui et al. https://doi.org/10.5194/hess-28-2809-2024
- Integrated Runoff Forecasting Model with Mode Decomposition and Metaheuristic-optimized Bidirectional Gated Recurrent Unit Z. Feng et al. https://doi.org/10.1007/s11269-025-04090-0
- State-of-the-art review: Operation of multi-purpose reservoirs during flood season S. Jain et al. https://doi.org/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. https://doi.org/10.1016/j.geoen.2024.212921
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- Effects of Multi-Step-Ahead Prediction Strategies on LSTM-Based Runoff Prediction J. Jiang et al. https://doi.org/10.1007/s11269-025-04332-1
- Monitoring the extreme flood events in the Yangtze River basin based on GRACE and GRACE-FO satellite data J. Xie et al. https://doi.org/10.5194/hess-26-5933-2022
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- Predictive simulation and optimal allocation of surface water resources in reservoir basins under climate change Q. Rong et al. https://doi.org/10.1016/j.iswcr.2023.08.003
- Integrated machine learning models for enhancing tropical rainfall prediction using NASA POWER meteorological data A. Saleh et al. https://doi.org/10.2166/wcc.2024.719
- Multi-reservoirs joint flood control scheduling using a two-layer hedging robust optimization method under uncertain inflows X. Yu et al. https://doi.org/10.1016/j.ejrh.2025.102244
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Saved (final revised paper)
Latest update: 22 Jul 2026
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...
