Articles | Volume 25, issue 11
Research article
18 Nov 2021
Research article |  | 18 Nov 2021

AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment

Yuxue Guo, Xinting Yu, Yue-Ping Xu, Hao Chen, Haiting Gu, and Jingkai Xie

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Cited articles

Adnan, R. M., Liang, Z., Heddam, S., Zounemat-Kermani, M., Kisi, O., and Li, B.: Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs, J. Hydrol., 586, 124371,, 2020. 
Ahmad, S. K. and Hossain, F.: A generic data-driven technique for forecasting of reservoir inflow: Application for hydropower maximization, Environ. Model. Softw., 119, 147–165,, 2019. 
Alexander, S., Yang, G., Addisu, G., and Block, P.: Forecast-informed reservoir operations to guide hydropower and agriculture allocations in the Blue Nile basin, Ethiopia, Int. J. Water Resour. Dev., 37, 208–233,, 2020. 
Ali, S. and Shahbaz, M.: Streamflow forecasting by modeling the rainfall–streamflow relationship using artificial neural networks, Model. Earth Syst. Environ., 6, 1645–1656,, 2020. 
Al-Sudani, Z. A., Salih, S. Q., and Yaseen, Z. M.: Development of multivariate adaptive regression spline integrated with differential evolution model for streamflow simulation, J. Hydrol., 573, 1–12,, 2019. 
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.