Preprints
https://doi.org/10.5194/hess-2020-617
https://doi.org/10.5194/hess-2020-617

  16 Dec 2020

16 Dec 2020

Review status: a revised version of this preprint is currently under review for the journal HESS.

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

Yuxue Guo, Yue-Ping Xu, Xinting Yu, Hao Chen, Haiting Gu, and Jingkai Xie Yuxue Guo et al.
  • Institute of Hydrology and Water Resources, Civil Engineering and Architecture, Zhejiang University, Hangzhou, 310058, China

Abstract. Streamflow forecasts are traditionally effective in mitigating water scarcity and flood defense. This study developed an Artificial Intelligence (AI)-based management methodology that integrated multi-step streamflow forecasts and multi-objective reservoir operation optimization for water resource allocation. Following the methodology, we aimed to assess forecast quality and forecast-informed reservoir operations performance together due to the influence of inflow forecast uncertainty. Varying combinations of climate and hydrological variables were inputs into three AI-based models, namely Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Least Squares Support Vector Machine (LSSVM), to forecast short-term streamflow. Based on three deterministic forecasts, the stochastic inflow scenarios were further developed using Bayesian Model Averaging (BMA) for quantifying uncertainty. The forecasting scheme was further coupled with a multi-reservoir optimization model, and the multi-objective programming was solved using the parameterized Multi-Objective Robust Decision Making (MORDM) approach. The AI-based management framework was applied and demonstrated over a multi-reservoir system (25 reservoirs) in the Zhoushan Islands, China. Three main conclusions were drawn from this study: 1) GRU and LSTM performed equally well on streamflow forecasts, and GRU might be the preferred method over LSTM, given that it had simpler structures and less modeling time; 2) Higher forecast performance could lead to improved reservoir operation, while uncertain forecasts were more valuable than deterministic forecasts, regarding two performance metrics, i.e., water supply reliability and operating costs; 3) The relationship between forecast horizon and reservoir operation was complex and depended on the operating configurations (forecast quality and uncertainty) and performance measures. This study reinforces the potential of an AI-based stochastic streamflow forecasting scheme to seek robust strategies under uncertainty.

Yuxue Guo et al.

 
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Yuxue Guo et al.

Yuxue Guo et al.

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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 forecast horizon and reservoir operation was complex and depended on operating configurations and performance measures.