Improving Sub-daily Runoff Forecast Based on the Multi-objective Optimized Extreme Learning Machine for Reservoir Operation

Wenhao Jia, Mufeng Chen (), Hongyi Yao, Yixu Wang, Sen Wang and Xiaokuan Ni
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Wenhao Jia: Key Laboratory of the Pearl River Estuary Regulation and Protection of Ministry of Water Resources
Mufeng Chen: Helmholtz Centre for Environmental Research – UFZ
Hongyi Yao: The University of Hong Kong
Yixu Wang: Pearl River Water Resources Commission of Ministry of Water Resources
Sen Wang: Key Laboratory of the Pearl River Estuary Regulation and Protection of Ministry of Water Resources
Xiaokuan Ni: Pudong New Area Emergency Management Bureau

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 15, No 18, 6173-6189

Abstract: Abstract Data-driven models have shown remarkable achievements in runoff prediction, but their simulation results can be overly homogenized due to the distillation of all simulation aspects into the loss function. This can make the models unreliable for predicting extreme events, leading to issues subsequent reservoir operations. This paper proposes a novel data-driven hybrid machine-learning model called the multi-objective optimized extreme learning machine (MOELM) to provide an accurate runoff forecasting for reservoirs. The objective is to minimize simulation error, with an additional focus on flood deviation. The results show that: (1) MOELM can improve flood events prediction, reducing the root mean square error (RMSE) for flood series by 5.27% without increasing the overall prediction error at Longtan reservoir. Compared to hydrological models, MOELM can reduce operational risk with lower reservoir maximum outflow and water level during typical flood events, and it can potentially increase hydropower generation at Longtan reservoir by 130 million kW·h. (2) MOELM can be transferred to other cross-sections with excellent performances, demonstrating hydrological transferability from fluctuation to flatness in regime. (3) Partial mutual information is introduced for input variable selection, with discharge at lag times t-4, t-1, t-8, and t-2 being vital to the prediction model. Our model is practical, requiring no additional input, fitting the hydrological runoff holistically, and capable of providing accurate flood forecasts.

Keywords: Sub-daily Runoff Forecast; Flood Events; Multi-objective Optimization; Extreme Learning Machine; Transfer Learning; Partial Mutual Information (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11269-024-03953-2

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