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Reliable prediction of reservoir inflows upstream of a substation is a key factor in flood control forecasting for substations. Inflow prediction is a complex task due to the need to integrate the effects of climate and hydrological changes. In this paper, a deep learning method based on convolutional long- and short-term memory is developed to predict the discharge volume in real time. This real-time prediction not only contributes to the efficient operation of water resources but also improves the reliability of operation by effectively monitoring the daily changes in water discharge. By considering information such as precipitation, temperature, and soil moisture content from historical observation day data, the attentional long- and short-term memory network anomaly detection algorithm is used to predict flood control in the area where the substation is located. The results of experiments conducted on observation day data from the Danube River basin show that the proposed method reduces the error of each analyzed water level measurement station, and the experimental results for high water level periods confirm the superiority of the proposed method over the shallow model.
Liang et al. (Fri,) studied this question.