Multi-step prediction of water level in Wudongde Reservoir based on LSTM-Informer model
Yaobin DUAN , Deng LIU , Hanlin MAN , Xiao CHEN , Hang LUO , Ping CHEN , Yifan HU , Fei YAO , Pei GAO
Water Resources and Hydropower Engineering ›› 2025, Vol. 56 ›› Issue (S1) : 1 -5.
In order to solve the problem of short foresight period of deep learning algorithms in reservoir water level prediction, the LSTM-Informer reservoir water level prediction model is constructed with Wudongde Reservoir as an example, which predicts the reservoir water level in the future for 6, 12, 24, 48, and 96 steps, and compares the prediction result with those of the LSTM and Informer models. The results show that when the prediction step size is not more than 12, all three models can simulate the reservoir level well and the performance difference is not obvious, when the prediction step size is more than 12, the performance of the three models is LSTM-Informer>Informer>LSTM, and the RMSE and MAE of the LSTM-Informer model at 96 steps are 0.147 and 0.120 respectively, and the RMSE of the LSTM-Informer model is 25%, 46% and 62% lower than LSTM, and the MAE is 23%, 40% and 47% lower than LSTM, respectively. The combined model LSTM-Informer can solve the long time series reservoir level prediction problem better.
LSTM-Informer model / Wudongde Reservoir / water level prediction
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