Reservoir Flood Prediction Service Based on Seq2seq Model

Reliable reservoir flood prediction model is an indispensable part to improve the digital construction of small and medium-sized reservoirs. Flood flow, as one of the important bases for measuring flood prediction, its prediction accuracy directly affects the effectiveness of reservoir flood prevention and control. The traditional flood flow prediction model often can’t get real-time and comprehensive data in time, which leads to the idealized prediction effect can’t be realized, so for this situation, this paper proposes a reservoir flood flow prediction model based on Seq2seq model. Based on the Seq2seq model, this model not only integrates the LSTM network as a recurrent processing unit to facilitate the capture of temporal information and long-term dependencies in the data, but also uses the attention mechanism to reduce the loss of information in the transmission process and improve the model’s generalization ability. At the same time, the model is compared with a variety of flood prediction machine learning models, and also compared with the prediction model based on the traditional flood flow statistics model, and then this paper further verifies the model’s improvement in prediction accuracy by comprehensively comparing the results of five evaluation indexes. Finally, the modeling service has been applied in the real environment, which again proves the effectiveness of the present service.