Reservoir level prediction and early warning method based on delay factor and PSO RNN Attention model

The invention relates to a reservoir level prediction and early warning method based on a delay factor and a PSO RNN Attention model. According to the method, a delay factor is calculated based on a numerical relationship between upper reservoir water levels, so that a model with a delayed water level characteristic is constructed, and the influence of an upstream water level (flood discharge quantity) on the reservoir water level is effectively input into the model. Based on an RNN (recurrent neural network) and Attention mechanism composite model, historical water level data, rainfall and flood discharge are used for training the model, and an RNN-Attention model hyper-parameter is optimized by using a PSO (Particle Swarm Optimization) algorithm. According to the model, reservoir and upstream rainwater condition data are fully utilized, future water level prediction is achieved, the prediction accuracy is high, and real-time monitoring and early warning of a dam can be achieved in combination with the Kafka