Reservoir downstream water level prediction method based on deep learning model

The invention discloses a reservoir downstream water level prediction method based on a deep learning model. The method comprises the following steps: screening downstream water level related factorsby adopting a maximum information coefficient; optimizing by adopting a genetic algorithm on the basis of correlation analysis to obtain an optimal feature combination among single correlation factors; constructing a deep learning model (CNNLSTM) based on a convolutional neural network and a long-term and short-term memory network by taking the optimal feature combination of the downstream water level related factors as input; and training a CNNLSTM model weight variable by adopting an Adam gradient optimization algorithm, and taking the trained CNNLSTM as a reservoir downstream water level prediction model. According to the prediction method, the correlation factors of the downstream water level are finely considered, the feature combination of the correlation factors is optimized, the deep learning prediction mo