In order to improve the prediction accuracy of the dam deformation prediction model,LSTM was used as the base model to predict the dam deformation,convolutional layer of CNN network was added before the LSTM network layer,and the convolutional kernel in the convolutional layer portrayed the local patterns of the data to realize the deep mining of the data features so as to extract the multifactorial sequential spatial and temporal features of the dam deforma-tion.An attention mechanism layer was added after the LSTM network layer for distinguishing the importance of feature information and giving it different levels of attention to further optimize the network model.A dam prediction model based on CNN-LSTM-AM was constructed.Compared with the prediction results and residuals of the LSTM,CNN-LSTM,and LSTM-AM models in engineering examples,the CNN-LSTM-AM model has better prediction results and goodness-of-fit.EMSE,ERMSE,EMAE,and R2 were used as accuracy evaluation indexes to compare the prediction performance among models,indicating that the introduction of the attention mechanism can improve the model prediction performance,and confirming that the dam prediction model based on CNN-LSTM-AM has engineering application value.
维普
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