Prediction of dam horizontal displacement based on CNN-LSTM and attention mechanism

Abstract

Aiming at the characteristics of dam safety monitoring data sequence with few samples, short sequence and nonlinearity, a dam horizontal displacement prediction method based on attention mechanism, convolutional neural network (CNN) and long short-term memory (LSTM) is proposed. This method can reduce the loss of historical information and improve the prediction accuracy. First, the missing values are supplemented by linear interpolation to improve the integrity of the data. Then the abstract feature data extracted by CNN is mapped to the predicted value of LSTM, and then optimized through attention mechanism. Finally, the model is trained and verified with the monitoring data of a concrete gravity dam in Chongqing as a sample. Experimental results show that the root mean square error (RMSE), mean absolute percentage error (MAPE) and fit (R2) of the CNN-LSTM hybrid model based on attention mechanism are 0.3882, 0.7121% and 0.9543, respectively. The prediction accuracy of the new model is better than the CNN-LSTM model and the LSTM neural network model.