Prediction of Saturation Line of Tailing Dam based on 1DCNN-LSTM
Accurately predicting changes in the saturation line is crucial for the stability and safety of tailings dams.In order to fully explore the spatial characteristics and temporal information provided by saturation line data this paper proposes a combined approach using one-dimensional convolutional neural network(1DCNN)and long short-term memory neural network(LSTM)to predict saturation line.Taking the main dam of Fengshuigou tailing pond in Qidashan,Liaoning Province as an example,the model utilizes five major factors,including historical saturation line,reservoir water level,internal and external displacements of the dam,dry beach length,as input data to predict the saturation line positions for the next one day and three days.The 1DCNN-LSTM model is compared with classical LSTM and backpropagation neural network(BP)models.The results show that the Coefficient of Determination(R2)of the 1DCNN-LSTM infiltration line prediction is above 0.9,the absolute value of the mean error of the prediction error of the saturation line in the next one day is 0.004 m,the absolute value of the maximum error is 0.06 m,and the absolute value of the mean prediction error of the saturation line in the next three days is 0.003 m,and the absolute value of the maximum error is 0.065 m,which is better than that of the classical model.This provides a certain reference for the prediction of short-term saturation line.
NETL
NSTL
万方数据
