A Coupled Model for Dam Foundation Seepage Behavior Monitoring and Forecasting Based on Variational Mode Decomposition and Improved Temporal Convolutional Network

Abstract

Grasping the change behavior of dam foundation seepage pressure is of great significance for ensuring the safety of concrete dams. Because of the environmental complexity of the dam location, the prototypical seepage pressure data are easy to be contaminated by noise, which brings challenges to accurate prediction. Traditional denoising methods will lose the detailed characteristics of the objects, resulting in prediction models with limited flexibility and prediction accuracy. To address these problems, the prototypical data with noise are denoised using the variational mode decomposition (VMD)-wavelet packet denoising method. Then, an improved temporal convolutional network (ITCN) model is built for dam foundation seepage pressure data prediction. A hysteresis experiment is carried out to optimize the model structure by correlating the receptive field size of the ITCN model with the hysteresis of the dam foundation seepage pressure. Finally, the optimal ITCN dam foundation seepage pressure prediction model of each measurement point is obtained after the training. Three state-of-the-art methods in dam seepage monitoring are used as benchmark methods to compare the prediction performance of the proposed method. Four evaluation indicators are introduced to quantitatively evaluate and compare the prediction performance of the proposed method. The experimental results prove that the proposed method achieves high prediction accuracy flexibility. The indicator values of the ITCN model are only 50%–90% of those of LSTM and RNN models and 15%–40% of those of the stepwise regression model, and the values are all small.