A CNN-LSTM-attention based seepage pressure prediction method for Earth and rock dams

The objective of seepage pressure monitoring of earth and rock dams is to predict seepage pressure in order to avoid potential risks. However, existing models for predicting seepage pressure in earth and rock dams do not account for the numerous nonlinearities between seepage pressure and the factors that influence it. These models lack the accuracy and generalizability required for effective risk management. In order to address this issue, this paper puts forth a methodology for the prediction of seepage pressure in earth and rock dams. This methodology is based on the use of Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), and an attention mechanism. The method initially normalizes each influence factor and divides the dataset. Subsequently, it employs a Convolutional Neural Network (CNN) to extract features from the data. Long Short-Term Memory (LSTM) networks are particularly adept at handling non-smooth time series data, enabling the capture of the deep information embedded within seepage pressure data. Furthermore, the introduction of attention mechanisms allows for the extraction of key information, ultimately enhancing the prediction accuracy and stability. The analysis of engineering examples demonstrates that, in comparison with the single CNN-LSTM, LSTM, Transformer, and BP models, the MAE, MAPE, and RMSE of the proposed method in this paper at two measurement points are the smallest among the four models. The results demonstrate that, in comparison to the other three prediction models, the method exhibits superior prediction accuracy and enhanced stability, is capable of discerning the local variation characteristics of seepage pressure data, exhibits enhanced robustness, and provides a novel approach for accurate prediction and analysis of seepage pressure in earth and rock dams.