A self-attention-LSTM method for dam deformation prediction based on CEEMDAN optimization

Abstract

The structural deformation of a dam directly affects its lifespan and safety, making its accurate prediction crucial. Traditional prediction methods often overlook the nonlinearity and non-smoothness of deformation data. Moreover, the irregular intervals within the historical deformation data used for model training can reduce prediction accuracy. To address these issues, we propose a hybrid deep learning model that uses signal decomposition and reconstruction to enhance dam deformation prediction. This model employs a long short-term memory (LSTM) neural network optimized using a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The self-attention mechanism in the LSTM model effectively captures the temporal features of dam deformation, alleviating the difficulties generated by the irregular intervals within the historical data used in model training. Furthermore, considering the lag effect of influencing factors on dam deformation and the differences among various measurement points, we propose a CEEMDAN-based feature selection method. Using 13 years worth of data from the Shuibuya Dam, we evaluate the accuracy and effectiveness of the CEEMDAN-Self-attention-LSTM model using indicators, such as MAE, RMSE, MAPE, and R2, and compared it with existing models. The experimental results show that this model reduces prediction error by more than 53.62%.