A Novel Long Short-Term Memory Seq2Seq Model with Chaos-Based Optimization and Attention Mechanism for Enhanced Dam Deformation Prediction

The accurate prediction of dam deformation is essential for ensuring safe and efficient dam operation and risk management. However, the nonlinear relationships between deformation and time-varying environmental factors pose significant challenges, often limiting the accuracy of conventional and deep learning models. To address these issues, this study aimed to improve the predictive accuracy and interpretability in dam deformation modeling by proposing a novel LSTM seq2seq model that integrates a chaos-based arithmetic optimization algorithm (AOA) and an attention mechanism. The AOA optimizes the model's learnable parameters by utilizing the distribution patterns of four mathematical operators, further enhanced by logistic and cubic mappings, to avoid local optima. The attention mechanism, placed between the encoder and decoder networks, dynamically quantifies the impact of influencing factors on deformation, enabling the model to focus on the most relevant information. This approach was applied to an earth-rock dam, achieving superior predictive performance with RMSE, MAE, and MAPE values of 0.695 mm, 0.301 mm, and 0.156%, respectively, outperforming conventional machine learning and deep learning models. The attention weights provide insights into the contributions of each factor, enhancing interpretability. This model holds potential for real-time deformation monitoring and predictive maintenance, contributing to the safety and resilience of dam infrastructure.