Predicting the Deformation of a Concrete Dam Using an Integration of Long Short-Term Memory (LSTM) Networks and Kolmogorov–Arnold Networks (KANs) with a Dual-Stage Attention Mechanism

An accurate prediction model for dam deformation is crucial for ensuring the safety and operational integrity of dam structures. This study introduces a hybrid modeling approach that integrates long short-term memory (LSTM) networks with Kolmogorov–Arnold networks (KANs). Additionally, the model incorporates a dual-stage attention mechanism (DA) that includes both factor and temporal attention components, enhancing the model’s precision and interpretability. The effectiveness of the DA-LSTM-KAN model was validated through a case study involving a concrete gravity dam. A comparative analysis with traditional models, including multiple linear regression and various LSTM variants, demonstrated that the DA-LSTM-KAN model significantly outperformed these alternatives in predicting dam deformation. An interpretability analysis further revealed that the seasonal and hydrostatic components contributed significantly to the horizontal displacement, while the irreversible component had the least impact. This importance ranking was qualitatively consistent with the results obtained from the Shapley Additive Explanations (SHAP) method and the relative weight method. The enhancement of the model’s predictive and explanatory capabilities underscores the hybrid model’s utility in providing detailed and actionable intelligence for dam safety monitoring.