A Displacement Monitoring Model for High-Arch Dams Based on SHAP-Driven Ensemble Learning Optimized by the Gray Wolf Algorithm

Displacement monitoring data is essential for assessing the structural safety of high-arch dams. Existing models, predominantly based on single-model architectures, often lack the ability to effectively integrate multiple algorithms, leading to limited predictive performance and poor interpretability. This study proposes an ensemble learning framework for dam displacement prediction, combining Hydraulic–Seasonal–Temporal model (HST), Random Forest (RF), and Bidirectional Gated Recurrent Unit (BiGRU) models as base learners. A stacking strategy is employed to enhance predictive accuracy, and the Grey Wolf Optimizer (GWO) is used for hyperparameter optimization. To improve model transparency, the Shapley Additive Explanations (SHAP) algorithm is applied for interpretability analysis. Extensive experiments demonstrate that the proposed ensemble model outperforms individual models, achieving a Root Mean Squared Error (RMSE) of 0.2241 and a Coefficient of Determination (R2) of 0.9993 on the test set. The SHAP analysis further elucidates the contribution of key variables, providing valuable insights into the displacement prediction process and offering a robust technical foundation for arch dam safety monitoring and early risk warning.