An Interpretable Deep Learning Approach Integrating PatchTST, Quantile Regression, and SHAP for Dam Displacement Interval Prediction

Accurate prediction of dam displacement is essential for structural safety and risk management. To comprehensively address the “accuracy–uncertainty–interpretability” trilemma in dam displacement prediction, this study proposes a deep learning framework that integrates Patch Time Series Transformer (PatchTST), Sand Cat Swarm Optimization (SCSO), Quantile Regression (QR), and SHapley Additive exPlanations (SHAP). The proposed framework first employs PatchTST to capture the nonlinear temporal dependencies between multiple monitoring factors and dam displacement, while SCSO is utilized to adaptively optimize key hyperparameters, enabling the construction of a high-precision point prediction model. On this basis, QR is introduced to model the distributional uncertainty of displacement responses and to generate confidence-based prediction intervals, facilitating the evaluation of displacement anomalies. Furthermore, SHAP is incorporated to quantify the marginal contribution of each input factor to the model outputs, thereby enhancing interpretability and aligning model behavior with physical domain knowledge. The framework is validated using multi-year monitoring data from a double-curvature arch dam located in Southwest China. Comparative experiments demonstrate that the proposed model outperforms five well-established machine learning methods and the traditional linear regression method in terms of point prediction accuracy, reliability of interval estimation, and false alarm rate, exhibiting strong generalization and robustness. The SHAP-based analysis further reveals that water pressure variations and seasonal temperature cycles are the dominant factors influencing radial displacement, consistent with known structural deformation mechanisms. These findings affirm the physical consistency and engineering applicability of the proposed framework, offering a deployable and trustworthy solution for intelligent dam health monitoring and uncertainty-aware forecasting in safety-critical infrastructures.