Spatial-Correlation-Aware Distribution-Adaptive Interval Prediction for Dam Monitoring via Two-Level Uncertainty Fusion

Long-term dam safety assessment relies on continuous monitoring data from multiple spatially distributed measurement points. However, monitoring data are affected by measurement noise, environmental disturbances, and model errors, while the spatial correlation among monitoring points is often ignored, leading to biased uncertainty estimation and unreliable prediction intervals. To address these issues, this study proposes a spatial-correlation-aware distribution-adaptive interval prediction method for dam monitoring via two-level uncertainty fusion. Measurement random noise is first separated using a filtering strategy, and its uncertainty is updated by incorporating the spatial correlation among multiple monitoring points. A regression model is then established based on the filtered monitoring data, and model prediction uncertainty is quantified from the residual distribution. The two uncertainty components are further integrated to construct distribution-adaptive asymmetric prediction intervals. The proposed method is verified using deformation monitoring data from the PB high core rockfill dam. The results show that the proposed method achieves an average PICP of 0.9898 on the training set, close to the target coverage level of 0.99, while reducing the average NMPIW by 16.9% and 13.4% compared with the symmetric interval method and the traditional 3σ method, respectively. On the validation set, the average NMPIW is further reduced by 18.4% and 26.5%, demonstrating that the proposed method can provide more compact and informative prediction intervals for refined dam safety monitoring.