Deformation is a critical indicator of structural integrity, and monitoring deformation is essential for ensuring the long-term safety of dams. However, characterizing the spatial correlations among dam deformation sequences and the similarity between displacements at various measurement points poses significant challenges when using single-point measurement models. Considering the limitations inherent in conventional models for processing spatiotemporal data, this paper introduces a novel model for predicting and imputing multi-point displacement monitoring data from earth-rock dams. The model integrates a convolutional neural network (CNN) with a bidirectional long short-term memory neural network (BiLSTM) while also incorporating an attention mechanism (AM). The CNN captures the spatial features of the displacement data, while the BiLSTM extracts temporal features. The AM assigns varying weights to input features, thereby enhancing the predictive accuracy of the model. The proposed model was experimentally validated, demonstrating its robust capabilities in data prediction and the imputation of missing data. The model provides a new strategy for forecasting dam deformation and addressing issues related to incomplete data.

With the increasing service duration of dams, the analysis of seepage pressure monitoring data plays a crucial role in ensuring the safety of seepage behavior. However, seepage pressure monitoring systems are often subject to environmental disturbances, sensor failures, and other interfering factors, leading to anomalous measurements during data acquisition. To objectively reflect the true operational state of dams and address the limitations of conventional detection and identification methods—such as low efficiency, high subjectivity in evaluation, and ineffective recognition of multi-category outliers—this study constructed an online detection, identification, and correction method for multi-category anomalous values. Specifically, an enhanced particle filter incorporating a Bernoulli probability model is constructed to characterize multi-category outliers in seepage pressure monitoring data, building upon the traditional particle filter framework. Following online detection and identification, the MissForest imputation method is employed to rectify the anomalous values. In the case study, both the false detection rate and missed detection rate ranged between 0% and 10%. Comparative experiments with three alternative methods revealed significant differences in data reconstruction performance, with the proposed method achieving the highest R² score (0.861) and the lowest RMSE (0.050) and MAE (0.052). The results demonstrate that the proposed method effectively identifies outliers, achieves superior reconstruction of seepage pressure data, and minimizes errors. Furthermore, this research provides a novel approach for detecting anomalous seepage pressure measurements and evaluating dam safety conditions.