###### Abstract  
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 R\({}^{2}\) 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. s 2025 15 May 20  
prevent misjudgments of dam safety conditions and erroneous data interpretations [10]. Effective anomaly detection and precise imputation of missing data are of significant practical importance for data preprocessing, subsequent monitoring analysis, and early warning predictions. Currently, extensive research has been conducted on anomaly detection and processing [11], primarily focusing on data dimensions, anomaly types, or methodological characteristics [12]. Existing time-series anomaly detection methods can be categorized into statistical approaches, traditional machine learning, and deep learning techniques [13; 14; 15; 16; 17]. Statistical anomaly detection methods typically identify anomalies by analyzing the probability distribution of time-series data. For instance, [PERSON] et al. [18] addressed misjudgments in anomaly detection by proposing an online robust identification and early warning model combining robust statistics with confidence intervals. [PERSON] et al. [19] developed an unsupervised interpretable univariate anomaly detection method based on quantiles and skewness coefficients. [PERSON] et al. [20] introduced a sliding-window anomaly detection approach by extracting the slope radius of confidence intervals from subsequences. Traditional machine learning methods commonly include distance-based, density-based, clustering-based, tree-based, classification-based, and ensemble learning approaches. For example, [PERSON] et al. [21] addressed the problem of distributed density-based outlier detection for large-scale data by proposing a distributed LOF calculation method to parallelize density-based outlier detection, with simulation experiments verifying the efficiency and effectiveness of the approach. [PERSON] et al. [22] tackled the challenge of unknown anomalies in database outliers by introducing a novel outlier clustering detection algorithm called ROCF, which can automatically compute the anomaly rate of a database and effectively detect both individual outliers and outlier clusters. [PERSON] et al.