## 4 Discussion  
Comprehensive case studies and comparative experiments verify the effectiveness and superiority of the proposed method. The results demonstrate that the presented anomaly detection and data reconstruction approach (method 4) exhibits significant advantages in multiple aspects, including anomaly identification accuracy, data fluctuation control, error distribution stability, and data reconstruction performance. Based on the research findings, the following discussions are presented:  
1. Anomaly Detection Performance Analysis  
The proposed method can effectively detect abnormal data that significantly deviate from normal patterns in real time, with both false positive and false negative rates controlled within 10%, outperforming traditional methods. By combining \(m_{k}\) and \(J_{k}\) indicators, the method can not only distinguish between random error data, systematic drift data, and gross error data but also accurately identify the position and magnitude of anomalies. During data imputation, the MissForest algorithm can fill missing values following the operational patterns of osmotic pressure, avoiding the bias introduced by mean imputation methods.  
2. Data Reconstruction Performance Comparison  
Particle distribution: the improved particle filter significantly enhances particle concentration through an optimized resampling strategy, and its combination with MissForest further improves distribution stability. Data fluctuation control: the adaptive noise parameter adjustment in the improved particle filter effectively reduces reconstruction data fluctuation, while MissForest's preservation of nonlinear relationships between variables further decreases random errors in imputed data. Error distribution characteristics: method 4 shows the smallest and most concentrated error range, proving more effective for seepage pressure state estimation.  
3. Reconstruction Accuracy Validation  
Comprehensive evaluation using R\({}^{2}\), RMSE, and MAE metrics confirms method 4's superior reconstruction accuracy: the highest R\({}^{2}\) (0.861) indicates a stronger correlation between reconstructed and measured data. Lower RMSE (0.050) and MAE (0.052) demonstrate better error control. These results align well with the observed particle concentration and error distribution stability.  
Figure 11: Comparative distribution plot of evaluation parameters for experimental methods. (**a**) Size of R\({}^{2}\) parameter distribution; (**b**) size of RMSE parameter distribution; (**c**) size of MAE parameter distribution.  
Method Limitations  
Despite the synergistic effect of improved particle filter and MissForest in enhancing reconstruction quality, two limitations remain: MissForest's computational efficiency with high-dimensional data requires further optimization. Anomaly detection criteria rely on historical data statistics and may need dynamic adjustment in non-stationary environments. Future research could focus on improving online learning mechanisms to improve adaptability.  
Implications  
The methodology constructed in this study provides an improved approach for dam safety assessment and identification of abnormal seepage pressure measurements, addressing the limitations of traditional detection methods. It offers reliable data support for seepage pressure data reconstruction and state evaluation. This research contributes to advancing dam health monitoring and diagnostics, helping to prevent catastrophic dam failures and seepage-related safety incidents. Ultimately, it supports the stable development of society and safeguards public lives and property.