## 3 Case Analysis
### Seepage Pressure Data Acquisition and Outlier Processing  
The seepage pressure data were obtained from the seepage monitoring historical records of the Wuluwati Hydro Project's operational data monitoring platform. Considering the varying influencing factors and variation patterns of dam foundation piezometric  
Figure 2: Flowchart of the multi-type anomaly reconstruction model for osmotic pressure data. monitoring points, representative measurement points were selected, including P1 (grout curtain anti-seepage line), P3 (cushion layer area), P4 (upstream section), and P9 (downstream section). To ensure data completeness of environmental variables, the dataset spanning from 1 January 2015 to 5 April 2019 (totaling 1511 days of monitoring records) incorporating both environmental parameters and seepage monitoring data was selected for analysis. Figure 3 illustrates the layout of the osmotic pressure monitoring points, while Figure 4 presents the corresponding time-series data curves for each measurement point. Initial implementation of the Bernoulli filtering algorithm for particle state tracking at monitoring point P1 provides an intuitive evaluation of the enhanced particle filter method's potential in measurement point identification. From Figure 5, it can be observed that the improved particle filter achieves satisfactory performance in particle state tracking. Although certain locations exhibit relatively large prediction errors, the algorithm successfully tracks the initial data overall. Figure 4: Process curve diagram of the corresponding measurement points. Figure 3: Layout diagram of seepage pressure monitoring at the measuring point. Figure 5: Particle state tracking image of Bernoulli filtering algorithm. The fitting results reveal fluctuations in filtering accuracy, which primarily reflect the dynamic variations in particle states in the time series. Over time, the distribution of particles in the state space is influenced by system noise and observational factors, leading to positional fluctuations. Despite these error variations, the update mechanism of the particle filter algorithm progressively corrects state estimations, reduces deviations, and ultimately achieves state convergence. As demonstrated in Figure 6a by the comparison between true values and filtered values, the improved particle filter method successfully identifies and tracks the true values despite detection fluctuations. Even in the presence of anomalous data, the filtered values gradually converge toward the actual measurements. Figure 6b further validates this conclusion through error analysis curves. The detection and identification errors based on the Bernoulli filtering algorithm stabilize and remain at a low level as the number of iterations increases, demonstrating the algorithm's capability to effectively correct state estimations and reduce deviations during prolonged processing. Figure 5: Particle state tracking image of Bernoulli filtering algorithm. In order to verify the effectiveness of the proposed method, considering the problem of filter graph reduction when there are too many time steps, the seepage pressure data time steps of multiple measurement points P1, P3, P4, and P9 at a certain time of 200 days (from 9 April 2015 to 25 October 2015) were selected for example analysis. Figure 7 shows the online detection and identification results of the measurement point parameters \(m_{k}\) and \(j_{k}\), where \(m_{k}\) is the particle filter identification value and \(j_{k}\) is the particle filter detection value. The measurement point P1 is combined with parameters \(m_{k}\) and \(j_{k}\). It can be seen that the time step is [30,50], [60,75], and [170,200], which are inconsistent with the true value, resulting in coarse error; at the same time, random error is generated between measurement points [20,25] and [95,125]. Similarly, measurement point P3 is combined with parameters \(m_{k}\) and \(j_{k}\).