## 3 Case Analysis
### Seepage Pressure Data Acquisition and Outlier Processing
It can be seen that the time step is inconsistent with the true value at [40,50], [50,100], [100,150], and [190,200], resulting in coarse error; random error is generated between measurement points [20,30] and [170,190]. Measurement point P4 is combined with parameters \(m_{k}\) and \(j_{k}\). It can be seen that time steps [50,85], [115,125], [150,175], and [185,200] are inconsistent with the true values, resulting in coarse errors; random errors occur between measurement points [15,30] and [125,150]. Measurement point P9 combines parameters \(m_{k}\) and \(j_{k}\). It can be seen that the time steps are [50,75], [120,145], and [175,185], which are inconsistent with the real value, resulting in coarse error; the measurement points between [15,30], [75,105], [150,175], and [190,200] produce random error. Figure 7: _Cont._  
To quantitatively assess the outlier detection performance of the proposed method, two evaluation metrics were introduced: false negatives (FN) and false positives (FP). A false negative occurs when abnormal monitoring data are incorrectly identified as normal data, while a false positive refers to normal monitoring data being mistakenly classified as abnormal data. Details are provided in Table 1.  
The monitoring data from points P1, P3, P4, and P9 were subsequently corrected using the following procedure: Based on the historical seepage monitoring operational data of the hydraulic complex, the improved particle filter algorithm was first employed for real-time detection and identification of multi-type outliers. Following the removal of anomalous \begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Osmotic Pressure** \\ **Measurement** \\ **Point** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Missed** \\ **Detections** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **False** \\ **Pales** \\ **Detections** \\ \end{tabular} &
\begin{tabular}{c} **False** \\ **Loss (\%)** \\ **Rate (\%)** \\ \end{tabular} \\ \hline Point 1 & 3 & 9 & 1.5 & 4.5 \\ Point 3 & 7 & 15 & 3.5 & 7.5 \\ Point 4 & 4 & 13 & 2.0 & 6.5 \\ Point 9 & 2 & 8 & 1.0 & 4.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation of detection and identification level of abnormal values. Figure 7: Online detection and identification diagram of measurement points. (**a**) parameter \(m_{k}\) value at monitoring point P1: (**b**) parameter \(m_{k}\) value at monitoring point P3: (**c**) parameter \(J  
data points, the resulting missing values were then reconstructed and corrected using the MissForest iterative imputation method. According to Figure 8, the measurement point P1 time step is in [30,50], and the coarse error of [60,75] and [170,200] is drift, and the measurement point is in random error in [20,25] and [95,125] and fluctuates up and down around the true value. Similarly, the P3 time step is in [40,50], [100,150], and [190,200]; coarse error is drift; in [50,100], coarse error is deviation; the measurement point is random error in [20,30] and [170,190], and it fluctuates around the true value. The P4 time step is drift in [115,125] and [185,200] and deviation in [50,85] and [150,175]; the measurement point is random error in [15,30] and [125,150] and can fluctuate around the true value. The P9 time step is drift in [50,75] and [120,145] coarse error and deviation in [175,185]: the measurement point is random error in [15,30], [75,105], [150,175], and [190,200], fluctuating up and down around the true value. Figure 8: Reconstruction of measurement point data. (a) Anomaly correction of monitoring point P1: (b) anomaly correction of monitoring point P3: (c) anomaly correction of monitoring point P4: (d) anomaly correction of monitoring point P9.