## 4 Experimental Results and Discussions
### Comparison with Other Frequently Used Classifiers  
To validate the accuracy and efficiency of the selected classifier, XGBoost was benchmarked against four other high-performance models commonly employed for classification tasks: AdaBoost, Random Forest (RF), LightGBM, and CatBoost. The comparative performance was evaluated based on the metrics of precision, recall, and F\({}_{1\text{-score}}\), as presented in Table 5, while the computational runtime for each classifier is detailed in Table 6. Figure 8  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data** & **Structure** & **Source** & **Precision** & **Recall** & **F\({}_{1\text{-score}}\)** \\ \hline Dataset 1 & Shield & Nanjing, line2 & 87.17 & 95.56 & 91.18 \\ Dataset 2 & Shield & Nanjing, line10 & 97.41 & 98.27 & 97.84 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The Recall, Precision, and F\({}_{1\text{-score}}\) using different datasets at optimal scales (%).  
Figure 6: Example results of leakage detection for Dataset 1.  
Figure 7: Intensity probability density for both datasets. (**a**) Dataset 1. (**b**) Dataset 2.  
provides a qualitative illustration of the leakage detection results produced by the different classifiers on Dataset 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{2}{c}{**Dataset 1**} & \multicolumn{2}{c}{**Dataset 2**} \\ \cline{2-5}  & **Training** & **Testing** & **Training** & **Testing** \\ \hline XGBoost & 9.43 & 0.03 & 0.14 & 0.001 \\ AdaBoost & 5308.25 & 0.58 & 6.93 & 0.02 \\ RF & 956.40 & 0.15 & 0.93 & 0.006 \\ LightGBM & 6.13 & 0.08 & 0.05 & 0.001 \\ CatBoost & 90.78 & 0.10 & 2.76 & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The execution time of different classifiers. Note: unit is minute(s).  
Figure 8: Example results of leakage detection from different classifiers presented in a 2D side view for Dataset 1.  
As shown in Table 4, XGBoost achieves the highest recall for leakage detection among all classifiers in both datasets, a result which corresponds with the visualizations in Figure 8. This indicates that XGBoost is particularly effective in identifying potential leakage areas. While it exhibits slightly lower precision compared to some classifiers, its superior recall culminates in the highest overall F1-score for both datasets. This demonstrates that XGBoost most effectively and reliably segments leakage from the tunnel lining among the models tested.  
The analysis of computational efficiency, presented in Table 5, reveals a more nuanced landscape. For inference, XGBoost demonstrates exceptional speed, with a testing time of only 0.03 min on Dataset 1 and a near-instantaneous 0.001 min on Dataset 2. In terms of training, while LightGBM exhibits the fastest performance on Dataset 1 (6.13 m), the training time for XGBoost (9.43 m) remains highly competitive and is orders of magnitude faster than traditional models like AdaBoost (5308.25 m) and Random Forest (956.40 m). This efficiency can be attributed to the optimized gradient boosting framework of XGBoost, which supports parallel and distributed computing, facilitating rapid model convergence. When considering both its state-of-the-art accuracy and its excellent computational performance, particularly its rapid inference speed, XGBoost emerges as the most balanced and practical choice for this application.