## 4 Field Evaluation of Risk Model  
Incorporating the risk model established herein, this study developed a QGIS-based mapping system as illustrated in Figure 6. Figure 6a shows the railway networks of the Seoul area in the hazard map system and Figure 6b shows the variation of risk levels of tested locations boxed in Figure 6a. This system enables users to view the distribution of risk level along with inventory data that includes basic geotechnical boring data, water supply and wastewater utility's age, cover depth, length, etc., if available. The geological risk model was developed based on regression analysis taking into account the ground water level, alluvial layer thickness, and standard penetration test (SPT) data as follows. \[\text{Geological risk}=\ \frac{\exp(-0.441\times G.T-0.505\times T+0.183\times SPT _{N}+4.471)}{1+\exp(-0.441\times G.T-0.505\times T+0.183\times SPT_{N}+4.471)} \tag{6}\] \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Wastewater Supply Risk Prediction**} & \multirow{2}{*}{
\begin{tabular}{c} **Indicator** \\ **of Event** \\ \end{tabular} } & \multicolumn{2}{c}{**No. of Event**} & \multirow{2}{*}{**Classification Accuracy (\%)**} & \multirow{2}{*}{**Overall Accuracy (\%)**} \\ \cline{3-3} \cline{5-6}  & 0.00 & & & 0.00 & & \\ \hline Polynomial kernel & 0.00 & 827 & 1 & 99.9 & \\  & 1.00 & 96 & 5 & 5.0 & \\ \hline RBF kernel & 0.00 & 818 & 10 & 98.8 & \\  & 1.00 & 80 & 21 & 20.1 & 90.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Analysis of wastewater risk prediction model using SVM. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Wastewater Supply Risk Prediction**} & \multirow{2}{*}{
\begin{tabular}{c} **Indicator** \\ **of Event** \\ \end{tabular} } & \multicolumn{2}{c}{**No. of Event**} & \multirow{2}{*}{**Classification Accuracy (\%)**} & \multirow{2}{*}{**Overall Accuracy (\%)**} \\ \cline{3-3} \cline{5-6}  & 0.00 & & & 0.00 & & \\ \hline Hidden node: 100 & 0.00 & 799 & 29 & 96.5 & \\  & 1.00 & 37 & 64 & 36.6 & \\ \hline Hidden node: 300 & 0.00 & 810 & 18 & 97.8 & \\  & 1.00 & 60 & 41 & 40.6 & \\ \hline Hidden node: 500 & 0.00 & 809 & 19 & 97.7 & \\  & 1.00 & 48 & 53 & 52.5 & \\ \hline Hidden node: 700 & 0.00 & 807 & 21 & 97.5 & \\  & 1.00 & 41 & 60 & 59.4 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Analysis of wastewater risk prediction model using MLP. Where, GT is the groundwater table in meters, T is the alluvial layer thickness in meters, and SPT\({}_{\text{N}}\) is the sum of the number of blows required for the second and third 150 mm of penetration from SPT test. The total risk index can be calculated using Equation (7), and is classified into five categories as shown in Table 6.  
\[\text{Total risk index (\%)}=\text{(Geological risk)}\times 0.2+\text{(Water pipe risk)}\times 0.4+\text{(Wastewater pipe risk)}\times 0.4 \tag{7}\]  
Figure 7 illustrates the procedure to quantify the risk level in the hazard map system. \begin{table}
\begin{tabular}{c c} \hline
**Total Risk Index (\%)** & **Description** \\ \hline
80–100 & Very high risk requires closing the line for repair \\
65–79 & High risk requires immediate maintenance action \\
50–64 & Medium risk requires periodic maintenance action \\
25–49 & Low risk requires a maintenance plan \\ \textless{}25 & Very low risk requires no maintenance action \\ \hline \end{tabular}
\end{table} 
Table 6: Description of risk index in the hazard map system. Figure 6: (a) Railways of Seoul in mapping system and (b) a close-up example of tested locations showing different risk levels. Figure 7: Procedure to determine the risk index in the hazard map system. In this study, field validation of the established risk model was conducted using a nondestructive survey and pneumatic cone penetration (PCP) test. Twelve field test sections were selected based on the risk index represented from low to high.