## 3 Risk Model
### Evaluation of Risk Model  
The 310 water and 101 wastewater data were obtained within 50 m of the area where ground subsidences occurred. Furthermore, 154 water and 828 wastewater data were collected at the locations where the events had not occurred. Table 1 shows the examples of water and wastewater data collected. Figure 5: Multi-layer perceptron (MLP) model. As shown in the Table 1, the main attributes of the water and wastewater data were installation date, diameter, length, and average cover depth. In this study, polynomial and radial basis function (RBF) kernel techniques were used for SVM approach in order to map the given data to the new vector space. The classification performance of the SVM with polynomial and the RBF kernel is shown in Table 2.  
In the table, indicator of event \"0.00\" represents the absence of ground subsidence and \"1.00\" the presence of it. The classification accuracy based on the polynomial kernel was 81.5% and the accuracy based on the RBF kernel was 86.6%. Overall, the prediction accuracy for the presence of event was superior to that of the absence of event case. As stated earlier, MLP creates a hidden layer between input and output layers to learn the weights and bias of nodes belonging to the three layers. The classification accuracy was measured by varying the number of hidden nodes which are the neurons in the hidden layer to establish a water supply risk model. As presented in Table 3, the classification accuracy of absence of the event case (87.7% with the number of hidden nodes of 700) was significantly improved compared to the SVM model. As the number of hidden nodes increased, higher accuracy was generally achieved. The wastewater risk model was also assembled using the SVM and MLP. Similar to water risk model assessment, RBF and polynomial functions were used as SVM kernel functions, and their performance is shown in Table 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pipe Type** & **Installation Date** & **Diameter (m)** & **Length (m)** & **Average Depth (m)** \\ \hline Water & 19 October 2011 & 1.00 & 14.62 & 1.10 \\ Wastewater & 1 January 1997 & 4.50 & 59.09 & 2.00 \\ Wastewater & 1 January 1989 & 7.00 & 4.01 & 5.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Examples of data for computing the risk level. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Water Supply Risk Prediction** & \
\begin{tabular}{c} **Indicator** \\ **of Event** \\ \end{tabular} &
\begin{tabular}{c} **No. of Event** \\ **0.00** \\ \end{tabular} & **Classification Accuracy (\%)** & **Overall Accuracy (\%)** \\ \hline Polynomial kernel & 0.00 & 99 & 55 & 64.3 & 81.5 \\  & 1.00 & 31 & 279 & 90.0 & 81.5 \\ \hline Radial basis function (RBF) kernel & 0.00 & 112 & 42 & 72.7 & 86.6 \\  & 1.00 & 20 & 290 & 93.5 & 86.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Analysis of water risk prediction model using Support Vector Machine (SVM). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Water Supply Risk Prediction** & \ \begin{tabular}{c} **Indicator** \\ **of Event** \\ \end{tabular}  & \begin{tabular}{c} **No. of Event** \\ **0.00** \\ \end{tabular}  & **Classification Accuracy (\%)** & **Overall Accuracy (\%)** \\ \hline Hidden node: 100 & 0.00 & 126 & 28 & 81.8 & 89.9 \\  & 1.00 & 19 & 291 & 93.9 & 89.9 \\ \hline Hidden node: 300 & 0.00 & 132 & 22 & 85.7 & 92.2 \\  & 1.00 & 1  
In Table 4, the overall classification accuracy is as high as 89.6% and 90.3%; however, it should be noted that the classification accuracy of the presence of the event was substantially low compared to the water risk model. This is attributed to the fact that wastewater risk model was trained with a relatively imbalanced dataset between the absence and presence of ground subsidence cases, unlike the water risk datasets. Table 5 presents the results of the wastewater risk model assessment using the MLP model. The classification accuracy of the presence of the event case was improved compared to that of the SVM model.