# Prediction of Ground Subsidence Risk in Urban Centers Using Underground Characteristics Information
## 4 Ground Subsidence Risk Prediction Model Based on Machine Learning
### Results of the Machine Learning Application  
This study aims to develop a machine learning-based ground subsidence risk prediction model using data on underground facility attributes, permeability coefficient, stratigraphic thickness, and height. A total of 12 datasets were constructed according to the conditions. The constructed datasets were used in the machine learning algorithm and the model performance was evaluated with and without the SMOTE sampling technique. The machine learning-based model was implemented in Python 3.8 using the scikit-learn library for machine learning algorithms and sampling. The results derived from the model developed in this study are presented in Tables 7 and 8. Table 7 shows the results of the model trained on the dataset without the application of the SMOTE sampling technique, and Table 8 presents the results of the model with the SMOTE sampling technique applied. The results indicate that the model with the SMOTE sampling technique exhibits superior performance. The improved model performance can be attributed to the mitigation of the extreme imbalance in ground subsidence risk data through sampling to amplify a small amount of data [13]. The LGBM model with SMOTE demonstrated the best performance under the following conditions; years of use are categorized into 5-year increments, the pipe diameter is 50 mm, and the number of instances of ground subsidence in a grid with a risk level of 2 is between 1 and 3. The conditions of this dataset are identical to those under which a ground subsidence risk prediction model was developed using only underground facility attribute information [13]. The difference in the accuracy between the training (0.740) and evaluation (0.658) sets was less than 0.1, suggesting that overfitting has been avoided. The AUC was 0.83, indicating excellent model quality. The accuracy of the model was lower than that of other models. However, the F1-Score, which is the most important metric for evaluating imbalanced data, was the highest at 0.66, and hence, this model was determined to be the best. The hyperparameters tuned to achieve excellent model performance are shown in Table 9.  
Figure 2: Conceptual diagram of the ROC Curve. The importance values of the 20 key factors used in classifying the ground subsidence risk, obtained from the best-performing model, are shown in Figure 3. Y denotes the years of use, and DTR denotes the pipe diameter. The importance of pipelines with a pipe diameter between 551 and 600 mm was the highest, and data from pipelines with a diameter between 251 and 300 mm, as well as data included between 35 and 40 years of burial, were primarily utilized. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Model** & \multicolumn{2}{c}{**RF**} & \multicolumn{2}{c}{**XGB**} & \multicolumn{2}{c}{**LGBM**} \\ \hline
**No.** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** \\ \hline
1 & 0.791 & 0.55 & 0.763 & 0.56 & 0.771 & 0.56 \\
2 & 0.708 & 0.56 & 0.678 & 0.55 & 0.678 & 0.55 \\
3 & 0.658 & 0.62 & 0.626 & 0.6 & 0.646 & 0.61 \\
4 & 0.776 & 0.54 & 0.768 & 0.55 & 0.766 & 0.56 \\
5 & 0.701 & 0.57 & 0.678 & 0.55 & 0.681 & 0.51 \\
6 & 0.661 & 0.62 & 0.643 & 0.61 & 0.661 & 0.63 \\
7 & 0.788 & 0.55 & 0.778 & 0.56 & 0.771 & 0.57 \\
8 & 0.688 & 0.56 & 0.698 & 0.59 & 0.706 & 0.58 \\
9 & 0.646 & 0.61 & 0.661 & 0.63 & 0.671 & 0.64 \\
10 & 0.761 & 0.53 & 0.773 & 0.57 & 0.768 & 0.54 \\
11 & 0.681 & 0.53 & 0.658 & 0.55 & 0.678 & 0.55 \\
12 & 0.656 & 0.60 & 0.648 & 0.6 & 0.656 & 0.60 \\ \hline \end{tabular}
\end{table} 
Table 7: Results of the machine learning model (SMOTE not applied). \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Model** & \multicolumn{2}{c}{**RF**} & \multicolumn{2}{c}{**XGB**} & \multicolumn{2}{c}{**LGBM**} \\ \hline
**No.