# Data Mining Approaches for Landslide Susceptibility Mapping in Unyeonsan, Seoul, South Korea
## 5 Results
However, an erosion control project implemented after the 2011 event has significantly reduced the possibility of landslides. Therefore, it is important to obtain information on landslide susceptibility in areas where landslides have not occurred. In addition, implementation of precautionary measures to prevent landslides from occurring in the future is necessary. Thus, half of the landslide occurrence points were used as training data, and the remaining points were used as validation data. Figure 7: Landslide susceptibility maps generated using data mining models: Using an (**a**) support vector machine (SVM) model with linear kernel; (**b**) SVM model with polynomial kernel; (**c**) SVM model with radial basis function kernel; (**d**) SVM model with sigmoid kernel; (**e**) and the artificial neural network (ANN) model. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Landslide**} & \multicolumn{6}{c}{**SVM**} & \multirow{2}{*}{**Backpropagation**} & \multirow{2}{*}{**Percentage**} \\ \cline{2-2} \cline{5-11}
**Susceptibility Index** & **Linear** & **Percentage** & **Polynomial** & **Percentage** & **REF** & **Percentage** & **Sigmoid** & **Percentage** & **ANN (km\({}^{3}\))** & **(\%)** \\ \cline{2-2} \cline{5-11}
**Very high** & **2.2599** & **19.77\%** & **2.2599** & **19.77\%** & **2.2889** & **20.03\%** & **2.2836** & **19.98\%** & **3.9262** & **34.35\%** \\ High** & **2.3081** & **20.20\%** & **2.3081** & **20.20\%** & **2.2815** & **19.11\%** & **2.2866** & **20.01\%** & **0.9881** & **8.65\%** \\ Moderate** & **2.2878** & **20.02\%** & **2.2878** & **20.02\%** & **2.2329** & **19.56\%** & **2.2836** & **20.01\%** & **1.9451** & **17.02\%** \\ Low & **2.2866** & **20.01\%** & **2.2866** & **20.01\%** & **2.4474** & **21.14\%** & **2.2859** & **20.00\%** & **2.2849** & **19.99\%** \\ Very low & **2.2858** & **20.00\%** & **2.2878** & **20.00\%** & **2.2727** & **19.89\%** & **2.2859** & **20.00\%** & **2.2841** & **19.99\%** \\ Total & **11.4283** & **100.00** & **11.4283** & **100.00** & **11.4283** & **100.00** & **11.4283** & **100.00** & **11.4283** & **100.00** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Area and percentage of each category. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**200 Cycles for 1 epoch** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **Average** & **Standard** & **Normalized Weight** \\ \hline Slope gradient & 0.0555 & 0.0548 & 0.0546 & 0.0546 & 0.0549 & 0.0552 & 0.0557 & 0.0561 & 0.0564 & 0.056 The AUC of the receiver operating characteristic (ROC) curve was used for the validation of the models. The ROC consists of specificity, the \(x\)-axis, which shows the percentage of the areas where landslides are expected to occur but have not occurred, and sensitivity, the \(y\)-axis, which represents the predicted landslide areas according to training data of landslide occurrences. The ROC graph was drawn using the following procedure. To show the rank relative to the predicted results, the values of the results of landslide susceptibility from the models were sorted in descending order. The ranked results were represented in ROC curves using 100 classes; a single class comprised an area that was approximately 1% of the total study area. Figure 8a shows the AUC of the landslide susceptibility map for the SVM model; the linear, polynomial, RBF, and sigmoid kernel had 72.41 (0.7241), 72.83 (0.7283), 77.17 (0.7717) and 72.79% (0.7279) accuracy versus 78.41% (0.7841) accuracy in the ANN model (Figure 8b). A higher AUC value indicates better model performance, but it is important to perform additional empirical analyses based on input and ground truth data. Our validation results indicated that the five landslide sensitivity assessments performed well; the AUC values were all greater than 0.7 [60]. In the SVM model, the AUC value using the RBF kernel was 0.7717, which was superior to the other three kernels in the study area.