# Data Mining Approaches for Landslide Susceptibility Mapping in Unyeonsan, Seoul, South Korea
## 5 Results  
Figure 7a-d shows the landslide susceptibility maps produced by applying the SVM models with four kernels; and Figure 7e shows the map of the results from the ANN models. The values of the landslide susceptibility results were sorted in descending order and categorized into five classes for easy visual interpretation. For intuitive and efficient analysis, the results for the landslide susceptible areas were classified into five groups of approximately the same size: very high, high, medium, low, and very low susceptibility. The comparative analysis between the results and existing landslide occurrence locations was also made easier by representing the landslide susceptibility with the five categories. The spatial distribution of the landslide locations was concentrated in high-susceptibility areas. In Figure 7, the results map of the linear, polynomial, and sigmoid kernels of the SVM models showed a similar pattern and represented the influence of the input factors of Figures 4 and 5. In addition, the RBF kernel showed a similar pattern to the ANN map in that the center of the map had relatively higher susceptibility. In general, highly susceptible areas were principally distributed in the northeast direction from the center of the study area. Areas with high susceptibility were located in dense forest areas of broadleaved forest, above-average soil depth, and steep slopes. The dominant rocks constituting the Umyeonsan area are greaisic rocks, which have high landslide susceptibility because they are heavily eroded; landslides occur in areas of high soil drainage. Figure 7: _Cont._  
In Table 2, the area of each class is represented by percentage. The areas with very high indices in the SVM model were 2.2599, 2.2599, 2.2889 and 2.28369 km\({}^{2}\) for the linear, polynomial, RBF, and sigmoid kernels, respectively. Among the results from the SVM models, the RBF kernel accounted for a slightly higher percentage (20.03%) of the areas with a very high index compared with the linear, polynomial, and sigmoid kernels. The areas with a very high index in the ANN model covered 3.9262 km\({}^{2}\), which had the highest percentage (34.35%). In contrast, the areas with high index accounted for only a small proportion (8.65%). The area with a very low index accounted for similar percentages of 20.00, 20.00, 20.00 and 19.99% for the linear, polynomial, and sigmoid kernels, and ANN, respectively, while the RBF accounted for a lower percentage of 19.89%. Table 3 shows the relative weights per variable, which were calculated from randomly selected training data for 20 epochs of 200 cycles. The weights between layers represent the contribution of each variable to the prediction of the landslide susceptibility. Timber age showed the lowest average value of weight, 0.04828. For the comparison analysis of the relativeness of the weights, the values were divided by the weight of the timber age and were normalized with respect to the average weight of the timber age. The highest average weight of 0.09615 for the standardized height was normalized to 2.00164, which was approximately twice the value of the weight of the timber age. The second major variable contributing to landslides was timber diameter (1.46886), followed by curvature (1.28335), soil depth (1.27746), and aspect (1.25804). The standard deviation ranged from 0.00022 to 0.01132. To investigate the reliability of the proposed landslide susceptibility map using the previously described SVM and ANN data mining models, we performed a validation. The 2011 landslide occurrence data not used for the training were used for validation. After 51 points of training data had been extracted using random sampling from the landslide occurrence data from the 2011 Umyeonsan landslide and applied to the models, 52 points were used for the validation. Because the predicted landslide susceptibility map was an indicator of the potential possibility of landslides in the future, the data obtained after the occurrence of the landslides were a suitable choice for the validation data.