# Data Mining Approaches for Landslide Susceptibility Mapping in Unyeonsan, Seoul, South Korea
## 6 Discussion  
In this study, two types of data mining models were used to create landslide susceptibility maps. Because the results of data mining models are derived from their data, the reliability of the input data is fundamental. Landslide location data were acquired from aerial photographs to provide accurate training datasets. The data mining models are suitable for observing the rapid changes in an urban area with its compactness and complex characteristics. In addition, other topographical-, forest-, and soil-related variables were obtained, and topographic-related factors were calculated from the topographical map produced from the aerial photographs.  
The results from each model in Figure 7 were not statistically different in the spatial pattern of the landslide susceptibility area. This can be interpreted that all of the four prediction results from the SVM model were based on the same input factors, and all landslide predisposing factors were regarded as having equal importance so that the same weight was assigned to each predisposing factor. However, for the results from the RBF kernel, which had the highest accuracy among the SVM  
Figure 8: Validation results using data mining models: (**a**) SVM model; (**b**) ANN model.  
models, the susceptibility on the west, southwest, and southeast slopes was lower than that from the other kernels, which was similar to the trend for the ANN model. In general, the northern area of the mountain showed high susceptibility, and had the same pattern as the existing damaged area of the southern ring roads.  
The percentages of the classes of five indices were also calculated. The linear, polynomial, and sigmoid kernels were distributed fairly evenly, while the RBF kernel showed a slightly higher percentage in the very high index class, and a lower percentage in the very low index class. This suggests that a high percentage in the high susceptibility index yields good validation results. Figure 8a shows that the RBF kernel had a percentage of 77.71%, which was approximately 5% higher than that of the other kernels. Similarly, the results from the ANN model yielded a high percentage (more than 30% of the total) in the very high class, which represented high risk areas, and this also led to a high validation percentage for the ANN model, 78.41%. In addition, the ANN model results in Table 3 show that the standardized height contributed most to landslide susceptibility with a normalized weight of greater than 2. This can be seen in Figure 7e, which shows the effect of standardized height with the high relative weight of the ANN model. Standardized height was the most important factor among all of the landslide-related factors. The factors related to landslide susceptibility should be managed based on their relative weight.  
Because this study investigated the susceptibility of landslides in urban areas, the study area was fairly small, with an area of approximately 11.4 km\({}^{2}\) and a spatial resolution of 5 m along road and river boundaries rather than a rectangular boundary. It should be noted that the ROC was characterized using a percentage of the landslide susceptibility rates. Thus, in this study, the validation rate was relatively low compared with most previous studies, which were based on rectangles. Moreover, we could use the slope unit instead of common raster unit, which is closely correlated to the topography, for the further steps; the slope unit is defined as the unit between the valleys and ridges. Most of the mountains in the city are managed in the framework of city parks in which the various economic and social aspects of Umyeonsan are also considered, rather than the area being covered solely by forests that are more resistant to landslides. Given that landslide damage in urban areas can lead to human casualties, it is important to investigate the soil and topographical characteristics of the areas where landslides are likely to occur. Furthermore, landslide risk should be incorporated in city-level policies.