# Data Mining Approaches for Landslide Susceptibility Mapping in Unyeonsan, Seoul, South Korea
## 7 Conclusions  
In this study, we applied a spatial data mining approach to identify areas where landslides are likely to occur using aerial photographs and GIS. Landslide-prone locations were determined using the interpretation of aerial photographs and field survey results to construct spatial datasets. The topographical predisposing factors were extracted from a digital topographical map constructed from aerial photographs, and the soil and forest predisposing factors were extracted from publicly available maps. A spatial database was constructed using the extracted and calculated factors including randomly extracted training data for the landslide area, and landslide susceptibility was mapped using SVM and ANN models. Finally, the results map was validated using the half of the landslide location data not used for training.  
In urban areas such as Seoul, landslides have previously not been considered a serious concern, compared with floods and storms. Seoul has undergone rapid urbanization that has taken place with little consideration of natural disasters. In large cities such as Seoul, global warming, rapid urbanization, extreme precipitation, and population pressures could potentially lead to complex events, including landslides occurring in the city, which could have serious consequences. Therefore, it is important to create a scientifically valid landslide susceptibility map for urban areas such as Seoul.  
The predicted rate of landslides was high on the steep mountain tops and similar to the general landslide pattern on the ridges. The map created in this study showed that susceptibility to landslides decreased with slope gradient. Because landslides are caused by differences in gravity due to terrain shape, it is logical that their occurrence is affected by the geographical structure of the area.  
The relationship between landslide occurrence and forested land cover showed that broadleaved forests had a high susceptibility, and this type of forest occupied the largest proportion of the study area. In addition, large trunk-size, high-density forests, and middle-aged forests had a high risk of landslides. Among the soil factors, red and yellow soils had higher landslide susceptibility, as well as areas with large soil depth; the occurrence rate was higher in areas with good soil drainage. The performance of the SVM and ANN models was validated using AUC analysis. The SVM model yielded 72.41%, 72.83%, 77.17% and 72.79% accuracy for the linear, polynomial, RBF, and sigmoid kernels, respectively; while the ANN model had 78.41% accuracy. The results of this study are expected to support spatial decision-making from relevant agencies to formulate policies related to landslide disaster risk reduction in urban areas.  
This research was conducted by Korea Environment Institute (KEI) with support of a grant (16 CTAP-C114629-01) from Technology Advancement Research Program (TARP) funded by Ministry of Land, Infrastructure and Transport of Korean government. This work was supported by the Space Core Technology Development Program through the National Research Foundation of Korea, funded by the Ministry of Science, ICT and Future Planning, under Grant (NRF-2014M1A3A3A03034798).  
[PERSON] applied the SVM and ANN models and wrote the paper. [PERSON] designed the experiments. Also, he interpreted the input data and the results. [PERSON] suggested the idea and organized the paperwork. In addition, he collected data and conducted pre-processing of the input data. All of authors contributed to the writing of each part.  
The authors declare no conflict of interest.