# Data Mining Approaches for Landslide Susceptibility Mapping in Unyeonsan, Seoul, South Korea
## 3 Data
SPI can be calculated as follows [44]:  
\[SPI=As\ \times\ \tan(\beta) \tag{2}\]  
where \(As\) is the area of the target point of the study area, and \(\beta\) is the slope angle. After calculating the slope, the slope length factor (SLF) for average erosion is calculated using the Revised Universal Soil Loss Equation (RUSLE) [45]. The SLF for slope of length \(\lambda\) is represented as:  
\[SLF=\bigg{(}\frac{\lambda}{72.6}\bigg{)}^{m} \tag{3}\]  
where the constant value 72.6 of the RUSLE is in feet, and the variable, \(m\), is the slope-length exponent calculated from the local slope angle \(\beta\)[45]. The SLF is influenced by the ratio of rill erosion to interrill erosion, \(\beta\). Rill erosion is caused by flow, while interrill erosion is caused by the effects from rainfall. System for Automated Geoscientific Analyses (SAGA) GIS were used to calculate additional topographic factors [46]. The standardized height is the absolute height multiplied by the normalized height of the study area; normalized height is the normalization of the height of the study area between 0 and 1. The standardized height was calculated using linear regression to yield the suitable parameters for prediction of the soil attributes by simplifying the complex terrain states [47]. The valley depth is the vertical distance of the specific point from the base level of the channel network [46]. The elevation of the channel network base level is interpolated, and the valley depth is calculated by subtracting the base level of the channel network from the DEM [46]. The downslope distance gradient is a quantitative estimation of the hydraulic gradient. The downslope distance gradient is obtained by calculating the downhill distance when water loses a determined quantity of energy from precipitation [48]. \(\tan a_{d}\) identifies the downslope distance gradient:  
\[\tan a_{d}=\ \frac{d}{L_{d}} \tag{4}\]  
where \(d\) and \(L_{d}\) are the elevation and the horizontal distance to the reference point, respectively. The type and composition of the soil influence the occurrence of landslides as they determine the degree of erosion and saturation [49]. The slope stability can be strengthened by the roots of vegetation and the degree of strengthening depends on the specific attributes of the vegetation; hence, the impact of heavy rainfall on the slope can be mitigated by vegetation [50]. The vegetation map was provided by the Korea Forest Research Institute and was in polygon format with a scale of 1:25,000 (Figure 4). The attributes of timber diameter, type, density, and age were extracted from the vegetation map. In addition to the vegetation variables, the soil type and condition also influence the occurrence of landslides. The soil maps used in this study were provided by the National Academy of Agricultural Science (RDA) in polygon format with a scale of 1:25,000. Soil depth, drainage, topography, and texture were derived from the soil maps (Figure 5). Figure 4: Landslide-related factors obtained from the vegetation map: (**a**) timber type; (**b**) timber diameter; (**c**) timber density; (**d**) timber age. Figure 5: Landslide-related factors obtained from the soil map: (**a**) soil depth; (**b**) soil drainage; (**c**) soil topography; (**d**) soil texture. All variables used in this study were resampled into a grid format of 5-m spatial resolution to create a spatial database. The total size of the spatial database was 1015 \(\times\) 874, and the study area contained 457,133 cells. The 103 landslide occurrence locations in point format were also converted into the 5-m grid format to be included in the data in the spatial database. Half of the 103 landslide location data points (51 points) were used as training data, and the other half were used as validation data. All of the collected data were prepared in ASCII format from the grid. Finally, a spatial database was established to apply the model to (Table 1).