# Simulation of Debris-Flow Runout Near a Construction Site in Korea
## 3 Method of Numerical Simulation of Debris Flows Based on Landslide Susceptibility Analysis
### Analysis of Landslide Susceptibility  
Landslide susceptibility was analyzed based on the landslide probability mapping of the area by [11]. The landslide probability map was drawn by using a logistic regression model, developed by [14], to predict landslide susceptibility. The target area of landslide prediction was Mt Goobong, located to the east of the construction site. The metamorphic rock complex, amphibolites and colluvium, 1.0-1.5 m thick on slopes of 30-40 degrees, make the mountain susceptible to landslides.  
[PERSON] et al. [14] proposed a logistic regression landslide model reflecting the complex relationship among the factors (especially geology, topography, and soil properties) influencing landslides. The following is the logistic regression model for landslide prediction derived by [14].  
\[\begin{array}{ll}\text{logit }(\text{p})=&-9.3670+0.2129*\text{Slope angle}+0.7690*\text{ Dry density}-0.0052\\ &*\text{Elevation}+0.4248*\text{Lithology}+0.1777\\ &*\text{Permeability coeff.}+~{}0.0555*\text{Porosity}\end{array} \tag{1}\]  
The output of this equation is converted to probability with the following equation.  
\[\text{p}=\ \frac{\text{exp}[\text{logit}(\text{p})]}{[1+\text{exp}[\text{ logit}(\text{p})]]} \tag{2}\]  
Figure 5: Small scale talus distribution on mountain slopes.  
[PERSON] et al. [11] used this relationship for landslide prediction mapping in the Chooncheon area including Mt Goobong. Among the factors in Equation (1), slope angle and elevation were collected by the topographic digital elevation model (DEM). Lithology was classified by a field survey for the study area. Soil properties such as dry density, permeability coefficient and porosity were acquired by the laboratory soil tests with undisturbed soil samples collected at the field. The area was divided into 10 m \(\times\) 10 m cells and factor values (as in Equation (1)) were assigned to each cell. The logit values were then calculated in each cell and converted to landslide occurrence probabilities. This landslide prediction map was used to assess the landslide susceptibility of the study area, covering 12 km\({}^{2}\), around Mt Goobong (Figure 6).  
Focusing on the western slope of the mountain, the probability of a landslide was grouped into four grades. Areas with over 70% landslide probability, when over 250 mm of rain fell locally in 24 h, were categorized as high potential based on [14]. According to the rainfall data in Chooncheon area from 2000 to 2019, rainfall events over 250 mm/24 h occurred three times in 2011, 2013 and 2016. Because the most highly weighted factor in the logistic regression model is slope angle, landslide probability is primarily controlled by slope angle with steep slopes having higher landslide probabilities than gentle slopes. However, steep slope areas with outcrops, and hence fewer soil materials, tend to have landslide probabilities lower than 50%. The outcrop areas were determined during the regolith mapping on Mt Goobong.  
Within the prediction map, areas with 70-90% landslide probability cover 3.5% of the land and those with over 90% probability are 0.79% of the area. The location of the construction site appears to be particularly vulnerable to debris flows from two valleys, both with areas of landslide probability over 70%. Other parts of Mt Goobong, to the north and south of the construction site, also include several areas with over 70% landslide probability.