Comparison of factors influencing landslide risk near a forest road in Chungju-si, South Korea

Abstract

Abstract Background The study aimed to identify the influential factors required to prepare landslide vulnerability maps and establish disaster prevention measures for mountainous areas with forest roads. The target area is Sancheok-myeon, Chungju-si, where several landslides have occurred in a narrow area of approximately 3 km × 4 km. As the area has the same rainfall and vegetation conditions, the influences of the physico-mechanical characteristics of the soil in accordance with compaction and topographic characteristics could be analyzed precisely. Methods Geological surveying, sampling, and laboratory testing assessed landslide risk in the study area, and data including unit weight, specific gravity, porosity, water content, soil depth, friction angle, cohesion, slope angle, profile/plan curvature, TWI were obtained. Preprocessing and screening such as min-max normalization and multicollinearity were conducted for the data in order to eliminate overestimation of each factor’s effectiveness. The influence of each factor was analyzed using logistic regression (LR), structural equation modeling (SEM), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). Results All methods showed that soil depth has the greatest impact on landslide occurrence. Friction angle, slope angle, and porosity were also selected as influential factors, although each method ranked them slightly differently. Topographic factors, such as plan curvature, profile curvature, and the topographic wetness index, had minimal influence. This appears to be because landslides near forest roads are more affected by how well compaction was performed during banking than by the concave or convex shape of the slope. This study presents analysis results for an area with the same rainfall and vegetation conditions; therefore, the analysis of the influence of the physico-mechanical characteristics of the soil and topography was more precise than when comparing landslides occurring in different regions. Our results may be helpful in preparing landslide vulnerability maps.

Keywords

• Landslide influential factor
• Logistic regression analysis
• Structural equation model
• XGBoost
• LightGBM