Building Damage Potential Assessment Based on Landslide Susceptibility Analysis in Major South Korean Cities

• 발행기관: 인간식물환경학회
• 저자명: Jun Woo Kim Ho Gul Kim
• 간행물 정보: 『인간식물환경학회지(JPPE)』제28권 제4호, 389~397쪽, 전체 9쪽
• 주제분류: 자연과학 > 자연과학일반
• 파일형태: PDF
• 발행일자: 2025.08.31

국문 초록

Background and objective: Climate change driven increases in the frequency and intensity of localized heavy rainfall haveundermined slope stability in the Seoul metropolitan area, where urban development meets steep terrain. This studydevelops a robust, ensemble-based landslide susceptibility model and applies it at the building scale to quantify damagepotential, providing municipalities with precise, actionable guidance for risk mitigation. Methods: We compiled 883 georeferenced landslide events and ten spatial predictors covering topography, soil properties,climate variables, and vegetation cover. To correct presence-only sampling bias, pseudo absence points were generatedat a 3:1 ratio using the Small Range Envelope method. The data were divided into 80% for training and 20% for testing, withfive-fold cross-validation. Ten machine learning algorithms were trained; models achieving a validation AUC ≥ 0.70 werecombined into an ensemble via weighted averaging and committee voting. Ensemble outputs were reclassified into fivequantile-based susceptibility classes (1: Very High; 2: High; 3: Moderate; 4: Low; 5: Very Low) and overlaid on buildingpoint data to assign each structure a class by use (residential, commercial, public). Results: The ensemble achieved a validation AUC of 0.968. Areas of high landslide susceptibility are concentrated in thenorthern and eastern parts of Gyeonggi Province. Among municipalities, Yongin-si, Yangpyeong-gun, Gwangju-si,Namyangju-si, and Seoul Special City exhibit the greatest numbers of high-susceptibility buildings, with residentialstructures comprising 53% of these assets. This building-scale framework supports targeted, data-driven disastermitigation planning. Conclusion: Integrating an ensemble-based landslide susceptibility surface with building-use attributes provides apractical, building-scale measure of susceptibility-based damage potential across the Seoul metropolitan area. High-susceptibility assets are disproportionately single-family houses and Neighborhood Living Facilities, indicating clearpriorities for mitigation. We also outline a policy-ready extension Potential Risk = (Susceptibility + Runout) × Exposure ×Vulnerability to support pre-disaster screening and hazard-informed planning.

목차

Introduction

Research Methods

Results and Discussion

Conclusion

References

키워드

ensemble machine learning landslide susceptibility susceptibility-based building damage potential GIS