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

Background and objective: Climate change driven increases in the frequency and intensity of localized heavy rainfall have undermined slope stability in the Seoul metropolitan area, where urban development meets steep terrain. This study develops a robust, ensemble-based landslide susceptibility model and applies it at the building scale to quantify damage potential, 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 generated at a 3:1 ratio using the Small Range Envelope method. The data were divided into 80% for training and 20% for testing, with five-fold cross-validation. Ten machine learning algorithms were trained; models achieving a validation AUC ≥ 0.70 were combined into an ensemble via weighted averaging and committee voting. Ensemble outputs were reclassified into five quantile-based susceptibility classes (1: Very High; 2: High; 3: Moderate; 4: Low; 5: Very Low) and overlaid on building point 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 the northern 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 residential structures comprising 53% of these assets. This building-scale framework supports targeted, data-driven disaster mitigation planning.Conclusion: Integrating an ensemble-based landslide susceptibility surface with building-use attributes provides a practical, 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 clear priorities for mitigation. We also outline a policy-ready extension Potential Risk = (Susceptibility + Runout) × Exposure × Vulnerability to support pre-disaster screening and hazard-informed planning

Geographic information system · Georeference · Geospatial analysis · Landslide · Metropolitan area · Spatial analysis · Electrical and Electromagnetic Research · Geochemistry and Geologic Mapping · Geological Modeling and Analysis

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