Background and objective: As climate change intensifies extreme rainfall events, the global risk of landslides continues to rise. In Korea, existing landslide hazard maps and warning systems often prove inadequate for accurately predicting large-scale or complex slope failures. Consequently, empirical models like LAHARZ, which enable the rapid assessment of debris-flow volume-extent relationships, are drawing increased attention. This study examines whether LAHARZ can be effectively applied in Yecheon-gun a non-volcanic region to efficiently estimate landslide damage extents and strengthen local disaster preparedness.Methods: We collected debris-flow occurrence data from a July 2023 heavy rainfall event and derived a 5 m-resolution DEM from 1:5,000 topographic maps. Using satellite imagery, we delineated observed debris-flow areas in three sub-regions (Baekseok-ri, Geumgok-ri, Beolbang-ri) and input these datasets into LAHARZ to generate estimated damage extents, which were then compared with actual damage footprints.Results: In the simpler terrain of Baekseok-ri, the model's estimated damage extents closely matched observed debris-flow footprints, demonstrating robust performance. However, at multi-source sites (Beolbang-ri and Geumgok-ri), the estimated inundation ranges were either over- or underestimated. These discrepancies underscore the challenge of accurately capturing flow confluence when relying solely on volume-driven empirical formulas.Conclusion: LAHARZ proves effective for rapid damage-extent estimation in single-source debris-flow events, yet it may fail to capture key physical interactions in more complex terrains where multiple initiation points merge. Incorporating high-resolution DEM data, physical modeling techniques, and field measurements is recommended to enhance the model's accuracy and applicability in non-volcanic regions such as Yecheon County.

Background and objective: Landslides have inflicted significant damage to human lives and property for many years, leading to substantial socio-economic costs and environmental degradation. With the advent of climate change, the increase and intensification of rainfall exacerbate the risk of landslides. Considering this scenario, understanding the priorities in landslide response becomes crucial. This study aims to compare methods of predicting future landslide-prone areas, explore accurate forecasting techniques, and determine the landslide response priorities at the municipal level in the study Methods: (1) Collection and development of the landslide inventory map and landslide conditioning factors. (2) Constructing the landslide susceptibility model (LSM) using the landslide inventory map and conditioning factors. (3) Projecting rainfall data from periods B and C onto the LSM of past period A. (4) Comparing and analyzing landslide-prone areas for each scenario and year. (5) Identifying areas vulnerable to landslides based on the scenario with the most frequent occurrence of landslide-prone areas during the rainy seasons in periods B and C.Results: From the LSM, the landslide susceptible area (LSA) for period A was identified as 31,902 ㎢. All Supply-side platform(SSP) scenarios displayed an increasing trend in landslide-prone areas, with the SSP5-8.5 scenario displaying the most significant increase. Taking this into consideration, landslide response priorities were established, with Goseong County in South Gyeongsang ranking first with an LSA ratio of 88.4%. This suggests that this area should be prioritized for future landslide risk mitigation.Conclusion: The study provides a foundational model for future landslide response strategies which consider environmental changes. limitations of the study were challenges in considering landslide conditioning factors other than rainfall when analyzing future landslide susceptibility. Future studies will aim to provide more reliable information through higher resolution analysis and damage scale predictions and to discern response priorities.

Disasters that occur in buildings and urban spaces have a profound impact on people's daily lives, often exacerbating anxiety. In the case of buildings, the financial repercussions of damage can be substantial, and there is also a possibility of physical injuries and fatalities among occupants. Consequently, there is an imperative to assess the risk of disasters in buildings and to devise measures that ensure safety.The building risk analysis model developed in this study was designed to meet three criteria. First, it should be capable of responding to various disaster types, allowing the addition or removal of disaster categories as needed. Second, it should be able to present disaster risk at the building level. Third, the results must be easily comprehensible so that countermeasures can be prepared based on the assessed risk. To this end, we developed a building disaster risk analysis model to evaluate building-level risks for individual disaster types and to link these results. A multidimensional matrix was employed to assess fire, flood, and landslide risks at the building level.We then proceeded to analyze the fire, flood, and landslide risks of buildings in a sample area and linked the results. Machine learning and deep learning techniques were applied to the risk analysis. The integration of these three risk categories resulted in the classification of buildings into eight distinct categories—ranging from “very risky” to “safe”—based on the number of high-risk disaster types. A total of 32,079 buildings were assessed in the target area, of which 48 buildings (0.15%) were identified as being at high risk of both fire and flood, primarily situated along rivers and boulevards. Conversely, 47 buildings (0.15%) were at high risk of both fire and landslide, mainly located in forested areas. No buildings were found to be at high risk for all three disaster types. A total of 95 buildings (0.3%) were determined to be at high risk for two or more disaster types.A comprehensive approach to disaster risk mitigation necessitates the establishment of a building-level disaster risk check system. This system would be informed by the risk characteristics of each disaster type and the regional distribution of high-risk buildings. By leveraging this information, it would be possible to delineate inspection areas and items, as well as prepare countermeasures to ensure building safety. The establishment of a service that can assess disaster risk on a building-by-building basis will empower residents and users to proactively identify safety concerns and implement countermeasures, thus transitioning from a passive reliance on government assistance to a more autonomous and proactive approach to disaster mitigation.

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.

This study hypothesizes that the impacts of landslides on housing prices are not uniform but instead vary depending on their spatial proximity to hazard zones, as well as on neighborhood, urban, and temporal characteristics of each city. To test this hypothesis, we analyze APT price responses to landslides across three South Korean cities with distinct urban characteristics: Seoul (capital city), Busan (metropolitan city), and Gunsan (medium-sized local city). Using 120 three-stage hierarchical linear regression (HLR) models, the analysis incorporates housing characteristics, neighborhood attributes, and urban–temporal factors to capture multilevel variations in price dynamics. The results reveal distinct spatial and temporal patterns. At the national level, immediate post-event changes are not uniformly negative: within 250 m of landslide zones, prices increase by 0.8%, while 500-m and 750-m groups rise by 0.5% and 1.9%, respectively, and only the 1000-m group declines by 0.9%. However, in the following year, the 250-m and 500-m groups experience notable declines before showing partial recovery in the second year. City-specific trajectories further underscore regional heterogeneity. In Seoul, medium- and long-term declines dominate, with post-event decreases of 1.9%, 4.2%, and 3.5% in the 500-m, 750-m, and 1000-m groups, respectively. Busan exhibits the sharpest and most persistent declines, with immediate decreases of 3.2% to 4.1% across distance bands, followed by sustained downturns in subsequent years. In contrast, Gunsan shows mixed but relatively faster recovery, as the 750-m group increases by 3.6% post-event and eventually surpasses pre-landslide levels.