The purpose of this study is to identify the relationship between weather variables and buildings damaged in natural disasters. We used four datasets on building damage history and 33 weather datasets from 230 regions in South Korea in a decision tree analysis to evaluate the risk of building damage. We generated the decision tree model to determine the risk of rain, gale, and typhoon (excluding gale with less damage). Using the weight and limit values of the weather variables derived using the decision tree model, the risk of building damage was assessed for 230 regions in South Korea until 2100. The number of regions at risk of rain damage increased by more than 30% on average. Conversely, regions at risk of damage from snowfall decreased by more than 90%. The regions at risk of typhoons decreased by 57.5% on average, while those at high risk of the same increased by up to 62.5% under RCP 8.5. The results of this study are highly fluid since they are based on the uncertainty of future climate change. However, the study is meaningful because it suggests a new method for assessing disaster risk using weather indices.

Changes in extreme weather patterns are expected under climate change. In this study, a risk assessment was conducted using 4 building damage history datasets and 33 weather datasets (precipitation, wind speed, snow, and temperature) from 230 regions in South Korea to quantitatively analyze and predict building damage caused by potential future natural disasters. Decision tree analysis was used to evaluate building damage risk in 230 regions. The decision tree model to determine the risk of flood, gale, and typhoon was generated, which excluded gales, with less damage. The weight (variable importance) and limit value (damage limit) of the weather variables ware derived using the decision tree model. Using these two factors, we assessed the building damage risk in 230 regions in South Korea until 2100. The number of regions at risk of flood damage increased by more than 30% in average. Conversely, regions at risk of snowfall damage decreased by more than 90%. The regions at risk of typhoons decreased by 57.5% on average, and the number of regions at high risk of typhoon damage increased by up to 62.5% in RCP 8.5. These results can be used as objective data to minimize future building damage throughout South Korea, representing the first step towards sustainable development in the region with respect to disaster response.

Earthquakes contribute to approximately 20% of annual global losses from natural disasters, causing over 25,000 fatalities each year and significant socio-economic damage. Quantitative risk assessment has become a critical tool for mitigating and responding to earthquake-related risks. While South Korea experiences fewer and less severe earthquakes than its neighbors, recent magnitude 5 or greater events along the eastern coast have underscored the urgent need for region-specific risk assessments.This study introduces a deterministic methodology for evaluating seismic risks in three regions along South Korea’s east coast, identified as areas of potentially high seismic vulnerability. Unlike prior studies that often focus on macro-level analyses, this research employs a fine-scale 500-meter grid framework to integrate three key components: (1) historical earthquake occurrences, (2) population exposure, and (3) building vulnerability.Historical seismic data from 1978 to the present were interpolated to calculate cumulative magnitudes for each grid cell. Population exposure was quantified by aggregating data for individual buildings within the grid, providing a localized understanding of human vulnerability. Building vulnerability was assessed using the seismic fragility functions developed by South Korea’s Ministry of the Interior and Safety. These functions, incorporating variables such as structural type, seismic design status, site-specific ground motion, and amplification factors, were applied to estimate damage probabilities under a 2,400-year return period earthquake scenario. The damage rates for approximately 250,000 buildings were then converted into damage areas by applying them to the total floor area of each building.Key findings reveal significant spatial variations in risk across the study regions, with critical zones requiring immediate intervention. For example, highly vulnerable zones were identified near densely populated areas with older, non-seismically designed buildings. The results highlight the value of integrating grid-based frameworks, ground motion data, and fragility functions into seismic risk assessments, providing actionable insights for disaster mitigation and preparedness.This research advances the precision of regional seismic risk assessments and establishes a foundation for future developments, including probabilistic modeling and artificial intelligence applications. By offering tailored risk reduction strategies, the findings support policymakers and disaster managers in their efforts to reduce vulnerabilities and enhance resilience against seismic hazards.