Seismic vulnerability assessment and mapping using frequency ratio and logistic regression: A case study of Gyeongju, South Korea

In this study, a seismic vulnerability of Gyeongju city, where the 9.12 Gyeongju earthquakes occurred, was analyzed and compared the prediction accuracy using frequency ratio (FR) and logistic regression (LR) models. The buildings damaged by the 9.12 Gyeongju earthquakes were used as dependent variables, of which the buildings were randomly selected data for training (70%) and validation (30%). The total eighteen seismic-related factors were used as independent variables as slope, elevation, groundwater level, distance to epicenter, distance to faults, peak ground acceleration (PGA), age of children, age of elderly, population density, building density, construction materials, number of floors, age of buildings, distance to police stations, distance to fire stations, distance to hospitals, distance to gas stations, and distance to road network. The spatial relationship between damaged buildings and seismic-related factors was analyzed using FR and LR models. The produced seismic vulnerability maps were classified into five zones, i.e., very high, high, moderate, low, and very low. The two maps validated and compared prediction accuracy using relative operating characteristic (ROC) curve and the areas under the curves (AUC). The validation results indicated that AUC value of FR seismic vulnerability map (73.1%) was about 3% higher than LR map (71.4%). The seismic vulnerability maps produced in this study could possibly be used to minimize damage caused by earthquakes and could be used as a reference when establishing policies.