The main purpose of this study was to compare the prediction accuracies of various seismic vulnerability assessment and mapping methods. We applied the frequency ratio (FR), decision tree (DT), and random forest (RF) methods to seismic data for Gyeongju, South Korea. A magnitude 5.8 earthquake occurred in Gyeongju on 12 September 2016. Buildings damaged during the earthquake were used as dependent variables, and 18 sub-indicators related to seismic vulnerability were used as independent variables. Seismic data were used to construct a model for each method, and the models’ results and prediction accuracies were validated using receiver operating characteristic (ROC) curves. The success rates of the FR, DT, and RF models were 0.661, 0.899, and 1.000, and their prediction rates were 0.655, 0.851, and 0.949, respectively. The importance of each indicator was determined, and the peak ground acceleration (PGA) and distance to epicenter were found to have the greatest impact on seismic vulnerability in the DT and RF models. The constructed models were applied to all buildings in Gyeongju to derive prediction values, which were then normalized to between 0 and 1, and then divided into five classes at equal intervals to create seismic vulnerability maps. An analysis of the class distribution of building damage in each of the 23 administrative districts showed that district 15 (Wolseong) was the most vulnerable area and districts 2 (Gangdong), 18 (Yangbuk), and 23 (Yangnam) were the safest areas.

In this study, we performed seismic vulnerability assessment and mapping of the M_L5.8 Gyeongju Earthquake in Gyeongju, South Korea, as a case study. We applied logistic regression (LR) and four kernel models based on the support vector machine (SVM) learning method to derive suitable models for assessing seismic vulnerabilities; the results of each model were then mapped and evaluated. Dependent variables were quantified using buildings damaged in the 9.12 Gyeongju Earthquake, and independent variables were constructed and used as spatial databases by selecting 15 sub-indicators related to earthquakes. Success and prediction rates were calculated using receiver operating characteristic (ROC) curves. The success rates of the models (LR, SVM models based on linear, polynomial, radial basis function, and sigmoid kernels) were 0.652, 0.649, 0.842, 0.998, and 0.630, respectively, and the prediction rates were 0.714, 0.651, 0.804, 0.919, and 0.629, respectively. Among the five models, RBF-SVM showed the highest performance. Seismic vulnerability maps were created for each of the five models and were graded as safe, low, moderate, high, or very high. Finally, we examined the distribution of building classes among the 23 administrative districts of Gyeongju. The common vulnerable regions among all five maps were Jungbu-dong and Hwangnam-dong, and the common safe region among all five maps was Gangdong-myeon.

<!-- *** Custom HTML *** --> Kim, S.; Park, S.; Han, J.; Son, S.; Lee, S.; Han, K.; Kim, J., and Kim, J., 2019. Feasibility of UAV photogrammetry for coastal monitoring: A case study in Imlang Beach, South Korea. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 386-392. Coconut Creek (Florida), ISSN 0749-0208. This study assessed the potential of unmanned aerial vehicle (UAV) photogrammetry to accurately monitor coastal zone features, such as vertical profiles, and to detect shoreline. In total, 245 images with a ground spatial distance (GSD) of 1.59 cm were captured using a Zenmuse X7 camera mounted on an Inspire 2 UAV at Imlang Beach, Busan, Korea; 40 ground control points (GCPs) for UAV photogrammetry and 21 stations for terrestrial laser scanning (TLS) were surveyed using a network real-time kinematic (RTK) approach. The root mean square error (RMSE) values in the X, Y, and Z directions were 0.015, 0.017, and 0.040 m, respectively, based on bundle adjustment of 24 GCPs and 16 checkpoints. The root sum of squares error (RSSE) was 0.046 m. To assess the accuracy of the vertical profiles obtained for Imlang Beach, digital elevation models (DEMs) of six cross-shore profiles were constructed and compared based on UAV photogrammetry and TLS surveying. Vertical accuracy assessment showed an average difference in height between the models of 0.02 m and an RMSE of 0.04 m. A well-established object-based image segmentation approach was applied with standard parameters (size 100, shape 0.5, and compactness 0.5) to extract the shoreline from orthomosaic images of Imlang Beach. The results suggest that UAV photogrammetry has the capacity to achieve accurate and continuous coastal monitoring.

Urban flood damage is increasing globally, particularly in major cities. Factors contributing to flood risk include urban environmental changes, such as watershed development and precipitation variations caused by climate change. Rapid urbanization and weather anomalies further complicate flood management and damage mitigation. Additionally, detailed analyses at small spatial units (e.g., roads, buildings) remain insufficient. Hence, urban flood analysis considering such spatial variations is required. This study analyzed flood risk in Ulsan, Korea, under a severe flood scenario. Land cover changes from the 1980s to 2010s were examined in 10-year intervals, along with the frequency of heavy rainfall and high river water levels that trigger severe floods. Flood risk was structured as a matrix of likelihood and impact. The results revealed that land cover changes, influenced by development policies or regulations, had a minimal impact on urban flood risk, which is likely because effective drainage systems and stringent urban planning regulations mitigated their effects. However, the frequency and intensity of extreme precipitation events had a substantial effect. These findings were validated using a comparative analysis of an inundation damage trace map and flood range simulated by a physical model. The 10 m grid resolution and time-series likelihood-and-impact framework used in this study can inform budget allocation, resource mobilization, disaster prevention planning, and decision-making during disaster response efforts in major cities.

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.