Abstract
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.
Highlights
An ML 5.8 earthquake occurred 8.7 km south–southwest of Gyeongju, South Korea (35◦46 36” N, 129◦11 24” E) at 11:32:55 UTC (20:32:54 Korea Standard Time; GMT + 9 h) on 12 September 2016 [1,2]
The results showed that the performance of the model based on the radial basis function (RBF) kernel (0.998) of SVM was the best, followed by polynomial (0.842), linear (0.649), logistic regression (LR) (0.649), and sigmoid (0.630)
Seismic vulnerability maps were created and seismic vulnerability assessment was performed for buildings in Gyeongju, South Korea using the probabilistic frequency ratio (FR) model and machine-learning-based decision tree (DT) and random forest (RF) models
Summary
An ML 5.8 earthquake occurred 8.7 km south–southwest of Gyeongju, South Korea (35◦46 36” N, 129◦11 24” E) at 11:32:55 UTC (20:32:54 Korea Standard Time; GMT + 9 h) on 12 September 2016 [1,2]. As of 31 March 2017 [3], the Gyeongju Earthquake was the largest earthquake among those recorded by the domestic seismic observation network; it consisted of a shock wave with concentrated energy, in which strong ground motion lasted for only 1–2 s, 15 km beneath the surface. Due to these characteristics, the initial reporting indicated that the earthquake did not significantly damage structures; it resulted in 5368 damaged properties, 111 victims, and 23 injured people. This disaster made it impossible to rule out the possibility of similar earthquakes in the future, highlighting the importance of precautions to prevent greater losses
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