Seismic Vulnerability Assessment and Mapping of Gyeongju, South Korea Using Frequency Ratio, Decision Tree, and Random Forest

Jihye Han, Jinsoo Kim, Soyoung Park, Sanghun Son and Minji Ryu
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Jihye Han: Geomatics Research Institute, Pukyong National University, 45 Yongso-ro, Busan 48513, Korea
Jinsoo Kim: Department of Spatial Information System, Pukyong National University, 45 Yongso-ro, Busan 48513, Korea
Soyoung Park: Geomatics Research Institute, Pukyong National University, 45 Yongso-ro, Busan 48513, Korea
Sanghun Son: Division of Earth Environmental System Science (Major of Spatial Information Engineering), Pukyong National University, 45 Yongso-ro, Busan 48513, Korea
Minji Ryu: Division of Earth Environmental System Science (Major of Spatial Information Engineering), Pukyong National University, 45 Yongso-ro, Busan 48513, Korea

Sustainability, 2020, vol. 12, issue 18, 1-22

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.

Keywords: seismic vulnerability assessment; frequency ratio; decision tree; random forest; machine learning; Gyeongju Earthquake; geographic information system (GIS) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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