Performance of Logistic Regression and Support Vector Machines for Seismic Vulnerability Assessment and Mapping: A Case Study of the 12 September 2016 ML5.8 Gyeongju Earthquake, South Korea

Jihye Han, Soyoung Park, Seongheon Kim, Sanghun Son, Seonghyeok Lee and Jinsoo Kim
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Jihye Han: Division of Earth Environmental System Science (Major of Spatial Information Engineering), Pukyong National University, 45 Yongso-ro, Busan 48513, Korea
Soyoung Park: BK21 Plus Project of the Graduate School of Earth Environmental Hazards System, Pukyong National University, 45 Yongso-ro, Busan 48513, Korea
Seongheon Kim: Division of Earth Environmental System Science (Major of Spatial Information Engineering), 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
Seonghyeok Lee: Division of Earth Environmental System Science (Major of Spatial Information Engineering), 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

Sustainability, 2019, vol. 11, issue 24, 1-19

Abstract: In this study, we performed seismic vulnerability assessment and mapping of the M L 5.8 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.

Keywords: seismic vulnerability; support vector machine; kernel function; logistic regression; machine learning; Gyeongju Earthquake; GIS (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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