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

Abstract

In this study, we performed seismic vulnerability assessment and mapping of the ML5.8 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.

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

“…Thus, several studies have found that tree-based machine learning models exhibited higher performance than statistical models, and RF models exhibited high performance in most studies, confirming their suitability for vulnerability analysis. In a previous study, Han et al (2019) [20] used 15 factors except for social indicators to build LR and SVM kernel models to compare and analyze their performance. 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), LR (0.649), and sigmoid (0.630).…”

“…Many recent studies related to seismic vulnerability assessment and mapping have been conducted using machine learning techniques [12,[18][19][20][21]. For example, Han et al (2019) [20] used a logistic regression (LR) model and applied the support vector machine (SVM) methodology to four kernel models (linear, polynomial, radial basis function, and sigmoid) to derive a suitable model for seismic vulnerability assessment; this study was notable in that the results of several seismic vulnerability models were compared analytically; such analyses are rarely conducted in this field, despite the broad application of machine learning techniques in recent years.…”

Seismic Vulnerability Assessment in Ranau, Sabah, Using Two Different Models

“…The FR model applied in this study is based on the assumption that future seismic activity in the study area will affect the area and the area's vulnerability to the future seismic event/s is directly correlated to the nine conditional factors. References [21] and [53] implemented the FR model for seismic vulnerability mapping in their research. In most cases, many of the past researches and studies used the FR model to develop landslide susceptibility models (e.g., [10,12,13,29,54,55]).…”

“…Seismic vulnerability in this study was expressed in terms of an index to analyze conditions controlled by physical and environmental factors, which increase the susceptibility of a community in the study area to the impact of seismic hazards [15]. Several methods for the seismic vulnerability mapping have been developed, proposed and adopted in recent years including Geographic Information System (GIS)-based multicriteria decision analysis (MCDA) [16], simple additive weighting (SAW) [16], analytical hierarchy process (AHP) [17][18][19], analytical network process (ANP) [17,20], logistic regression (LR) [17,21,22], support vector machine (SVM) [21], artificial neural network (ANN) [23], ANP-ANN [24], random forest (RF) along with decision tree (DT) and frequency ratio (FR) by [25] and step-wise weight assessment ratio analysis (SWARA) [26]. Reference [17] also combined various models to produce four hybrid models of; (1) fuzzy logic (fuzzy) with logistic regression (LR) (abbreviated as fuzzy-LR), (2) fuzzy with analytical network process (ANP) and AHP (abbreviated as A-fuzzy), (3) ANP and AHP with ordered weight averaging (OWA) (abbreviated as (A-OWA) and (4) OWA-LR.…”

Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations

“…Han et al [24], in their paper entitled "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", assess and map seismic vulnerability of the 2016 Gyeongju earthquake in Gyeongju, Korea. The LR and support vector machine (SVM) models were used to assess and map the seismic vulnerability.…”