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

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.

Gaussian Process Regression-Based Structural Response Model and Its Application to Regional Damage Assessment

“…The Korean Peninsula is located in East Asia, surrounded by the Eurasian plate where earthquakes occur. This peninsula has landscapes including plutonic, sedimentary, and volcanic rocks deposited over a tectonic and geomorphic history [21] and geological structures including faults and weak crust that have led to concurrent earthquakes [22]. Mountain ranges on the peninsula are featured by erosions and tectonic movements with an asymmetric topography that has experienced an initial uplift [23].…”

Development of a monitoring system for grain loss of paddy rice based on a decision tree algorithm

“…Each branch of a tree represents a chain of nodes from the root to a leaf, and each node represents an attribute (or feature) [15][16][17] . Decision trees are one of the most effective and widely used techniques in many areas of Data Mining, such as pattern recognition, machine learning, image processing and information retrieval [18][19][20] .…”

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

“…Although seismic vulnerability assessment using various indicators with similar approaches has never been carried out in the study area, various research projects internationally have been undertaken concerning seismic vulnerability assessment. [25] for example used FR, DT, RF to map the seismic vulnerability of Gyeongju, South Korea, and compared the accuracies of the three models using 18 factors. They found out that the peak ground acceleration (PGA) and distance to the epicenter have the highest influences on seismic vulnerability in the DT and RF models while altitude had the greatest impact in the FR model.…”

“…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.…”