• Journal of the Korean earth science society
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• v.27 no.4
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• pp.416-424
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• 2006

Because there is no generally accepted definition of a geographic center and no completely satisfactory method to determine one, there may be as many geographic centers of a country as there are many definitions. The geographic center of an area may be defined as the center of gravity on a surface, or that point on which the surface of an area would balance if it were a plane of uniform thickness. This research uses geographic information system (GIS) analysis and there are places where it defines the geographical and the geological centers in the inland of South Korea. To compute the geo-centers in South Korea: 1) firstly, we collected existing reaserch data related to digital map data. 2)Secondly, we analyzed a geological center and data collection examples of Korea and other nations-the Europe and America. 3) Thirdly, we carried out numerous processes to build a geodatabase, short for geograhic database, so that GIS analysis and the constructed geodatabase is covered within the inland in South Korea. Where geodatabase is a kind of spatial database. 4) Fourthly, in order to determine the geographical center, we supposed that the condition of continental surface is the plane of homogeneous or irregular density. 5) Consequently, we chose a few resonable conditions and produced a variety of geographical centers that is geometric and gravitational in South Korea. As a result of the analysis, center points are massed to southern part of Chungbuk province, Korea.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.299-316
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• 2019

The purpose of this study was to analyze landslide susceptibility in the Pyeongchang area using Weight of Evidence (WOE) and Evidential Belief Function (EBF) as probability models and Artificial Neural Networks (ANN) as a machine learning model in a geographic information system (GIS). This study examined the widespread shallow landslides triggered by heavy rainfall during Typhoon Ewiniar in 2006, which caused serious property damage and significant loss of life. For the landslide susceptibility mapping, 3,955 landslide occurrences were detected using aerial photographs, and environmental spatial data such as terrain, geology, soil, forest, and land use were collected and constructed in a spatial database. Seventeen factors that could affect landsliding were extracted from the spatial database. All landslides were randomly separated into two datasets, a training set (50%) and validation set (50%), to establish and validate the EBF, WOE, and ANN models. According to the validation results of the area under the curve (AUC) method, the accuracy was 74.73%, 75.03%, and 70.87% for WOE, EBF, and ANN, respectively. The EBF model had the highest accuracy. However, all models had predictive accuracy exceeding 70%, the level that is effective for landslide susceptibility mapping. These models can be applied to predict landslide susceptibility in an area where landslides have not occurred previously based on the relationships between landslide and environmental factors. This susceptibility map can help reduce landslide risk, provide guidance for policy and land use development, and save time and expense for landslide hazard prevention. In the future, more generalized models should be developed by applying landslide susceptibility mapping in various areas.

• Economic and Environmental Geology
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• v.34 no.4
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• pp.385-394
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• 2001

There are serious damage of people and properties every year due to landslides that are occurred by heavy rain. Because these phenomena repeat and the heavy rain is not an atmospheric anomaly, the counter plan becomes necessary. The study area, Ulsan, is one of the seven metropolitan, and largest cities of Korea and has many large facilities such as petrochemical complex and factories of automobile and shipbuilding. So it is necessary assess the landslide hazard potential. In the study. the three steps of landslide hazard assessment techniques such as susceptibility, possibility, and risk were performed to the study area using GIS. For the analyses, the topographic, geologic, soil, forest, meteorological, and population and facility spatial database were constructed. Landslide susceptibility representing how susceptible to a given area was assessed by overlay of the slope, aspect, curvature of topography from the topographic DB, type, material, drainage and effective thickness of soil from the soil DB, lype age, diameter and density from forest DB and land use. Then landslide possibility representing how possible to landslide was assessed by overlay of the susceptibility and rainfall frequency map, Finally, landslide risk representing how dangerous to people and facility was assessed by overlay of the possibil. ity and the population and facility density maps The assessment results can be used to urban and land use plan for landslide hazard prevention.