• Economic and Environmental Geology
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• v.38 no.1
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• pp.33-43
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• 2005

The purpose of this study is to make and validate landslide susceptibility map using artificial neural network and GIS in Kangneung area. For this, topography, soil, forest, geology and land cover data sets were constructed as a spatial database in GIS. From the database, slope, aspect, curvature, water system, topographic type, soil texture, soil material, soil drainage, soil effective thickness, wood type, wood age, wood diameter, forest density, lithology, land cover, and lineament were used as the landslide occurrence factors. The weight of the each factor was calculated, and applied to make landslide susceptibility maps using artificial neural network. Then the maps were validated using rate curve method which can predict qualitatively the landslide occurrence. The landslide susceptibility map can be used to reduce associated hazards, and to plan land use and construction as basic data.

• 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.

• Land and Housing Review
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• v.2 no.2
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• pp.157-162
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• 2011

This paper presents the landslide characteristics occurred in Hoengseong, Gangwondo and around July 16 in 2006, caused by heavy rainfall and antecedent precipitation by two typhoons of Ewiniar and Bilis. The main causes of landslides were antecedent precipitation between July 12 to 13, resulting in weakening grounds by increasing the degree of saturation previously, and the additional heavy rainfall between July 15 to 16. Most of landslides at natural slopes were transitional failures occurred along the boundary between residual weathered soil in shallow depth and hard mother rock. From the results of conclusive analyses for 100 sites in Hoengseong region where landslides occurred, the slope length of landslide and slope width were less than 100m with 87% of frequency and 30m with 74% of frequency, respectively. The average value of slope angles was $24^{\circ}$.

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 2001.03a
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• pp.221-230
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• 2001

본 연구에서는 지리정보시스템(GIS)을 이용하여 철도 연변 낙석, 산사태 관련 공간정보를 검색 및 출력 등 관리할 수 있는 정보시스템을 개발하였다. 이를 위해 전국 철도 노선 중 낙석 및 산사태 발생 가능성이 높은 경춘선, 영동선, 중앙선, 태백선, 정선선의 2.5km 혹은 5km 반경 지역에 대해 철도 관련 정보, 각종 지도 관련 정보, 지형분석 정보, 수문기상 정보, 현장 조사된 낙석 관련 정보 등 각종 공간 데이터베이스를 구축하였다. 그리고 구축된 공간 데이터베이스를 관리하는 철도 연변 낙석, 산사태 정보시스템을 개발하였다. 본 정보시스템은 보기환경, 테이블환경, 차트환경, 레이아웃환경, 프로젝트환경 등 5개로 구성되어 있다. 본 정보시스템의 기능은 구축된 공간 데이터베이스를 입력, 검색, 출력 뿐 아니라 자료 변환, 자료 및 범례 편집, 라벨 생성, 화면 확대, 축소, 지도 작성, 그림 편집, 문자 DB 관리, 차트작성, 도움말 등 다양하다. 본 정보시스템은 ArcView의 스크립트 언어인 Avenue를 이용하여 개발되었고 풀다운 메뉴 및 아이콘 방식을 채택하여 사용자가 사용하기 쉽게 개발되었다. 구축된 공간 데이터베이스와 개발된 정보시스템은 낙석 및 산사태 관리 및 분석을 위한 기본 자료 및 도구로 사용될 수 있다.

• Journal of the Korean GEO-environmental Society
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• v.20 no.6
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• pp.15-22
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• 2019

In this study, the hazard areas are identified by using the Newmark displacement model, which is a predictive model for identifying the areas at risk of landslide triggered by earthquakes, based on the results of field survey and laboratory test, and literature data. The Newmark displacement model mainly utilizes earthquake and slope related data, and the safety of slope stability derived from LSMAP, which is a landslide prediction program. Backyang Mt. in Busan where the landslide has already occurred, was chosen as the study area of this research. As a result of this study, the area of landslide prone zone identified by using the Newmark displacement model without earthquake factor is about 1.15 times larger than that identified by using LSMAP.

• The Journal of Engineering Geology
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• v.31 no.4
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• pp.631-646
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• 2021

Increasing incidences of landslides in Korea are endangering life and damaging property. To ascertain the cause of the rapid increase in landslides in 2020, this study analyzed the correlation between frequency of their occurrence and persistence of rainfall. The study area comprised seven areas in Gangwon-do, Gyeonggi-do, Gyeongsangnam-do, Gyeongsangbuk-do, Jeollanam-do, Jeollabuk-do, and Chungcheongnam-do. The used rainfall factors were monthly rainfall in June, July, and August, rainfall during the summer (June-August), rainfall during the monsoon season, and number of precipitation days during the summer and during the monsoon season. The effect of these factors on landslides was identified by comparing them with the occurrence of landslides in the year of increased landslide occurrence in each area. The results confirmed that not only rainfall but also the number of precipitation days during the monsoon season affect the occurrence of landslides. The rapid increase in landslide occurrence in 2020 was attributed to increases in both the number of precipitation days during the monsoon season and rainfall during the monsoon season in 2020. These results are expected to be used as basic data for future landslide warning standards that consider the effect of the persistence of rainfall.

• Journal of Korean Society for Geospatial Information Science
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• v.2 no.2 s.4
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• pp.43-53
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• 1994

GSIS(geo-spatial information system), particularly when utilized in hazard management decision, is one of hazard analysis tool. Data of GSIS input from digitizing or scanning of map or aerial photos. This paper focuses upon the hazard prediction in GSIS and RS analysis to assess map, aerialphotos, satellite imagery and soil map. This study found computation of hazard area analysis. the results is formed as raster data model of quadtree. Authors knew more accurate results of overlay. This paper shows building up integrated data base as well as search of hazard area in aerial photographs.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.4
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• pp.81-90
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• 2005

Based on the landslide hazard scoring system of Korea Forest Research Institute, a GIS model for predicting landslide hazards was developed. The risk of landslide hazards was analyzed as the function of 7 environmental site factors for the terrain, vegetation, and geological characteristics of the corresponding forest stand sites. Among the environmental factors, slope distance, relative height and shapes of slopes were interpreted using the forestland slope interpretation module developed by Chung et al. (2002). The program consists of three modules for managing spatial data, analyzing landslide hazard and report-writing, A performance test of the model showed that 72% of the total landslides in Youngin-Ansung landslides area took place in the highly vulnerable zones of grade 1 or 2 of the landslide hazard scoring map.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.1 s.35
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• pp.3-12
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• 2006

Kangwondo area is mountainous and landslide happens easily during the rainy period in summer time. Especially, when there is torrential downpour caused by the unusual weather change, there will be greater possibility to see landslide. It is very difficult to analyze and study a natural phenomenon like the landslide because there are so many factors behind it. And the way to conduct the analysis is also very complicated. However, if GIS is used, we can classify and analyze data efficiently by modeling the real phenomenon with a computer. Based upon the analysis on the causes of landslide in the areas where it occurred in the past, therefore, this study shows several factors leading to landslide and contains the GIS database categorized by grade and stored in the computer. In order to analyze the influence of every factor causing landslide, we calculated the rates of weight by AHP and evaluated landslide vulnerability in the study area by using GIS. As a result of such analysis, we found that the forest factor has most potential influences among other factors in landslide.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1205-1214
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• 2021

Precipitation is one of a major causes of landslides by rising of pore water pressure, which leads to fluctuations of soil strength and stress. For this reason, precipitation is the most frequently used to determine the landslide thresholds. However, using only precipitation has limitations in predicting and estimating slope stability quantitatively for reducing false alarm events. On the other hand, Soil Moisture (SM) has been used for calculating slope stability in many studies since it is directly related to pore water pressure than precipitation. Therefore, this study attempted to evaluate the appropriateness of applying soil moisture in determining the landslide threshold. First, the reactivity of soil saturation level to precipitation was identified through time-series analysis. The precipitation threshold was calculated using daily precipitation (P_daily) and the Antecedent Precipitation Index (API), and the hydrological threshold was calculated using daily precipitation and soil saturation level. Using a contingency table, these two thresholds were assessed qualitatively. In results, compared to P_daily only threshold, Goesan showed an improvement of 75% (P_daily + API) and 42% (P_daily + SM) and Changsu showed an improvement of 33% (P_daily + API) and 44% (P_daily + SM), respectively. Both API and SM effectively enhanced the Critical Success Index (CSI) and reduced the False Alarm Rate (FAR). In the future, studies such as calculating rainfall intensity required to cause/trigger landslides through soil saturation level or estimating rainfall resistance according to the soil saturation level are expected to contribute to improving landslide prediction accuracy.