• 한국측량학회:학술대회논문집
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• 한국측량학회 2004년도 추계학술발표회 논문집
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• pp.381-384
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• 2004

The goal of this study is to generate a landslide susceptibility map using GIS(geographic information system) based method. A simple and efficient algorithm is proposed to generate a landslide susceptibility map from DEM(digital elevation model) and existing maps. The categories of controlling factors for landslides, aspect of slope, soil, topographical index, landuse, vegetation are defined, because those factors are said to have relevance to landslide and are easy to obtain theirs sources. The weight value for landslide susceptibility is calculated from the density of the area of landslide blocks in each class. Finally, a map of susceptibility zones is produced using the weight value of all controlling factors, and then each susceptibility zone is evaluated by comparing with the distribution of each controlling factor class.

• 자원환경지질
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• 제33권4호
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• pp.333-340
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• 2000

The purpose of this study is to develop landslide susceptibility analysis techniques using artificial neural network and apply the newly developed techniques for assessment of landslide susceptibility to study areas, Yongin. Landslide locations detected from interpretation of aerial photo and field survey, and topographic, soil and geological maps of the Yongin area were collected. The data of the locations of land-slide, slope, soil texture, topography and lithology were constructed into spatial database using GIS. Using the factors, landslide susceptibility was analyzed by artificial neural network methods. The results of the analysis were verified using the landslide location data. The validation results showed satisfactory agreement between the susceptibility map and landslide location data.

• 한국지반환경공학회 논문집
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• 제15권12호
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• pp.79-85
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• 2014

To analyze the landslide susceptibility in Baekdu mountain area in china, we get two susceptibility maps using AcrView software through weighted overlay GIS (Geographic Information System) method in this paper. To assess the landslide susceptibility, five factors which affect the landslide occurrence were selected as: slope, aspect, soil type, geological type, and land use. The weight value and rating value of each factor were calculated by the two different methods of AHP (Analytic Hierarchy Process) and ANN (Artificial Neural Network). Then, the weight and rating value was used to obtain the susceptibility maps. Finally, the susceptibility map shows that the very dangerous areas (0.9 or higher) were mainly distributed in the mountainous areas around JiAnShi, LinJiangShi, and HeLongShi near the china-north Korea border and in the mountainous area between the WangQingXian and AnTuXian. From the contrast two susceptibility map, we also Knew that The accuracy of landslide susceptibility map drew by ANN method was better than AHP method.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2002년도 가을 학술발표회 논문집
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• pp.499-506
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• 2002

The purpose of this study is to develop landslide susceptibility analysis techniques using artificial neural networks and to apply the newly developed techniques for assessment of landslide susceptibility to the study area of Yongin in Korea. Landslide locations were identified in the study area from interpretation of aerial Photographs and field survey data, and a spatial database of the topography, soil type and timber cover were constructed. The landslide-related factors such as topographic slope, topographic curvature, soil texture, soil drainage, soil effective thickness, timber age, and timber diameter were extracted from the spatial database. Using those factors, landslide susceptibility and weights of each factor were analyzed by two artificial neural network methods. In the first method, the landslide susceptibility index was calculated by the back propagation method, which is a type of artificial neural network method. Then, the susceptibility map was made with a GIS program. The results of the landslide susceptibility analysis were verified using landslide location data. The verification results show satisfactory agreement between the susceptibility index and existing landslide location data. In the second method, weights of each factor were determinated. The weights, relative importance of each factor, were calculated using importance-free characteristics method of artificial neural networks.

• 한국GIS학회:학술대회논문집
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• 한국GIS학회 2003년도 공동 춘계학술대회 논문집
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• pp.77-82
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• 2003

The purpose of this study is to develop landslide susceptibility analysis techniques using artificial neural network and to apply the developed techniques to the study area of janghung in Korea. Landslide locations were identified in the study area from interpretation of satellite image and field survey data, and a spatial database of the topography, soil, forest and land use were consturced. The 13 landslide-related factors were extracted from the spatial database. Using those factors, landslide susceptibility was analyzed by artificial neural network methods, and the susceptibility map was made with a e15 program. For this, the weights of each factor were determinated in 5 cases by the backpropagation method, which is a type of artificial neural network method. Then the landslide susceptibility indexes were calculated using the weights and the susceptibility maps were made with a GIS to the 5 cases. A GIS was used to efficiently analyze the vast amount of data, and an artificial neural network was turned out be an effective tool to analyze the landslide susceptibility.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.291-293
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• 2004

The aim of this study was to cross-validate a spatial probabilistic model of landslide likelihood ratios at Boun, Janghung and Yongin, in Korea, using a Geographic Information System (GIS). Landslide locations within the study areas were identified by interpreting aerial photographs, satellite images and field surveys. Maps of the topography, soil type, forest cover, lineaments and land cover were constructed from the spatial data sets. The 14 factors that influence landslide occurrence were extracted from the database and the likelihood ratio of each factor was computed. 'Landslide susceptibility maps were drawn for these three areas using likelihood ratios derived not only from the data for that area but also using the likelihood ratios calculated from each of the other two areas (nine maps in all) as a cross-check of the validity of the method For validation and cross-validation, the results of the analyses were compared, in each study area, with actual landslide locations. The validation and cross-validation of the results showed satisfactory agreement between the susceptibility map and the existing landslide locations.

• 한국측량학회지
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• 제32권4_1호
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• pp.281-292
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• 2014

본 연구는 산사태 관련 인자를 달리하여 산사태 취약성을 분석한 후, 정확도를 비교하고자 한다. 이를 위해 항공사진을 이용하여 산사태 위치를 추출하였고, 항공 LiDAR와 수치지도를 이용한 지형인자, 각종 주제도를 이용한 토양, 임상, 토지피복 인자를 추출하여 공간데이터베이스를 구축하였다. 산사태 취약성 지도는 로지스틱 회귀분석과 빈도비를 이용하여 산사태 취약지수를 산정하는 것에 의해 작성되었다. 분석결과, 항공 LiDAR와 수치지도의 상관관계는 거의 일치하였으며, 각 방법별로 작성된 산사태 취약성 지도 사이에는 강한 상관관계가 존재하였다. 각 방법별로 작성된 산사태 취약성 지도는 높은 예측 정확도를 보였다. 특히, 빈도비와 항공 LiDAR를 이용할 경우 성능이 더욱 향상되었다. 이를 통해 항공 LiDAR 자료는 효과적인 산사태 발생 예측 및 피해저감대책을 수립하는데 기여할 것으로 판단된다.

• 한국지형학회지
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• 제26권3호
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• pp.53-67
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• 2019

The purpose of this study is to identify the quality of landslide susceptibility in a landslide-prone area (Jinbu-myeon, Gangwon-do, South Korea) by spatial prediction modeling approach and compare the results obtained. For this goal, a landslide inventory map was prepared mainly based on past historical information and aerial photographs analysis (Daum Map, 2008), as well as some field observation. Altogether, 550 landslides were counted at the whole study area. Among them, 182 landslides are debris flow and each group of landslides was constructed in the inventory map separately. Then, the landslide inventory was randomly selected through Excel; 50% landslide was used for model analysis and the remaining 50% was used for validation purpose. Total 12 contributing factors, such as slope, aspect, curvature, topographic wetness index (TWI), elevation, forest type, forest timber diameter, forest crown density, geology, landuse, soil depth, and soil drainage were used in the analysis. Moreover, to find out the co-relation between landslide causative factors and incidents landslide, pixels were divided into several classes and frequency ratio for individual class was extracted. Eventually, six landslide susceptibility maps were constructed using the Bayesian Predictive Discriminant (BPD), Empirical Likelihood Ratio (ELR), and Linear Regression Method (LRM) models based on different category dada. Finally, in the cross validation process, landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract success rate curve. The result showed that Bayesian, likelihood and linear models were of 85.52%, 85.23%, and 83.49% accuracy respectively for total data. Subsequently, in the category of debris flow landslide, results are little better compare with total data and its contained 86.33%, 85.53% and 84.17% accuracy. It means all three models were reasonable methods for landslide susceptibility analysis. The models have proved to produce reliable predictions for regional spatial planning or land-use planning.

• 한국지반환경공학회 논문집
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• 제18권2호
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• pp.21-27
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• 2017

In order to evaluate landslide susceptibility in Yanbian region, this study analyzed 7 factors related to landslide occurrence, such as soil, geology, land use, slope, slope aspect, fault and river by Analytic Hierarchy Process (AHP), and calculated the weights of these 7 hazard-induced factors, determined the internal weights and the relative weights between various factors. According to these weights, combining the Remote Sensing technology (RS) with Geographic Information System technology (GIS), the selected area was evaluated by using GIS raster data analysis function, then landslide susceptibility chart was mapped out. The comprehensive analysis of AHP and GIS showed that there has unstable area with the potential risk of sliding in the research area. The result of landslide susceptibility agrees well with the historical landslides, which proves the accuracy of adopted methods and hazard-induced factors.

• 한국환경과학회지
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• 제18권11호
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• pp.1189-1197
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• 2009

The purpose of this research was develop tailored landslide hazard assessment table (LHAT) in Gyeongsangbuk-do Province and propose building strategies on environmental information system to estimate landslide hazard area according to LHAT. To accomplish this purpose, this research investigated factors occurring landslide at 172 landslide occurred sites in 23 city and county of Gyeongsangbuk-do Province and analyzed what factors effected landslide occurrence quantity using the multiple statistics of quantification method(I). The results of analysis, factors affecting landslide occurrence quantity were shown in order of slope position, slope length, bedrock, aspect, forest age, slope form and slope. And results of the development of LHAT for predict mapping of landslide-susceptible area in Gyeongsangbuk-do Province, total score range was divided that 107 under is stable area(IV class), 107~176 is area with little susceptibility to landslide(III class), 177~246 is area with moderate susceptibility to landslide(II class), above 247 area with severe susceptibility to landslide(I class). According to LHAT, this research built landslide attribute database and made 7 digital theme maps at mountainous area located in Goryeong Gun, Seongju-Gun, and Kimcheon-City. The results of prediction on degree of landslide hazard using environmental information system, area with little susceptibility to landslide(III class) occupied 65.56% and severe susceptibility to landslide(I class) occupied 0.51%.