• 한국GIS학회:학술대회논문집
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• 한국GIS학회 2003년도 공동 춘계학술대회 논문집
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• pp.71-76
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• 2003

The purpose of this study is the development, application and assessment of probability and artificial neural network methods for assessing landslide susceptibility in a chosen study area. As the basic analysis tool, a Geographic Information System (GIS) was used for spatial data management. A probability method was used for calculating the rating of the relative importance of each factor class to landslide occurrence, For calculating the weight of the relative importance of each factor to landslide occurrence, an artificial neural network method was developed. Using these methods, the landslide susceptibility index was calculated using the rating and weight, and a landslide susceptibility map was produced using the index. The results of the landslide susceptibility analysis, with and without weights, were confirmed from comparison with the landslide location data. The comparison result with weighting was better than the results without weighting. The calculated weight and rating can be used to landslide susceptibility mapping.

• 한국지형학회지
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• 제23권2호
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• pp.109-125
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• 2016

This study aims to identify the landslide susceptible zones of Boeun area and provide reliable landslide susceptibility maps by applying different modeling methods. Aerial photographs and field survey on the Boeun area identified landslide inventory map that consists of 388 landslide locations. A total ofseven landslide causative factors (elevation, slope angle, slope aspect, geology, soil, forest and land-use) were extracted from the database and then converted into raster. Landslide causative factors were provided to investigate about the spatial relationship between each factor and landslide occurrence by using fuzzy set and logistic regression model. Fuzzy membership value and logistic regression coefficient were employed to determine each factor's rating for landslide susceptibility mapping. Then, the landslide susceptibility maps were compared and validated by cross validation technique. In the cross validation process, 50% of observed landslides were selected randomly by Excel and two success rate curves (SRC) were generated for each landslide susceptibility map. The result demonstrates the 84.34% and 83.29% accuracy ratio for logistic regression model and fuzzy set model respectively. It means that both models were very reliable and reasonable methods for landslide susceptibility analysis.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.306-309
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• 2004

The susceptibility of landslides and the effect of landslide-related factors at Penang in Malaysia using the Geographic Information System (GIS) and remote sensing data have been evaluated. Landslide locations were identified in the study area from interpretation of aerial photographs and from field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land use from Landsat TM (Thermatic Mapper) satellite images; and the vegetation index value from SPOT HRV (High Resolution Visible) satellite images. Landslide hazardous areas were analysed and mapped using the landslide-occurrence factors employing the probability-frequency ratio method. To assess the effect of these factors, each factor was excluded from the analysis, and its effect verified using the landslide location data. As a result, land 'cover had relatively positive effects, and lithology had relatively negative effects on the landslide susceptibility maps in the study area. In addition, the landslide susceptibility maps using the all factors showed the relatively good results.

• 대한공간정보학회지
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• 제14권1호
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• pp.3-12
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• 2006

강원도 지역은 산지지형이 많고 여름철 장마나 이상기후에 의한 국지적인 집중호우에 의해서 산사태가 자주 발생하고 있다. 산사태를 유발하는 인자들은 매우 다양하고 붕괴 메커니즘이 매우 복잡하기 때문에 산사태와 같은 자연현상을 분석하고 연구하기에는 많은 어려움이 따른다. 그러나 GIS를 이용하면 효과적으로 자료를 분류하고 분석할 수 있으며, 컴퓨터에 의해 실세계를 모델링함으로서 분석결과를 시각적이고 객관적으로 설명할 수 있다. 따라서 이 연구에서는 과거 산사태가 발생하였던 지역에서의 산사태 발생 원인에 대한 분석을 통해서 산사태를 유발하는 인자를 결정하고, 각 유발인자들을 등급별로 분류하여 GIS DB를 구축하였으며, AHP법에 의해 경중률을 계산하고 GIS를 이용하여 연구지역에 대한 산사태 발생취약성을 평가한 후, 각각의 산사태 유발인자의 영향을 분석한 결과 임상인자의 영향이 가장 큰 것으로 분석되었다.

• 한국측량학회지
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• 제19권3호
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• pp.273-281
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• 2001

GSIS와 AHP법을 이용하여 삼척지역을 대상으로 산사태 발생 가능성을 분석하였다. 산사태를 유발하는 많은 인자들 중에서 경사도, 경사방향, 지질, 토양, 임상자료들은 기존의 지도자료를 이용하여 입력하여 데이터베이스를 구축하였다. 연구대상지역의 환경적ㆍ지리적 특성을 고려하여 산사태를 유발하는 인자를 결정하였으며, AHP법을 적용하여 유발인자들에 대한 입력값을 결정하였다. 산사태가 발생할 가능성이 있는 지역은 산사태 유발인자들로 만들어진 각각의 레이어를 중첩함으로써 작성되었다. 마지막으로 작성된 도면을 실제 산사태가 발생한 곳과 비교함으로써 산사태 유발인자들이 산사태 발생에 미치는 영향을 알아보았다. 그 결과 삼척지역에서는 토양과 지질적 요소가 가장 많은 영향을 미쳤다는 것을 알 수 있었다.

• 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.

• 대한원격탐사학회지
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• 제36권1호
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• pp.67-81
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• 2020

The rapid climatic changes being caused by global warming are resulting in abnormal weather conditions worldwide, which in some regions have increased the frequency of landslides. This study was aimed to analyze and compare the landslide susceptibility using the Frequency Ratio (FR), Statistical Index, Weight of Evidence, Certainty Factor, and Index of Entropy (IoE) at Woomyeon Mountain in South Korea. Through the construction of a landslide inventory map, 164 landslide locations in total were found, of which 50 (30%) were reserved to validate the model after 114 (70%) had been chosen at random for model training. The sixteen landslide conditioning factors related to topography, hydrology, pedology, and forestry factors were considered. The results were evaluated and compared using relative operating characteristic curve and the statistical indexes. From the analysis, it was shown that the FR and IoE models were better than the other models. The FR model, with a prediction rate of 0.805, performed slightly better than the IoE model with a prediction rate of 0.798. These models had the same sensitivity values of 0.940. The IoE model gave a specific value of 0.329 and an accuracy value of 0.710, which outperforms the FR model which gave 0.276 and 0.680, respectively, to predict the spatial landslide in the study area. The generated landslide susceptibility maps can be useful for disaster and land use planning.

• 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.

• 한국환경생태학회지
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• 제22권2호
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• pp.119-127
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• 2008

본 연구는 북한산 국립공원을 대상으로 산사태 발생인자들에 대한 공간정보를 구축하였으며, 중첩분석 및 합산평가 매트릭스분석을 이용한산사태 취약성 지도 및 생태적 위험 지표 작성을 통해 향후산사태 재해 예방을 위한 기초자료를 제시하고자 하였다. 산사태 평가 인자로는 사면경사, 사면방향, 경사길이, 토양배수, 식생활력도(NDVI), 토지이용도가 선택되었으며, 공간데이터베이스는 $30m\times30m$ 해상도로 구축되었다. 분석결과, 우이동 및 도봉계곡 일대의 산사태 취약성이 높은 것으로 분석되었으며, 생태적 위험도는 도봉계곡, 용어천계곡 및 정릉계곡, 평창계곡 등이 높은 것으로 분석되어 향후 이들 지역의 관리계획 수립 시 산사태 위험에 대한 영향도 고려되어져야 할 것으로 판단된다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.363-366
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• 2004

The purpose of this study is to reveal the spatial relationship between landslides and geospatial data set and to map the landslide susceptibility using this relationship, and the landslide occurrence data in Kangneung area in 2002. Landslide locations were identified from interpretation of satellite images. Landslide susceptibility was analyzed using an artificial neural network. The weights of each factor were determined by the back-propagation training method. Susceptibility maps were constructed from Geographic Information System (GIS), The cases were overlaid and cross overlaid for landslide susceptibility mapping in each study area in Kangneung.