• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.06a
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• pp.231-237
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• 2010

이상기후에 의한 집중호우나 태풍의 영향으로 예전에는 기록이 없었던 백두대간과 전국 국립공원의 자연사연에서 산사태가 많이 발생하고 있으며 특히 지형이 험준하고 고도가 높은 지리산의 경우, 다른 국립공원에 비해서 그 발생빈도가 높게 나타난다. 본 연구에서는 지리산 북쪽지역으로 경상남도 함양군 마천면과 전라북도 남원시 산내면에 걸쳐서 발생한 산사태를 중심으로 산사태를 발생시키는 영향인자를 GIS와 원격탐사를 이용하여 분석하였다. 먼저 산사태 발생 지역의 지형특성을 분석하였고 산사태 발생과 산사태 발생에 영향을 끼친 인자들의 상관관계를 알아보기 위해서 빈도비를 사용하였으며 가중치를 도출하기 위해서 다중 회귀분석을 실시하였다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.19 no.3
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• pp.273-281
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• 2001

The landslide occurrence in Sam-Chuck area was analyzed through Geo-Spatial Information System and AHP(Analytic Hierarchy Process). Among many factors which causes landslide, terrain slope, terrain aspect, lithology, soil texture and vegetation arc taken as input data from existing maps and constructed as a database. These factors are determined by each environmental factor by environmental and geological characters in the study area, and the rating and weight about factor are input using AHP. Possible areas for landslide have been extracted by overlaying each layers. Finally, the estimated results are compared with real landslide sites to know which factor is the most effective for landslide. The results showed that lithology and soil factor have high susceptibility in Sam-Chuck area.

• Spatial Information Research
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• v.20 no.3
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• pp.57-66
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• 2012

Gangwon-do has been suffering extensive landslide dam age, because its geography consists mainly of mountains. Analyzing the related factors is crucial for landslide prediction. We digitized the landslide and non-landslide spots on an aerial photo obtained right after a disaster in Inje, Gangwon-do. Three landslide factors-topographic, forest type, and soil factors-w ere statistically analyzed through GIS overlap analysis between topographic map, forest type map, and soil map. The analysis showed that landslides occurred mainly between the inclination of $20^{\circ}$ and $35^{\circ}$, and needleleaf tree area is more vulnerable to a landslide. About soil properties, an area with shallow effective soil depth and parent material of acidic rock has a greater chance of landslide.

• 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.

• The Journal of Engineering Geology
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• v.14 no.2
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• pp.211-222
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• 2004

In this study, a probabilistic prediction model for debris flow occurrence was developed using a logistic regression analysis. The model can be applicable to metamorphic rocks and granite area. order to develop the prediction model, detailed field survey and laboratory soil tests were conducted both in the northern and the southern Gyeonggi province and in Sangju, Gyeongbuk province, Korea. The seven landslide triggering factors were selected by a logistic regression analysis as well as several basic statistical analyses. The seven factors consist of two topographic factors and five geological and geotechnical factors. The model assigns a weight value to each selected factor. The verification results reveal that the model has 90.74% of prediction accuracy. Therefore, it is possible to predict landslide occurrence in a probabilistic and quantitative manner.

• The Journal of Engineering Geology
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• v.34 no.1
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• pp.51-65
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• 2024

Landslides are natural disasters that causes significant property damage worldwide every year. In Korea, damage due to landslides is increasing owing to the effects of climate change, and it is important to identify the factors that increase the prevalence of landslides in order to reduce the damage they cause. Therefore, this study used a random forest model to analyze the importance of 14 factors in influencing landslide damage in a specific area of Chungju, Chungcheongbuk-do province, Korea. The random forest model performed accurately with an AUC of 0.87 and the most-important factors were ranked in the order of aspect, slope, distance to valley, and elevation, suggesting that topographic factors such as aspect and slope more greatly influence landslide damage than geological or soil factors such as rock type and soil thickness. The results of this study are expected to provide a basis for mapping and predicting landslide damage, and for research focused on reducing landslide damage.

• Economic and Environmental Geology
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• v.43 no.4
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• pp.381-391
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• 2010

Probabilistic prediction methods of landslides which have been developed in recent can be reliable with premise of detailed survey and analysis based on deep and special knowledge. However, landslide susceptibility should also be analyzed with some reliable and simple methods by various people such as government officials and engineering geologists who do not have deep statistical knowledge at the moment of hazards. Therefore, this study suggests an evaluation chart of landslide susceptibility with high reliability drawn by accurate statistical approaches, which the chart can be understood easily and utilized for both specialists and non-specialists. The evaluation chart was developed by a quantification method based on canonical correlation analysis using the data of geology, topography, and soil property of landslides in Korea. This study analyzed field data and laboratory test results and determined influential factors and rating values of each factor. The quantification analysis result shows that slope angle has the highest significance among the factors and elevation, permeability coefficient, porosity, lithology, and dry density are important in descending order. Based on the score assigned to each evaluation factor, an evaluation chart of landslide susceptibility was developed with rating values in each class of a factor. It is possible for an analyst to identify susceptibility degree of a landslide by checking each property of an evaluation factor and calculating sum of the rating values. This result can also be used to draw landslide susceptibility maps based on GIS techniques.

• Journal of Korean Society of Forest Science
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• v.96 no.6
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• pp.611-619
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• 2007

This study was carried out to analyze the landslide characteristics and forest environment factors on the landslide area of 7 national parks in korea. The results obtained from this study were summarized as follows; The total number of landslide occurrence was 44 areas. The average length of the landslides scar was 152 m, average width was 17 m. And the average area was $2,818m^2$. The factors influencing landslides were highly occurred in Metamorphic rock, mixed forest type. And also, $30{\sim}35^{\circ}$ in slope gradient, NE in slope aspect, slope higher than 1,000 m, concave (凹) type in vertical and cross slope, 0 ordered stream. The main factors affecting landslide area in stepwise regression analysis were sheet type in landslided shape, NE in slope aspect, 2 ordered stream, SE in slope aspect, slope gradient and complex slope in cross slope type in order of regression coefficient.

• The Journal of Engineering Geology
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• v.31 no.4
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• pp.701-718
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• 2021

Geological field surveys and a series of laboratory tests were conducted to obtain data related to landslides in Sancheok-myeon, Chungju-si, Chungcheongbuk-do, South Korea where many landslides occurred in the summer of 2020. The magnitudes of various factors' influence on landslide occurrence were evaluated using logistic regression analysis and an artificial neural network. Undisturbed specimens were sampled according to landslide occurrence, and dynamic cone penetration testing measured the depth of the soil layer during geological field surveys. Laboratory tests were performed following the standards of ASTM International. To solve the problem of multicollinearity, the variation inflation factor was calculated for all factors related to landslides, and then nine factors (shear strength, lithology, saturated water content, specific gravity, hydraulic conductivity, USCS, slope angle, and elevation) were determined as influential factors for consideration by machine learning techniques. Minimum-maximum normalization compared factors directly with each other. Logistic regression analysis identified soil depth, slope angle, saturated water content, and shear strength as having the greatest influence (in that order) on the occurrence of landslides. Artificial neural network analysis ranked factors by greatest influence in the order of slope angle, soil depth, saturated water content, and shear strength. Arithmetically averaging the effectiveness of both analyses found slope angle, soil depth, saturated water content, and shear strength as the top four factors. The sum of their effectiveness was ~70%.

• The Journal of Engineering Geology
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• v.14 no.4 s.41
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• pp.443-450
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• 2004

This study developed a prediction model of debris flow to predict a landslide probability on natural terrain composed of the Tertiary sedimentary and volcanic rocks using a logistic regression analysis. The landslides data were collected around Pohang, Gyeongbuk province where more than 100 landslides were occurred in 1998. Considered with basic characteristics of the logistic regression analysis, field survey and laboratory soil tests were performed for both slided points and not-slided points. The final iufluential factors on landslides were selected as six factors by the logistic regression analysis. The six factors are composed of two topographic factors and four geologic factors. The developed landslide prediction model has more than $90\%$ of prediction accuracy. Therefore, it is possible to make probabilistic and quantitative prediction of landslide occurrence using the developed model in this study area as well as the previously developed model for metamorphic and granitic rocks.