• 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.

• 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.

• 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.

• The Journal of Engineering Geology
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• v.21 no.4
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• pp.281-293
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• 2011

We investigated the causes and characteristics of a landslide at Hwangryeong Mountain, Busan, based on aerial photos, annual precipitation data, rock fracture patterns, and geomorphic features using GIS Software, and a statistical analysis of tilted trees. The analyzed slope shows evidence of a previous slope failure event and the possibility of future failures. Although the NW-SE trending slope was relatively stable until 1975, a large-scale slope failure occurred between 1975 and 1985 due to complex factors, including favorably oriented geologic structures, human activity, and heavy rain. This indicates that a detailed study of geologic structures, slope stability, and rainfall characteristics is important for slope cuttings that could be a major factor and cause of urban landsliding events. The statistic analysis of tilted trees shows a slow progressive creeping type of mass wasting with rock falls oblique to the dip of the slope, with the slope having moved towards the west since 1985. A concentration of tree tilting has developed on the northwestern part of the slope, which could reach critical levels in the future. The analysis of deformed trees is a useful tool for understanding landslides and for predicting and preventing future landslide events.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.3
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• pp.23-29
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• 2015

Due to the characteristics of landslide disasters including debris flow, the rapid speed to downward and difficulty to respond or evacuate from them, it is imperative to identify their potential hazards and prepare the reduction plans. However, the current landslide hazards generated by a variety of methods has been raised its accuracy because of the complexity of input data and their analyses, and the simplification of the landslide model. The main objective of this study is, therefore, to evaluate the preliminary landslide hazard based on the identification of geomorphological features. Especially, two methodologies based on the statistics of the directional data, Vector dispersion and Planarity analyses, are used to find some relationships between geomorphological characteristics and the landslide hazard. Results show that both methods well discriminate geomorphological features between stable and unstable domains in the landslide areas. Geomorphological features are closely related to the landslide hazard and it is imperative to maximize their characteristics by adapting multiple models rather than individual model only. In conclusions, the mechanism of landslide is not determined solely by a simple cause but the complex natural phenomenon caused by the interactions of the numerous factors and it is of primary importance to require additional researches for the outbreaking mechanism that are based on various methodologies.

• The Journal of Engineering Geology
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• v.18 no.2
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• pp.191-196
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• 2008

This study is a case study for application of the RTI warning model to Korea which was previously developed to predict landslide potential and occurrence time during a rainfall event. The rainfall triggering index (RTI) is defined as the product of the rainfall intensity I (mm/hr) and the effective accumulated rainfall $R_t$ (mm). This index is used to evaluate the landslide and debris-flow occurrence potential at time t during a rainfall event. The upper critical value ($RTI_{UC}$) of RTI and the lower critical value ($RTI_{LC}$) of RTI can be determined by historical rainfall data of a certain area. When the rainfall intensity exceeds the upper critical value, there are high potential to occur land-slides. The analysis result can predict landslide occurrence time of an area during a rainfall event as well as land-slide potential. The result can also be used as an important data to issue early-warning of landslides. In order to apply the RTI warning model to Korea this study analyzed rainfall data and landslides data in Inje county, Gangwon province, Korea from July 13 to July 19, 2006. According to the analysis result, the rainfall intensity exceeded the upper critical value 23 hours ago, 11 hours ago, and 9 hours ago from 11:00 in the morning, July 16. Therefore, landslide warnings would be issued three times for people evacuation for avoiding or reducing hurts and dam-ages from landslides in mountainous areas of Inje.

• Economic and Environmental Geology
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• v.52 no.2
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• pp.199-212
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• 2019

In the machine learning techniques, the sampling strategy of the training data affects a performance of the prediction model such as generalizing ability as well as prediction accuracy. Especially, in landslide susceptibility analysis, the data sampling procedure is the essential step for setting the training data because the number of non-landslide points is much bigger than the number of landslide points. However, the previous researches did not consider the various sampling methods for the training data. That is, the previous studies selected the training data randomly. Therefore, in this study the authors proposed several different sampling methods and assessed the effect of the sampling strategies of the training data in landslide susceptibility analysis. For that, total six different scenarios were set up based on the sampling strategies of landslide points and non-landslide points. Then Random Forest technique was trained on the basis of six different scenarios and the attribute importance for each input variable was evaluated. Subsequently, the landslide susceptibility maps were produced using the input variables and their attribute importances. In the analysis results, the AUC values of the landslide susceptibility maps, obtained from six different sampling strategies, showed high prediction rates, ranges from 70 % to 80 %. It means that the Random Forest technique shows appropriate predictive performance and the attribute importance for the input variables obtained from Random Forest can be used as the weight of landslide conditioning factors in the susceptibility analysis. In addition, the analysis results obtained using specific sampling strategies for training data show higher prediction accuracy than the analysis results using the previous random sampling method.

• The Journal of Engineering Geology
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• v.32 no.1
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• pp.27-40
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• 2022

Every year landslides cause serious casualties and property damages around the world. As the accurate prediction of landslides is important to reduce the fatalities and economic losses, various approaches have been developed to predict them. Prediction methods can be divided into landslide susceptibility analysis, landslide hazard analysis and landslide risk analysis according to the type of the conditioning factors, the predicted level of the landslide dangers, and whether the expected consequence cased by landslides were considered. Landslide susceptibility analyses are mainly based on the available landslide data and consequently, they predict the likelihood of landslide occurrence by considering factors that can induce landslides and analyzing the spatial distribution of these factors. Various qualitative and quantitative analysis techniques have been applied to landslide susceptibility analysis. Recently, quantitative susceptibility analyses have predominantly employed the physically based model due to high predictive capacity. This is because the physically based approaches use physical slope model to analyze slope stability regardless of prior landslide occurrence. This approach can also reproduce the physical processes governing landslide occurrence. This review examines physically based landslide susceptibility analysis approaches.

• 한국공간정보시스템학회:학술대회논문집
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• 2007.06a
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• pp.413-423
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• 2007

우리나라의 자연재해는 기상학적 자연현상에 의해 주로 발생되고 있으며 그 발생원인은 태풍, 호우, 폭풍, 폭풍우, 재설, 폭풍성 우박, 해일 및 기타(낙뢰, 돌풍, 설해, 결빙, 지진 등을 포함)로 구분되며 이중 발생빈도가 가장 높은 것은 강우에 의한 재해로 전체 재해발생 원인 중 약 80%로 대부분을 차지하고 있다. 특히 사면붕괴와 관련된 자연재해(산사태, 옹벽붕괴, 매몰 등)는 최근 국지성 집중호우를 포함하여 호우의 집중 강도가 높아지는 등 기상학적 원인에 의해 매년 발생하고 있다. 따라서 우리나라에서 발생되는 자연재해와 관련한 사면붕괴의 특성을 강우특성에 따라 조사 분석할 필요가 있으며 이에 적합한 대책들이 더욱 필요하다. 이 연구에서는 산사태 유발인자와 강우조건을 고려하여 산사태 잠재가능성을 평가하고 산사태 취약지역을 분석하여 지역적인 강우특성을 고려한 산사태 가능성을 평가하였다.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2008.06a
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• pp.301-305
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• 2008

강원도 지역은 대부분의 지형이 산지로 이루어져 있고, 최근 심각해지고 있는 기후 변화로 인해 집중호우가 잦아지면서 이로 인한 산사태 피해 또한 증가하고 있는 상황이다. 하지만 기존에 이루어져왔던 직접 측량 방식은 많은 시간과 인력이 소모되고, 접근성의 제약으로 인해 곳곳에서 발생하는 모든 산사태를 체계적으로 감지하기에는 무리가 따른다. 따라서 효율적인 산사태 감지와 신속한 대처를 위해 최근 인공위성을 이용한 원격 탐측이 주목을 받고 있으며, 특히 고해상도 영상 레이더(Synthetic Aperture Radar, SAR)는 태양광의 유무나 대기 조건에 상관없이 상시 관측이 가능하다는 장점으로 인해 그 수요가 점점 늘어나고 있는 추세이다. 본 연구에서는 산사태가 집중되는 지역인 강원도 강릉 부근(N $37^{\circ}.30'{\sim}38{\circ}.10'$, E $128^{\circ}.05'{\sim}129^{\circ}.00'$)을 대상으로 SAR 영상 처리 기법 중 하나인 간섭기법(Interferometric SAR, InSAR)를 통해 생성되는 coherence 영상을 분석하여 93년 7월 27일과 동년 9월 9일 사이에 발생한 산사태 피해 지역을 추정하였다.