• Journal of Climate Change Research
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• v.2 no.1
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• pp.43-54
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• 2011

Recently, due to severe rainfall by the global climate change, natural disasters such as landslide had also been increased rapidly all over the world. Therefore, it has been very necessary to assess vulnerability of landslide and prepare adaptation measures to future climate change. In this study, we employed sensitivity, exposure and adaptative capacity as criteria for assessing the vulnerability of landslide due to climate change. Spatial database for the criteria was constructed using GIS technology. And vulnerability maps on the entire Korea of past and future were made based on the database. As a result, highly vulnerable area for landslide was detected in most area of Gangwon-do, the east of Gyeonggi-do, and southeast of Jeollanam-do, and the southwest of Gyeongsangnam-do. The result of landslide vulnerability depends on time shows that degree of very low class and low class were decreased and degree of moderate, high, and very high were increase from past to the future. Especially, these three classes above low class were significantly increased in the result of far future.

• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.4
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• pp.116-127
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• 2011

This study conducted landslide susceptibility analysis using logistic regression. The performance of prediction model needs to be evaluated considering two aspects such as a goodness of fit and a prediction accuracy. Thus to gain more objective prediction results in this study, the prediction performance of the applied model was evaluated considering two such evaluation aspects. The selected study area is located between Inje-eup and Buk-myeon in the middle of Kwangwon. Landslides in the study area were caused by heavy rain in 2006. Landslide causal factors were extracted from topographic map, forest map and soil map. The evaluation of prediction model was assessed based on the area under the curve of the cumulative gain chart. From the results of experiments, 87.9% in the goodness of fit and 84.8% in the cross validation were evaluated, showing good prediction accuracies and not big difference between the results of the two evaluation methods. The results can be interpreted in terms of the use of environmental factors which are highly related to landslide occurrences and the accuracy of the prediction model.

• Korean Journal of Environment and Ecology
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• v.22 no.2
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• pp.119-127
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• 2008

This research managed to establish the space information on incidence factors of landslide targeting Bukhansan National Park and aimed at suggesting a basic data for disaster prevention of a landslide for the period to come in Bukhansan National Park through drawing up the map indicating vulnerability to a landslide and ecological risks by the use of overlay analysis and adding-up estimation matrix analysis methods. This research selected slope angle, slope aspect, slope length, drainage, vegetation index(NDVI) and land use as an assessment factor of a landslide and constructed the spatial database at a level of '$30m\times30m$' resolution. The analysis result was that there existed high vulnerability to a landslide almost all over Uidong and Dobong valleys. As for ecological risks, Dobong valley, Yongueocheon valley, Jeongneung valley and Pyeongchang valley were analyzed to be higher, so it is judged that the impact on a landslide risk should be also considered in time of establishing a management plan for these districts for the time to come.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.06a
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• pp.291-293
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• 2010

최근 발생하고 있는 국지성 집중호우 및 돌발홍수로 인해 강원도와 경상북도 등을 중심으로 산지하천유역의 산사태 피해가 급증하고 있으며 발생면적은 연평균 402ha에 이르며 연평균 피해면적은 80년대에 비해 2000년대 들어 3배 이상 증가한 것으로 보고 되고 있다. 본 연구에서는 산지하천 유역의 토사유출재해 취약성 분석을 위해 GEOMania GMMap 기반으로 구동되는 산사태 분석모듈(HyGIS-Landslide)을 개발하였다 HyGIS-Landslide는 산림청의 산사태 위험지도 제작에 사용된 위험지역 평가기준을 참조하였으며 DEM을 이용하여 경사인자 및 사면인자를 생성하고 수치지질도, 수치임상도 산림입지도 등과의 연산을 통해 위험등급에 대한 분류결과를 제시한다. 또한, 과거 산사태 발생지역에 대한 맵핑 경과가 존재하는 경우 산사태 위험지역 분류결과를 과거 사상과 중첩하여 분류정확도를 확인할 수 있도록 제작되었다.

• The Journal of Engineering Geology
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• v.26 no.1
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• pp.23-32
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• 2016

This study evaluates landslide susceptibility using statistical analysis by SVM (support vector machine) and the illite index of clay minerals extracted from ASTER(advanced spaceborne thermal emission and reflection radiometer) imagery which can be use to create mineralogical mapping. Landslide locations in the study area were identified from aerial photographs and field surveys. A GIS spatial database was compiled containing topographic maps (slope, aspect, curvature, distance to stream, and distance to road), maps of soil properties (thickness, material, topography, and drainage), maps of timber properties (diameter, age, and density), and an ASTER satellite imagery (illite index). The landslide susceptibility map was constructed through factor correlation using SVM to analyze the spatial database. Comparison of area under the curve values showed that using the illite index model provided landslide susceptibility maps that were 76.46% accurate, which compared favorably with 74.09% accuracy achieved without them.

• Journal of Cadastre & Land InformatiX
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• v.44 no.2
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• pp.113-124
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• 2014

Recently landslide occurs frequently by heavy rainfall, therefore there area many studies to analyze the vulnerable district of landslide and forecast the occurrence of landslide. This study analyzed soil characteristics in the occurrence district of landslide and the occurrence possibility of landslide ranked high in well draining soil as the result of frequency ratio according to the characteristics of drainage. Also as the result of frequency ratio of slope derived from DEM data, the occurrence possibility of landslide ranked high in slope range of $20{\sim}40^{\circ}$. And Also as the result of frequency ratio of aspect by geospatial analysis, the occurrence possibility of landslide ranked high in north aspect. Also, it is possible to evaluate the vulnerability of landslide by overlapping frequency ratio of the drainage of soil, slope and aspect. And future prediction ratio of landslide occurrence can be evaluated by performing the analysis and validation process respectively on the subject of the occurrence district of landslide.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.1
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• pp.27-39
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• 2008

Landslides cause enormous economic losses and casualties. Korea has mountainous regions and heavy slopes in most parts of the land and has consistently built new roads and large-scale housing complexes according to its industrial and urban growth. As a result, the damage from landslides becomes greater every year. In summer, landslides frequently occur due to local torrential rains and storms. It is critical to predict the potential areas of landslides in advance and to take preventive measures to minimize consequences and to protect property and human life. The previous study on landslides mostly focused on identifying the causes of landslides in the areas where they occurred, and on analyzing landslide vulnerability around the areas without considering rainfall conditions. Thus there were not enough evaluations of the direct risk of landslides to human life. In this study, potentially risky areas for landslides were identified using the GIS data in order to evaluate direct risk on farmlands, roads, and artificial structures that were closely connected to human life. A map of landslide risk was made taking into account rainfall conditions, and a land use map was also drawn with satellite images and digital maps. Both maps were used to identify potentially risky areas for landslides.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.3
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• pp.69-76
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• 2007

Kangwondo area is mountainous and landslide is easily happened 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. Another reason behind landslide is the continuous forest fire in these several years. Since the surface of the earth has been changed by the fire, when rainfall comes, landslide just happens easily. Also, it is reported that landcover condition, excepted rainfall condition, is the most effect for determining landslide susceptibility area. In this study, it is determined a landslide vulnerable area and landcover information is extracted from four satellite image(Landsat TM), about the landslide vulnerable area, which is pictured for each year. And which distribution change is analyzed. also, NDVI picture is made and distribution change of vegetation vitality is analyzed to study that change of landcover have a effect on landslide. As a result, could know that forest and NDVI are decreasing in landslide vulnerable area.

• 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.