• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.6
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• pp.579-587
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• 2015

Landslides are major natural geological hazards that result in a large amount of property damage each year, with both direct and indirect costs. Many researchers have produced landslide susceptibility maps using various techniques over the last few decades. This paper presents the landslide susceptibility results from the geographically weighted regression model using remote sensing and geographic information system data for landslide susceptibility in the Inje area of South Korea. Landslide locations were identified from aerial photographs. The eleven landslide-related factors were calculated and extracted from the spatial database and used to analyze landslide susceptibility. Compared with the global logistic regression model, the Akaike Information Criteria was improved by 109.12, the adjusted R-squared was improved from 0.165 to 0.304, and the Moran’s I index of this analysis was improved from 0.4258 to 0.0553. The comparisons of susceptibility obtained from the models show that geographically weighted regression has higher predictive performance.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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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.

• Spatial Information Research
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• v.3 no.1
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• pp.81-90
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• 1995

Landslide data base system is necessitated to make a mid-long term master plan to prevent landslide from past landslide data and their statistical analysis. This paper emphasis on application of the efficient management system of GIS to reduce landslide disasters basis on the result of survey analysis of landslide problems. In this paper explains landslide data base system by the cause of landslide from past landslide data & application of GIS to it.

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.571-583
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• 2022

On 7 August 2020, a large-scale catastrophic landslide was triggered by extreme rainfall at Osan village, Gokseong County, South Jeolla Province, South Korea. The initiation mechanism of the Gokseong landslide was different from those typical landslides that occurred in South Korea. Despite the relatively low elevation and slope degree, the landslide had a long runout distance of about 640 m over a total vertical distance of 90 m. A detailed field investigation and chemical analysis were conducted to understand the possible mechanisms for the high-speed and long-runout behavior of the landslide. The terrain controlled the motion behavior of the landslide and the seepage was observed at the whole landslide body. The clay-rich soils covered on granite bedrock of the landslide deposition area from the rice paddy field to the landslide crown. The results of this study may provide basic data for further research on the mechanisms for landslide initiation and propagation.

• Economic and Environmental Geology
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• v.33 no.4
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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.

• 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.

• Journal of The Geomorphological Association of Korea
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• v.27 no.3
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• pp.87-103
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• 2020

The purpose of this study is to evaluate the risk of cropland and man-made infrastructures in a landslide-prone area using a GIS-based method. To achieve this goal, a landslide inventory map was prepared based on aerial photograph analysis as well as field observations. A total of 550 landslides have been counted in the entire study area. For model analysis and validation, extracted landslides were randomly selected and divided into two groups. The landslide causative factors such as slope, aspect, curvature, topographic wetness index, elevation, forest type, forest crown density, geology, land-use, soil drainage, and soil texture were used in the analysis. Moreover, to identify the correlation between landslides and causative factors, pixels were divided into several classes and frequency ratio was also extracted. A landslide susceptibility map was constructed using a bayesian predictive model (BPM) based on the entire events. In the cross validation process, the landslide susceptibility map as well as observation data were plotted with a receiver operating characteristic (ROC) curve then the area under the curve (AUC) was calculated and tried to extract a success rate curve. The results showed that, the BPM produced 85.8% accuracy. We believed that the model was acceptable for the landslide susceptibility analysis of the study area. In addition, for risk assessment, monetary value (local) and vulnerability scale were added for each social thematic data layers, which were then converted into US dollar considering landslide occurrence time. Moreover, the total number of the study area pixels and predictive landslide affected pixels were considered for making a probability table. Matching with the affected number, 5,000 landslide pixels were assumed to run for final calculation. Based on the result, cropland showed the estimated total risk as US $ 35.4 million and man-made infrastructure risk amounted to US $ 39.3 million.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.119-132
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• 2012

Recently, forest soil sediment disasters resulting from locally concentrated heavy rainfall have been occurring frequently in steep slope areas. The importance of landslide hazard map is emerging to analyze landslide vulnerable areas. This study was carried out to develop HyGIS-Landslide based on Hydro Geographic Information System in order to analyze forest soil sediment disaster in the mountainous river basin. HyGIS-Landslide is one of HyGIS components designed by considering the landslide hazard criteria of Korea Forest Service. It could show the distribution of landslide hazard areas after calculating the spatial data. In this system, the user could reset the weight of hazard criteria to reflect the regional characteristics of the landslide area. This component provided user interface that could make the latest spatial data available in the area of interest. HyGIS-Landslide could be applied to the surveyor's compensation score and it was possible to reflect the landslide risk exactly through it. Also, it could be used in topographic analysis techniques providing spatial analysis and making topographical parameters in HyGIS. Finally the accuracy could be acquired by calculating the landslide hazard grade map and landslide mapping data. This study applied HyGIS-Landslide at the Gangwon-do province sample site. As a result, HyGIS-Landslide could be applied to a decision support system searching for mountainous disaster risk region; it could be classified more effectively by re-weighting the landslide hazard criteria.

• Economic and Environmental Geology
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• v.33 no.6
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• pp.547-556
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• 2000

The purpose of this study is to analyze shear fracture system using landslide location occurred 1998 at Janghung area. For the geological implication, foliation was surveyed and analyzed, and location of landslide, geological structure and topography were constructed into spatial database using GIS. With the constructed spatial database, shear fracture system was assessed by the relation analysis between strike and dip of the foliation and aspect and slope of the topography. We compared strike and dip of foliation and aspect and slope of topography and recognized the typical fracture pattern, strike and dip of joint, that coincided with shear fracture system. The result tells us that foliation of gneiss has geometrical relation to joint or fault that leading landslide. GIS was used to analyze vast data efficiently and the result can be used to assess the landslide susceptibility as important factor.

• Proceedings of the KSRS Conference
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• v.2
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• pp.547-550
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• 2006

This study is to suggest a methodology to produce landslide hazard map by combining LRA (Logistic Regression Analysis) and AHP (Analytic Hierarchy Program) Approach. Topographic factors (slope, aspect, elevation), soil drain, soil depth and land use were adopted to classify landslide hazard areas. The method was applied to a 520 $km^2$ region located in the middle of South Korea which have occurred 39 landslides during 1999 and 2003. The suggested method showed 58.9 % matching rate for the real landslide sites comparing with the classified areas of high-risk landslide while LRA and AHP showed 46.1 % and 48.7 % matching rates respectively.