• Journal of Korean Society for Geospatial Information Science
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• v.13 no.1 s.31
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• pp.45-53
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• 2005

Korea is formed a mountainous district at 70% of country. Damages are occurred by landslide. It is increased annually by industrial development and extension of urban area. Serious damages occurred in Kangreng area of Gangwondo by typoon Rusa in August, 2002. Landslide hazard mapping needs to forecast and to manage landslide hazard area. GIS fits landslide hazard mapping, to reduce of time, manpower and costs, to investigate a board area, to do a spatial forecast model in study site. Variety of methods is for landslide hazard mapping. A study use root strength reinforcement model. This model use factors such as topography of slope, soil depth, soil properties, and vegetation of slope. Landslide hazard map is made by this model.

• Journal of Korean Society of Forest Science
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• v.97 no.3
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• pp.332-339
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• 2008

This study was carried out to develop the score table for prediction of landslide hazard in Gyeongsangbuk-Do province. It was studied to 172 places landslided in 23 cities and counties of Gyeongsangbuk-Do province. An analyze of the score table for landslide hazard was carried out through the multiple statistics of quantification method (I) by the computer. Factors effected to landslide occurrence quantity were shown in order of slope position, slope length, bedrock, aspect, forest age, slope form and slope. As results of the development of score table for prediction of landslide hazard in Gyeongsangbuk-Do province, total score range was divided that 107 under is stable area (IV class), 107~176 is area with little susceptibility to landslide (III class), 177~246 is area with moderate susceptibility to landslide (II class), above 247 area with severe susceptibility to landslide (I class).

• Journal of the Korean GEO-environmental Society
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• v.18 no.2
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• pp.21-27
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• 2017

In order to evaluate landslide susceptibility in Yanbian region, this study analyzed 7 factors related to landslide occurrence, such as soil, geology, land use, slope, slope aspect, fault and river by Analytic Hierarchy Process (AHP), and calculated the weights of these 7 hazard-induced factors, determined the internal weights and the relative weights between various factors. According to these weights, combining the Remote Sensing technology (RS) with Geographic Information System technology (GIS), the selected area was evaluated by using GIS raster data analysis function, then landslide susceptibility chart was mapped out. The comprehensive analysis of AHP and GIS showed that there has unstable area with the potential risk of sliding in the research area. The result of landslide susceptibility agrees well with the historical landslides, which proves the accuracy of adopted methods and hazard-induced factors.

• 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.

• 한국지형공간정보학회:학술대회논문집
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• 2005.08a
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• pp.61-72
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• 2005

The landslide risk assessment process consists of hazard risk assessment and vulnerability analysis. landslide hazard risk is location dependent. Therefore, maps and spatial technologies such as GIS are very important components of the risk assessment process. This paper discusses the advantages of using GIS technology in the risk assessment process and illustrates the benefits through case studies of live projects undertaken. The goal of this study is to generate a map of landslide vulnerability map by analysis of static natural factors with GIS. A simple and efficient algorithm is proposed to generate a landslide potentialities map from DEM and existing maps. The categories of controlling factors for landslides, aspect of slope, soil, vegetation are defined. The weight values for landslide potentialities are calculated from AHP method. Slope and slope-direction are extracted from DEM, and soil informations are extracted from digital soil map. Also, vegetation informations are extracted from digital vegetation map. Finally, as overlaying, landslide potentialities map is made out, and it is verified with landslide place.

• Journal of the Korean earth science society
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• v.26 no.2
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• pp.114-128
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• 2005

Fuzzy information representation of multi-source spatial data is applied to landslide hazard mapping. Information representation based on frequency ratio and non-parametric density estimation is used to construct fuzzy membership functions. Of particular interest is the representation of continuous data for preventing loss of information. The non-parametric density estimation method applied here is a Parzen window estimation that can directly use continuous data without any categorization procedure. The effect of the new continuous data representation method on the final integrated result is evaluated by a validation procedure. To illustrate the proposed scheme, a case study from Jangheung, Korea for landslide hazard mapping is presented. Analysis of the results indicates that the proposed methodology considerably improves prediction capabilities, as compared with the case in traditional continuous data representation.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.129-131
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• 2003

The aim of this study is to evaluate the hazard of landslides at Penang, Malaysia, using a Geographic Information System (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. The topographic and geologic data and satellite image were collected, processed and constructed into a spatial database using GIS and image processing. The used factors that influence landslide occurrence are topographic slope, topographic aspect topographic curv ature and distance from drainage from topographic database, geology and distance from lineament from the geologic database, land use from TM satellite image and vegetation index value from SPOT satellite image. Landslide hazardous area were analysed and mapped using the landslide-occurrence factors by probability - likelihood ratio - method. The results of the analysis were verified using the landslide location data. The validation results showed satisfactory agreement between the hazard map and the existing data on landslide location.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.4
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• pp.81-90
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• 2005

Based on the landslide hazard scoring system of Korea Forest Research Institute, a GIS model for predicting landslide hazards was developed. The risk of landslide hazards was analyzed as the function of 7 environmental site factors for the terrain, vegetation, and geological characteristics of the corresponding forest stand sites. Among the environmental factors, slope distance, relative height and shapes of slopes were interpreted using the forestland slope interpretation module developed by Chung et al. (2002). The program consists of three modules for managing spatial data, analyzing landslide hazard and report-writing, A performance test of the model showed that 72% of the total landslides in Youngin-Ansung landslides area took place in the highly vulnerable zones of grade 1 or 2 of the landslide hazard scoring map.

• Geomechanics and Engineering
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• v.24 no.5
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• pp.407-418
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• 2021

Due to the complexity of the causes of the sliding mass instabilities, landslide susceptibility and hazard evaluation are difficult, but they can be more carefully considered and regionally evaluated by using new programming technologies to minimize the hazard. This study aims to evaluate the landslide hazard zonation in the Tabriz region, Iran. A fuzzy logic-based multi-criteria decision-making method was proposed for susceptibility analysis and preparing the hazard zonation maps implemented in MATLAB programming language and Geographic Information System (GIS) environment. In this study, five main factors have been identified as triggering including climate (i.e., precipitation, temperature), geomorphology (i.e., slope gradient, slope aspect, land cover), tectonic and seismic parameters (i.e., tectonic lineament congestion, distribution of earthquakes, the unsafe radius of main faults, seismicity), geological and hydrological conditions (i.e., drainage patterns, hydraulic gradient, groundwater table depth, weathered geo-materials), and human activities (i.e., distance to roads, distance to the municipal areas) in the study area. The results of analyses are presented as a landslide hazard map which is classified into 5 different sensitive categories (i.e., insignificant to very high potential). Then, landslide susceptibility maps were prepared for the Tabriz region, which is categorized in a high-sensitive area located in the northern parts of the area. Based on these maps, the Bozgoosh-Sahand mountainous belt, Misho-Miro Mountains and western highlands of Jolfa have been delineated as risk-able zones.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.3
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• pp.63-68
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• 2007

The LRA(Logistic Regression Analysis) conducts a quantitative analysis by collecting a lot of samples and the AHP(Analytic Hierarchy Program) makes use of expert decision influenced by subjective judgment to a certain degree. This study is to suggest a combination method in mapping landslide hazard by giving equal weight for the result of LRA and AHP. Topographic factors(slope, aspect, elevation), soil dram, soil depth and land use were adopted to classify landslide hazard areas. The three methods(LRA, AHP, the combined approach) was applied to a $520km^2$ 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. Further studies are recommended to find the optimal combining weight of LRA and AHP with more landslide data.