• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.100-100
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• 2003

The purpose of this study is to reveals spatial relationships between landslides and geospatial data set, map the landslide susceptibility using the relationships and verify the landslide susceptibility using the landslide occurrence data in Bosun area in 1998. Landslide locations were detected from aerial photography and field survey and topography, soil, forest, and land use data sets were constructed as a spatial database using GIS. As the landslide occurrence factors, slope, aspect, curvature and type of topography, texture, material, drainage and effective thickness of soil, type, age, diameter and density of wood and land use were used. Is extract the relationship between landslides and geospatial database, Bayesian probability methods, likelihood ratio and weight of evidence, were applied and the ratio and contrast value that is W$\^$＋/- W$\^$-/ were calculated. The landslide susceptibility index was calculated by summation of the likelihood ratio and contrast value and the landslide susceptibility maps were generated using the index. As a result, it is expected that spatial relationships between landslides and geospatial database is helpful to explain the characteristics of landslide and the landslide susceptibility map is used to reduce associated hazards, and to plan land use and construction.

• 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.

• Geosciences Journal
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• v.22 no.6
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• pp.1053-1067
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• 2018

This paper uses a probability-based approach to study the spatial relationships between landslides and their causative factors in the Mingchukur area, Bostanlik districts of Tashkent, Uzbekistan. The approach is based on digital databases and incorporates methods including probability analysis, spatial pattern analysis, and interactive mapping. First, an object-oriented conceptual model for describing landslide events is proposed, and a combined database of landslides and environmental factors is constructed by integrating various databases within a unifying conceptual framework. The frequency ratio probability model and landslide occurrence data are linked for interactive, spatial evaluation of the relationships between landslides and their causative factors. In total, 15 factors were analyzed, divided into topography, hydrology, and geology categories. All analyzed factors were also divided into numerical and categorical types. Numerical factors are continuous and were evaluated according to their $R^2$ values. A landslide susceptibility map was constructed based on conditioning factors and landslide occurrence data using the frequency ratio model. Finally, the map was validated and the accuracy showed the satisfactory value of 83.3%.

• Journal of Environmental Impact Assessment
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• v.15 no.6
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• pp.373-383
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• 2006

The spatial mapping of risk is very useful data in planning for disaster preparedness. This research presents a methodology for making the landslide life risk map in the Boeun area which had considerable landslide damage following heavy rain in August, 1998. We have developed a three-stage procedure in spatial data analysis not only to estimate the probability of the occurrence of the natural hazardous events but also to evaluate the uncertainty of the estimators of that probability. The three-stage procedure consists of: (i)construction of a hazard prediction map of "future" hazardous events; (ii) validation of prediction results and estimation of the probability of occurrence for each predicted hazard level; and (iii) generation of risk maps with the introduction of human life factors representing assumed or established vulnerability levels by combining the prediction map in the first stage and the estimated probabilities in the second stage with human life data. The significance of the landslide susceptibility map was evaluated by computing a prediction rate curve. It is used that the Bayesian prediction model and the case study results (the landslide susceptibility map and prediction rate curve) can be prepared for prevention of future landslide life risk map. Data from the Bayesian model-based landslide susceptibility map and prediction ratio curves were used together with human rife data to draft future landslide life risk maps. Results reveal that individual pixels had low risks, but the total risk death toll was estimated at 3.14 people. In particular, the dangerous areas involving an estimated 1/100 people were shown to have the highest risk among all research-target areas. Three people were killed in this area when landslides occurred in 1998. Thus, this risk map can deliver factual damage situation prediction to policy decision-makers, and subsequently can be used as useful data in preventing disasters. In particular, drafting of maps on landslide risk in various steps will enable one to forecast the occurrence of disasters.

• The Journal of Engineering Geology
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• v.17 no.3
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• pp.411-418
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• 2007

It is a very difficult thing to estimate an occurrence possibility location and hazard expectation area by landslide. The prediction difficulty of landslide occurrence has relativity in factor of various geological physical factors and contributions. However, estimation of landslide occurrence possibility and classification of hazard area became available correlation mechanism through analysis of landslide occurrence through landslide data analysis and statistical analysis. This study analyzed a damage possibility of a cultual heritage area due to landslide occurrence by a heavy rainfall. We make a landslide prediction map and tried to analysis of landslide occurrence possibility for the cultural heritage site. The study area chooses a temple of Silsang-Sa Baekjang-Am site and made a landslide prediction map. In landslide prediction map, landslide hazard possibility area expressed by occurrence probability and divided by each of probability degrees. This degree used to evaluate occurrence possibility for existence and nonexistence of landslide in the study site. For the prediction and evaluation of landslide hazard for the cultural heritage site, investigation and analysis technique which is introduced in this study may contribute an efficient management and investigation in the cultural heritage site, Korea.

• Journal of The Geomorphological Association of Korea
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• v.27 no.2
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• pp.65-80
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• 2020

This paper deals with risk assessment of life in a landslide-prone area by a GIS-based modeling method. Landslide susceptibility maps can provide a probability of landslide prone areas to mitigate or proper control this problems and to take any development plan and disaster management. A landslide inventory map of the study area was prepared based on past historical information and aerial photography analysis. A total of 550 landslides have been counted at the whole study area. The extracted landslides were randomly selected and divided into two different groups, 50% of the landslides were used for model calibration and the other were used for validation purpose. Eleven causative factors (continuous and thematic) 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 hazard analysis. The correlation between landslides and these factors, pixels were divided into several classes and frequency ratio was also extracted. Eventually, a landslide susceptibility map was constructed using a logistic regression model based on entire events. Moreover, the landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract a success rate curve. Based on the results, logistic regression produced an 85.18% accuracy, so we believed that the model was reliable and acceptable for the landslide susceptibility analysis on the study area. In addition, for risk assessment, vulnerability scale were added for social thematic data layer. The study area predictive landslide affected pixels 2,000 and 5,000 were also calculated for making a probability table. In final calculation, the 2,000 predictive landslide affected pixels were assumed to run. The total population causalities were estimated as 7.75 person that was relatively close to the actual number published in Korean Annual Disaster Report, 2006.

• 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.

• Economic and Environmental Geology
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• v.37 no.2
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• pp.207-223
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• 2004

The purpose of this study is to reveal spatial relationships between landslide and geospatial data set, to map the landslide susceptibility using the relationship and to verify the landslide susceptibility using the landslide occurrence data in Boun area in 1998. Landslide locations were detected from aerial photography and field survey, and then topography, soil, forest, and land cover data set were constructed as a spatial database using GIS. Various spatial parameters were used as the landslide occurrence factors. They are slope, aspect, curvature and type of topography, texture, material, drainage and effective thickness of soil. type, age, diameter and density of wood, lithology, distance from lineament and land cover. To calculate the relationship between landslides and geospatial database, Bayesian probability methods, weight of evidence. were applied and the contrast value that is >$W^{+}$->$W^{-}$ were calculated. The landslide susceptibility index was calculated by summation of the contrast value and the landslide susceptibility maps were generated using the index. The landslide susceptibility map can be used to reduce associated hazards, and to plan land cover and construction.

• Economic and Environmental Geology
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• v.33 no.4
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• pp.321-332
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• 2000

The purpose of this study is to analyze landslide that occurred in Yongin area in 1991 using spatial database. For this, landslide locations are detected from aerial photographs interpretation and field survey. The locations of landslide, topography, soil, forest and geology were constructed to spatial database using Geographic Information System (GIS). To establish occurrence factors of landslide, slope, aspect and curvature of topography were calculated from the topographic database. Texture, material, drainage and effective thickness of soil were extracted from the soil database, and type, age, diameter and density of wood were extracted from the forest database. Lithology was extracted from the geological database, and land use was classified from the TM satellite image. Landslide was analyzed using spatial correlation between the landslide and the landslide occurrence factors by bivariate probability methods. GIS was used to analyze vast data efficiently and statistical programs were used to maintain specialty and accuracy. The result can be used to prevention of hazard, land use planning and construction planning as basic data.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.2
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• pp.95-106
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• 2016

Landslide is one of the natural hazards, triggered by rainfall or earthquake and it leads to damage and loss of properties and lives especially in hilly and mountainous regions. Inventory maps of the area is of much importance in order to understand the landslide phenomena in detail, conduct further studies on landslide, prepare susceptibility map and minimize risk. Inventory maps of landslides can be constructed by several methods, using multiple images through visual interpretation, using algorithms in multi-spectral or SAR images or verification from field investigation. The possible methods were explored for Sindhupalchowk district of Nepal, which was struck by massive earthquake on 2015 and landslide inventory was prepared. The inventory was analyzed for its frequency over elevation, slope aspect and dominant soil classes and also the information value for their occurrence probability.