• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.11 no.2
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• pp.53-58
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• 1993

Landslides, failure of slope stability by natural or artificial factors, occur loss of life and properties. Recently, landslides hazard area predict statistical methods and field measurements, but there are so many difficulties to find the occurrence system because of its complexity. To predict the landslide harvard region, model area is the Seodong in Pusan where occurred landslides. Database of ground height made the each topography in map scale of 1 : 25,000, 1 : 10,000, 1 : 5,000 and 1 : 1,200. Authors knew to landslide hazard area by the weight of ground height data and slope angle data. Finally, aerial photo analysis is possible find landslide hazard area.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.6
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• pp.597-607
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• 2016

The goal of this study was to analyze landslide susceptibility using two different models and compare the results. For this purpose, a landslide inventory map was produced from a field survey, and the inventory was divided into two groups for training and validation, respectively. Sixteen landslide conditioning factors were considered. The relationships between landslide occurrence and landslide conditioning factors were analyzed using the FR (Frequency Ratio) and EBF (Evidential Belief Function) models. The LSI (Landslide Susceptibility Index) maps that were produced were validated using the ROC (Relative Operating Characteristics) curve and the SCAI (Seed Cell Area Index). The AUC (Area under the ROC Curve) values of the FR and EBF LSI maps were 80.6% and 79.5%, with prediction accuracies of 72.7% and 71.8%, respectively. Additionally, in the low and very low susceptibility zones, the FR LSI map had higher SCAI values compared to the EBF LSI map, as high as 0.47%p. These results indicate that both models were reasonably accurate, however that the FR LSI map had a slightly higher accuracy for landslide susceptibility mapping in the study area.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.310-313
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• 2004

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 from field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land use from TM satellite images; and the vegetation index value from SPOT satellite images. Landslide hazardous area were analysed and mapped using the landslide-occurrence factors by logistic regression model. The results of the analysis were verified using the landslide location data and compared with probabilistic model. The validation results showed that the logistic regression model is better prediction accuracy than probabilistic model.

• 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.

• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.45-74
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• 2018

Landslide is a natural hazard that threats lives and properties in many areas around the world. Landslides are difficult to recognize, particularly in rainforest regions. Thus, an accurate, detailed, and updated inventory map is required for landslide susceptibility, hazard, and risk analyses. The inconsistency in the results obtained using different features selection techniques in the literature has highlighted the importance of evaluating these techniques. Thus, in this study, six techniques of features selection were evaluated. Very-high-resolution LiDAR point clouds and orthophotos were acquired simultaneously in a rainforest area of Cameron Highlands, Malaysia by airborne laser scanning (LiDAR). A fuzzy-based segmentation parameter (FbSP optimizer) was used to optimize the segmentation parameters. Training samples were evaluated using a stratified random sampling method and set to 70% training samples. Two machine-learning algorithms, namely, Support Vector Machine (SVM) and Random Forest (RF), were used to evaluate the performance of each features selection algorithm. The overall accuracies of the SVM and RF models revealed that three of the six algorithms exhibited higher ranks in landslide detection. Results indicated that the classification accuracies of the RF classifier were higher than the SVM classifier using either all features or only the optimal features. The proposed techniques performed well in detecting the landslides in a rainforest area of Malaysia, and these techniques can be easily extended to similar regions.

• Journal of the Korean GEO-environmental Society
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• v.20 no.6
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• pp.15-22
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• 2019

In this study, the hazard areas are identified by using the Newmark displacement model, which is a predictive model for identifying the areas at risk of landslide triggered by earthquakes, based on the results of field survey and laboratory test, and literature data. The Newmark displacement model mainly utilizes earthquake and slope related data, and the safety of slope stability derived from LSMAP, which is a landslide prediction program. Backyang Mt. in Busan where the landslide has already occurred, was chosen as the study area of this research. As a result of this study, the area of landslide prone zone identified by using the Newmark displacement model without earthquake factor is about 1.15 times larger than that identified by using LSMAP.

• 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.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.5
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• pp.1-13
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• 2022

The damages caused by landslides are increasing worldwide due to climate change. In Korea, damages from landslides occur frequently, making it necessary to develop the effective response strategies. In particular, there is a lack of countermeasures against landslides in cultural heritage areas. The purpose of this study was to spatially analyze the relationship between Buyeo-gun's cultural heritage and landslide susceptible areas in Buyeo-gun, Chungcheongnam-do, which has a long history. Nine spatial distribution models were used to evaluate the landslide susceptibility, and the ensemble method was applied to reduce the uncertainty of individual model. There were 17 cultural heritages belonging to the landslide susceptible area. As a result of calculating the area ratio of the landslide susceptible area for cultural heritages, the cultural heritages with 100% of the area included in the landslide susceptible area were "Standing statue of Maae in Hongsan Sangcheon-ri" and "Statue of King Seonjo." More than 35% of "Jeungsanseong", "Garimseong", and "Standing stone statue of Maitreya Bodhisattva in Daejosa Temple" belonged to landslide susceptible areas. In order to effectively prevent landslide damage, the application of landslide prevention measures should be prioritized according to the proportion belonging to the landslide susceptible area. Since it is very difficult to restore cultural properties once destroyed, preventive measures are required before landslide damage occurs. The approach and results of this study provide basic data and guidelines for disaster response plans to prevent landslides in Buyeo-gun.

• Journal of the Korean Geotechnical Society
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• v.28 no.4
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• pp.23-33
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• 2012

Recently, landslide disasters caused by severe rain storms and typhoons have been frequently reported. Due to the geomorphologic characteristics of Korea, considerable portion of urban area and infrastructures such as road and railway have been constructed near mountains. These infrastructures may encounter the risk of landslide and debris flow. It is important to evaluate the highly risky locations of landslide and to prepare measures for the protection of landslide in the process of construction planning. In this study, a landslide-risk prediction equation is proposed based on the statistical analysis of 423 landslide data set obtained from field surveys, disaster reports on national road, and digital maps of landslide area. Each dataset includes geomorphologic characteristics, soil properties, rainfall information, forest properties and hazard history. The comparison between the result of proposed equation and actual occurrence of landslide shows 92 percent in the accuracy of classification. Since the input for the equation can be provided within short period and low cost, and the results of equation can be easily incorporated with hazard map, the proposed equation can be effectively utilized in the analysis of landslide-risk for large mountainous area.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.4
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• pp.33-39
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• 2008

Typhoon Lusa in 2002 and Typhoon Maemi in 2003 caused the worst damage of landslide and debris flow to Gangwon-do. This damage includes severe damage in riverside road. The damage register indicates that this damage is concentrated on mountain areas in Gangwon-do. In recent years, the studies on GIS application to predicting landslide and debris flow have been progressing actively. Landslide risk map managed by The Forest Service is the representative one. In this study, we generated landslide and debris flow hazard maps using statistical analysis and deterministic analysis in Gangnung area where Typhoons caused severe damage to riverside roads. We built damage point GIS DB from damage registers of National Road Maintenance Agency and field survey, and verified accuracy of landslide and debris flow hazard maps using GIS methods.