• 환경영향평가
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• 제15권6호
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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.

• 지질공학
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• 제17권3호
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• pp.411-418
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• 2007

산사태가 일어날 지점을 예측한다든지 사태물질로 인한 피해 예상지역을 알아내는 것은 쉬운 일이 아니다. 이는 산사태를 발생시키는 요인들이 여러가지가 있고 개개의 요인들이 산사태를 발생시키는데 기여하는 중요도도 서로 다르기 때문이다. 그러나 많은 산사태자료에 대한 분석을 바탕으로 발생 메커니즘 규명과 통계적 해석기법을 통해 산사태 발생가능성의 예측과 위험지역의 분류가 가능해졌다. 석조문화재가 산사면 또는 그 직하부에 인접해 있는 경우는 산사태가 발생되면 재해에 무방비로 노출되어 있다. 이 연구에서는 여름철의 집중호우 등에 의해 석조문화재 및 그 주변지역에 산사태가 발생할 가능성을 사전에 예측함으로써 그로 인한 석조문화재의 피해가능성을 분석하고자 하였다. 이러한 목적을 위해 2002년 8월 산사태재해로 인해 피해가 발생된 바 있으며 중요 석조문화재가 위치해 있는 실상사 백장암지역을 연구대상지역으로 선정하여 산사태 예측도를 작성하였다. 그리고 산사태재해 가능성을 발생확률로 표현하여 등급별로 구분함으로써 석조문화재 및 그 주변지역이 산사태에 취약한지의 여부를 평가하였다. 또한, 이러한 조사 및 해석기법을 앞으로 석조문화재 주변의 산사태재해 예측 및 평가를 위해 실용적으로 활용할 수 있는 토대를 마련하였다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.840-843
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• 2006

The aim of this study was to detect landslide using satellite image and apply the landslide to probabilistic landslide-susceptibility mapping at Gangneung area, Korea using a Geographic Information System (GIS). Landslide locations were identified by change detection technique of KOMSAT-1 (Korea Multipurpose Satellite) EOC (Electro Optical Camera) images and checked in field. For landslide-susceptibility mapping, maps of the topography, geology, soil, forest, lineaments, and land cover were constructed from the spatial data sets. Then, the sixteen factors that influence landslide occurrence were extracted from the database. Using the factors and detected landslide, the relationships were calculated using frequency ratio, one of the probabilistic model. Then, landslide-susceptibility map was drawn using the frequency ration and finally, the map was verified by comparing with existing landslide locations. As the verification result, the prediction accuracy showed 86.76%. The landslide-susceptibility map can be used to reduce hazards associated with landslides and to land cover planning.

• 한국환경복원기술학회지
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• 제11권2호
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• pp.1-9
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• 2008

The objective of the study is 10 know the relation of landslide occurrence with using TPI (Topographic Position Index) in the Pyungchang County. Total 659 landslide scars were detected from aerial photographs. To analyze TPI, 100m SN (Small-Neighborhood) TPI map, 500m LN (Large-Neighborhood) TPI map, and slope map were generated from the DEM (Digital Elevation Model) data which are made from 1 : 5,000 digital topographic map. 10 classes clustered by regular condition after overlapping each TPI maps and slope map. Through this process, we could make landform classification map. Because it is only to classify landform, 7 classes were finally regrouped by the slope angle information of landslide occurrence detected from aerial photography analysis. The accuracy of reclassified map is about 46%.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2008년도 춘계 학술발표회 초청강연 및 논문집
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• pp.1378-1385
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• 2008

The probability of landslide hazards was predicted to natural terrain around the stone relics of Jinjeon-saji area, which is located in Yangyang, Kangwon Province. As the analysis results of field investigation, laboratory test and geology and geomorphology data, the effect factors of landslides occurrence were evaluated, and then the landslides prediction map was made up by use of prediction model considering the effect factors. The susceptibility of stone relics induced by landslides was investigated as the grading classification of occurrence probability using the landslides prediction map. In the landslides prediction map, the high probability area of landslides over 70% of occurrence probability was 3,489m3, which was 10.1% of total prediction area. If landslides are occurred at the high elevation area, the three stories stone pagoda of Jinjeon-saji (National treasure No.122) and the stone lantern of Jinjeon-saji (Treasure No.439) will be collapsed by debris flow.

• 한국지형학회지
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• 제26권3호
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• pp.53-67
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• 2019

The purpose of this study is to identify the quality of landslide susceptibility in a landslide-prone area (Jinbu-myeon, Gangwon-do, South Korea) by spatial prediction modeling approach and compare the results obtained. For this goal, a landslide inventory map was prepared mainly based on past historical information and aerial photographs analysis (Daum Map, 2008), as well as some field observation. Altogether, 550 landslides were counted at the whole study area. Among them, 182 landslides are debris flow and each group of landslides was constructed in the inventory map separately. Then, the landslide inventory was randomly selected through Excel; 50% landslide was used for model analysis and the remaining 50% was used for validation purpose. Total 12 contributing factors, such as slope, aspect, curvature, topographic wetness index (TWI), elevation, forest type, forest timber diameter, forest crown density, geology, landuse, soil depth, and soil drainage were used in the analysis. Moreover, to find out the co-relation between landslide causative factors and incidents landslide, pixels were divided into several classes and frequency ratio for individual class was extracted. Eventually, six landslide susceptibility maps were constructed using the Bayesian Predictive Discriminant (BPD), Empirical Likelihood Ratio (ELR), and Linear Regression Method (LRM) models based on different category dada. Finally, in the cross validation process, landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract success rate curve. The result showed that Bayesian, likelihood and linear models were of 85.52%, 85.23%, and 83.49% accuracy respectively for total data. Subsequently, in the category of debris flow landslide, results are little better compare with total data and its contained 86.33%, 85.53% and 84.17% accuracy. It means all three models were reasonable methods for landslide susceptibility analysis. The models have proved to produce reliable predictions for regional spatial planning or land-use planning.

• 한국지반공학회논문집
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• 제30권12호
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• pp.15-25
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• 2014

본 연구에서는 지리산 북쪽의 과거 산사태 발생영역에서 조사된 절리특성과 GIS를 이용하여 추출한 지형특성을 근거하여 연구지역에서 예상되는 암반사면 파괴유형을 분석하였다. 또 해발고도, 사면방향, 사면경사, 음영도, 곡률, 하천 이격거리 등 6개의 지형특성 인자의 빈도비를 중첩하여 산사태 예측도를 작성하였으며, 산사태 예측도와 도로 및 주거지와 같은 지역의 인문적인 인자를 고려한 산사태 피해도를 조합하여 최종적으로 연구지역의 산사태 위험도를 작성하였다. 연구지역에서 발생한 산사태의 지형적 특성을 분석한 결과, 해발고도 330~710m에서 88%, 사면방향 동남-남-남서 방향($90{\sim}270^{\circ}$)에서 77.7%, 사면경사 $10{\sim}40^{\circ}$에서 93.39%, 음영도 등급3~7에서 82.78%, 곡률특성 -5~+5에서 86.28%, 하천 이격거리 400m 이내에서 82.92%가 발생하였다. 산사태가 발생한 영역의 75%는 산사태 위험도에서 위험 등급이 '높음' 이상인 지역이어서 위험 예측에 대한 신뢰성이 확인되었으며, 연구지역의 13.27%는 산사태 위험에 노출된 것으로 분석되었다.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2007년도 춘계학술발표회 논문집
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• pp.371-374
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• 2007

For development of landslide risk assessment techniques using GIS(Geographic Information System), this study classifies the category of socioeconomic factors. The landslide quantitative risk assessment performs first prediction of flow trajectory and runout distance of debris flow over natural terrain. Based on those results, it can be analyzed the factors of socioeconomic which are directly related to the magnitude of risk due to landslide hazards. Those risk assessment results can deliver factual damage situation prediction to policy making for the landslide damage mitigation. Therefore, this study is based on feature classification of the digital map ver. 2.0 provided by the National Geographic Information Institute. The category of factors can be used as useful data in preventing landslide.

• 한국측량학회지
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• 제34권6호
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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.

• 대한원격탐사학회지
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• 제18권1호
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• pp.1-12
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• 2002

Landslide prediction modeling has been regarded as one of the important environmental applications in GIS. While, landslide stability in a certain area as collateral process for prediction modeling can be characterized by DEM-based hydrological features such as flow-direction, flow-accumulation, flow-length, wetness index, and so forth. In this study, Slope-Area plot methodology followed by stability index mapping with these hydrological variables is firstly performed for stability analysis with actual landslide occurrences at Boeun area, Korea, and then Landslide prediction modeling based on likelihood ratio model for landslide potential mapping is carried out; in addition, KOMPSAT EOC imagery is used to detect the locations and scalped scale of Landslide occurrences. These two tasks are independently processed for preparation of unbiased criteria, and then results of those are qualitatively compared. As results of this case study, land stability analysis based on DEM-based hydrological variables directly reflects terrain characteristics; however, the results in the form of land stability map by landslide prediction model are not fully matched with those of hydrologic landslide analysis due to the heuristic scheme based on location of existed landslide occurrences within prediction approach, especially zones of not-investigated occurrences. Therefore, it is expected that the resets on the space-robustness of landslide prediction models in conjunction with DEM-based landslide stability analysis can be effectively utilized to search out unrevealed or hidden landslide occurrences.