• 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.

• 지질공학
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• 제18권3호
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• pp.303-309
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• 2008

중요 석조문화재가 위치하고 있는 강원도 양양군 소재 진전사지 주변지역에 대한 사면재해 가능성을 예측하였다. 연구지역에 대한 현장조사, 실내시험, 지질 및 지형자료 분석을 통해 산사태에 유의한 영향요소를 평가하고 그 자료들을 예측모델에 적용하여 산사태예측도를 작성하였다. 그리고 산사태예측도에 의해 발생확률을 등급별로 분류함으로써 산사태로 인한 석조문화재의 피해가능성을 검토하였다. 산사태예측도에 의하면, 발생확률 70% 이상으로 산사태가 발생될 가능성이 높은 지역은 대상면적의 10.1%인 $3,489m^2$정도로 비교적 넓은 면적비율을 나타내었다. 피해영향권에 속한 높은 고도의 산사면에서 산사태가 발생할 경우 국보 제122호인 진전사지삼층석탑과 보물 제439호인 진전사지부도에 사태물질로 인한 피해가능성이 예상된다.

• 한국재난관리표준학회지
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• 제1권4호
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• pp.57-64
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• 2008

우리나라에서는 해마다 여름철에 발생하는 태풍 및 집중호우에 기인한 산사태 및 절 성토지의 붕괴로 인한 피해가 계속되고 있다. 본 연구에서는 지리정보시스템과 사면안정해석모형, 분포형 지하수위모형 및 토심추정모형을 이용하여 산사태를 예측하는 기법을 개발하였다. 기법의 적용성을 검증하기 위하여 산사태가 많이 발생하였던 지역을 선정하고, 실제 산사태가 발생한 위치를 위성영상으로부터 파악하여 예측시스템에서 추정된 산사태 가능성과 비교 검증하였다. 예측모델과 실제 데이터의 일치비율은 84.8%로 나타났다. 또한 수리학적, 지형적 요소와 산지재해의 관계도 분석되었다.

• 한국농공학회논문집
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• 제47권6호
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• pp.37-45
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• 2005

Almost every year, Korea has been suffered from serious damages of lives and properties, due to landslides that are triggered by heavy rains in monsoon season. In this paper, we systematized the physically based landslide prediction model which consisted of 3 parts, infinite slope stability analysis model, groundwater flow model and soil depth model. To evaluate its applicability to the prediction of landslides, the data of actual landslides were plotted on the predicted areas on the GIS map. The matching rate of this model to the actual data was $84.8\%$. And the relation between hydrological and land form factors and potential landslide were analyzed.

• 환경영향평가
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• 제22권1호
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• pp.39-50
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• 2013

Occurrence of landslides has been increasing due to extreme weather events(e.g. heavy rainfall, torrential rains) by climate change. Pyeongchang, Korea had seriously been damaged by landslides caused by a typhoon, Ewiniar in 2006. Moreover, the frequency and intensity of landslides are increasing in summer due to torrential rain. Therefore, risk assessment and adaptation measure is urgently needed to build resilience. To support landslide adaptation measures, this study predicted landslides occurrence using MaxEnt model and suggested susceptibility map of landslides. Precipitation data of RCP 8.5 Climate change scenarios were used to analyze an impact of increase in rainfall in the future. In 2050 and 2090, the probability of landslides occurrence was predicted to increase. These were due to an increase in heavy rainfall and cumulative rainfall. As a result of analysis, factors that has major impact on landslide appeared to be climate factors, prediction accuracy of the model was very high(92%). In the future Pyeongchang will have serious rainfall compare to 2006 and more intense landslides area expected to increase. This study will help to establish adaptation measure against landslides due to heavy rainfall.

• 한국지리정보학회지
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• 제14권4호
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• pp.116-127
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• 2011

본 논문에서는 로지스틱 회귀분석 기법을 이용하여 산사태 취약성 분석을 수행하였다. 예측모델의 성능은 모델의 적합도 검증을 통해 사용된 데이터가 모델에 얼마나 잘 반영되어 구축되었는지에 대한 적합도 평가뿐만 아니라 예측성능에 대한 평가가 필요하다. 따라서 이 논문에서는 모델에 대한 객관적인 결과를 얻기 위해 이와 같은 두 가지 측면에 대하여 예측성능 평가를 적용하였다. 연구지역은 2006년도 집중 호우로 많은 산사태가 발생한 강원도 인제 일대를 대상으로 하였다. 산사태 관련인자들은 지형도, 토양도, 임상도로부터 추출하였다. 예측모델에 대한 평가는 누적이득차트 곡선의 하부영역을 계산하였다. 예측모델의 적합도 평가에서는 87.9% 교차검증을 통한 예측정확도 평가 결과 84.8%로 두 평가 결과간의 큰 차이를 보이지 않으며 좋은 성능의 결과를 산출하였다. 이는 산사태와 관련성이 높은 유발인자와 예측모델 성능에서 기인된 결과로 해석 될 수 있다.

• 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.

• 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.

• 한국지형학회지
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• 제27권1호
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• pp.61-89
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• 2020

The objective of this study is to characterize landslide susceptibility depending on various geo-environmental variables as well as to compare the Frequency Ratio (FR) and Evidential Belief Function (EBF) methods for landslide susceptibility analysis of rainfall-induced landslides. In 2013, a total of 259 landslides occurred in Chuncheon, Gangwon Province, South Korea, due to heavy rainfall events with a total cumulative rainfall of 296~721mm in 106~231 hours duration. Landslides data were mapped with better accuracy using the geographic information system (ArcGIS 10.6 version) based on the historic landslide records in Chuncheon from the National Disaster Management System (NDMS), the 2013 landslide investigation report, orthographic images, and aerial photographs. Then the landslides were randomly split into a testing dataset (70%; 181 landslides) and validation dataset (30%; 78 landslides). First, geo-environmental variables were analyzed by using FR and EBF functions for the full data. The most significant factors related to landslides were altitude (100~200m), slope (15~25°), concave plan curvature, high SPI, young timber age, loose timber density, small timber diameter, artificial forests, coniferous forests, soil depth (50~100cm), very well-drained area, sandy loam soil and so on. Second, the landslide susceptibility index was calculated by using selected geo-environmental variables. The model fit and prediction performance were evaluated using the Receiver Operating Characteristic (ROC) curve and the Area Under Curve (AUC) methods. The AUC values of both model fit and prediction performance were 80.5% and 76.3% for FR and 76.6% and 74.9% for EBF respectively. However, the landslide susceptibility index, with classes of 'very high' and 'high', was detected by 73.1% of landslides in the EBF model rather than the FR model (66.7%). Therefore, the EBF can be a promising method for spatial prediction of landslide occurrence, while the FR is still a powerful method for the landslide susceptibility mapping.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2005년도 지반공학 공동 학술발표회
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• pp.655-662
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• 2005

The objective of this thesis is to develop a prediction system of potential landslide sites to apply to the prevention of landslide disaster which occurred during the heavy rainfall in the rainy season. The system was developed by combining a modified slope stability analysis model and a hydrological model. The modified slope stability analysis model, which was improved from 1-D infinite slope stability analysis model, has been taken into consideration of the flexion of the hill slopes. To evaluate its applicability to the prediction of landslides, the data of actual landslides were plotted on the predicted areas on the GIS map. The matching rate of this model to the actual data was 92.4%. And the relations between wetness index and landform factors and potential landslide were analyzed.