• Journal of Korean Society for Geospatial Information Science
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• v.2 no.2 s.4
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• pp.43-53
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• 1994

GSIS(geo-spatial information system), particularly when utilized in hazard management decision, is one of hazard analysis tool. Data of GSIS input from digitizing or scanning of map or aerial photos. This paper focuses upon the hazard prediction in GSIS and RS analysis to assess map, aerialphotos, satellite imagery and soil map. This study found computation of hazard area analysis. the results is formed as raster data model of quadtree. Authors knew more accurate results of overlay. This paper shows building up integrated data base as well as search of hazard area in aerial photographs.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.5
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• pp.927-940
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• 2017

Landslide has been a major disaster in Indonesia, and recent climate change and indiscriminate urban development around the mountains have increased landslide risks. Java Island, Indonesia, where more than half of Indonesia's population lives, is experiencing a great deal of damage due to frequent landslides. However, even in such a dangerous situation, the number of inhabitants residing in the landslide-prone area increases year by year, and it is necessary to develop a technique for analyzing landslide-hazardous and vulnerable areas. In this regard, this study aims to evaluate landslide susceptibility of Java, an island of Indonesia, by using GIS-based spatial prediction models. We constructed the geospatial database such as landslide locations, topography, hydrology, soil type, and land cover over the study area and created spatial prediction models by applying Weight of Evidence (WoE), decision trees algorithm and artificial neural network. The three models showed prediction accuracy of 66.95%, 67.04%, and 69.67%, respectively. The results of the study are expected to be useful for prevention of landslide damage for the future and landslide disaster management policies in Indonesia.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.33-33
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• 2016

산사태 발생 예측은 재해를 예방하고 대처하기 위한 가장 근본적이며 효과적인 방법이나, 과학기술의 발전과 많은 노력에도 불구하고 아직 산사태의 발생 장소와 시기를 예측하는 것은 매우 어려운 일이다. 산사태 발생 예측 기법은 크게 경험론적 지수기법, 통계적 해석기법, 물리적 해석 기법으로 나뉠 수 있다. 이 세 방법은 각기 장단점이 있으나 일반적으로 후자로 갈수록 많은 데이터가 요구되고, 해석에 시간이 필요하며, 보다 신뢰할만한 결과를 도출할 수 있다. 경험론적 지수 기법은 국내에서 실무적으로 널리 활용되고 있으며, 통계적 해석기법에 관한 연구도 수행된 바 있다. 하지만 이 두 방법론은 일정량 또는 일정강도 이상의 강우 발생 시 산사태의 발생 위험도를 공간적으로 예측할 수 있으나, 산사태의 발생 시점과 연속적인 강우량 또는 강우강도의 관계를 정량적으로 분석하기 힘든 한계가 있어 최근에는 이러한 한계를 극복하기 위해 최근 무한사면안정 모형과 토양수분침투 모형을 결합한 시변 사면안정모형들이 활용되기 시작하고 있다. 대표적으로는 TRIGRS가 있으며, 이 모형에서는 선형화한 1차원 Richards 방정식의 해석해를 활용하여 토양수분량을 계산한 후 이 정보를 무한사면안정모형에 반영하여 시변적인 사면안정도를 구하고 있다. 하지만 Richards 방정식을 선형화하기 위해서 제한된 토양수분-압력 관계식이 사용되며, GUI가 제공되지 않아 전처리 및 후처리가 번거로운 한계가 있다. 본 연구에서는 이러한 한계를 개선하기 위해 3차원 Richards방정식을 수치적으로 계산하여 보다 다양한 토양수분-압력 모형과 초기조건을 반영할 수 있게 하였다. 또한 GUI를 지원하여 사용자가 보다 손쉽게 해석모형을 사용할 수 있도록 하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.427-427
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• 2018

최근 집중호우나 극한 강우사상으로 인하여 산사태나 토석류와 같은 산지재해가 빈번하게 발생하고 있으며 특히 우리나라는 지형 특성상 주거지역이 산지와 인접해 있는 경우가 많아 재해발생 시 피해를 가중시키는 원인이 되고 있다. 산지재해는 예측하기가 어렵고 산지에서 발생한 토석류가 계곡을 따라 흘러 내려와 도심지 및 산지와 인접한 도로나 주택지에 많은 피해를 발생 시키고 있다. 본 연구에서는 해마다 반복적으로 발생하고 있는 산사태나 토석류와 같은 재해의 피해저감과 원인분석을 위하여 강원도 삼척시 도계읍 일대를 대상지역으로 선정하고 산지유역의 위험성 분석을 위하여 사면안정성 예측 모델인 SINMAP 모형을 사용하여 산지재해가 발생 가능한 위험지역 및 안전한 구간을 분석하고 지형분류기법 중의 하나인 Topographic Position Index(TPI) 분석방법을 통해 대상지역의 지형위치지수를 계산하여 위험지형을 분류하였다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.507-514
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• 2018

TSI (TPI-Slope Index) which is the combination of TPI (Topographic Position Index) and slope was newly proposed for landslide and applied to a landslide susceptibility model. To do this, we first compared the TPIs with various scale factors and found that TPI350 was the best fit for the study area. TPI350 was combined with slope to create TSI. TSI was evaluated using logistic regression. The evaluation showed that TSI can be used as a landslide factor. Then a logistic regression model was developed to assess the landslide susceptibility by adding other topographic factors, geological factors, and forestial factors. For this, landslide-related factors that can be extracted from DEM (Digital Elevation Model), soil map, and forest type map were collected. We checked these factors and excluded those that were highly correlated with other factors or not significant. After these processes, 8 factors of TSI, elevation, slope length, slope aspect, effective soil depth, tree age, tree density, and tree type were selected to be entered into the regression analysis as independent variables. Three models through three variable selection methods of forward selection, backward elimination, and enter method were built and evaluated. Selected variables in the three models were slightly different, but in common, effective soil depth, tree density, and TSI was most significant.

• Journal of Digital Convergence
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• v.10 no.6
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• pp.289-296
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• 2012

Due to the topographical and climatic features of Korea, there is a strong possibility of a landslide. Recently, many landslides, caused by the improper land development, frequently occured at the mountain area every summer. Cheonan has been recognized to be relatively safe against landslide, but with the increased risk factors, systematic analysis of the landslide is required. In this paper, the topographical features of Mt. Seonggo in Cheonan City were extracted using contour maps, and water courses and basin areas in heavy rain were computed using the results. Conclusively, Mt. Seonggo areas were relatively safe in the view points of the length of water courses and rain-inflow, but in case of some narrow areas, sustainedly observation was required. Meanwhile, a contour map is proper to analyze the risk of landslide in the 1'st level in that it is more cost effective than other types of digital map.

• Journal of Korean Society of Forest Science
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• v.97 no.3
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• pp.326-331
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• 2008

This study was carried out to analyze the characteristics of each factors by using the quantification theory(I) for prediction of landslide hazard area. The results obtained from this study were summarized as follows; The stepwise regression analysis between landslide sediment ($m^3$ ) and environmental factors, factors affecting landslide sediment ($m^3$ ) were high in order of mixed (forest type), < 15 cm(soil depth), 801~1,200 m (altitude), $31{\sim}40^{\circ}$ (slope gradient), 46 cm < (soil depth), 1,201 m < (altitude) and s(aspect). According to the range, it was shown in order of soil depth (0.3784), altitude (0.2876), forest type (0.2409), slope gradient (0.1728) and aspect (0.1681). The prediction of landslide hazard area was estimated by score table of each category. The extent of prediction score was 0 to 1.2478, and middle score was 0.6239. Class I was over 1.1720, class II was 0.7543 to 0.1719, class III was 0.4989 to 0.7542 and class IV was below 0.4988.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.4
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• pp.71-80
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• 2014

This study predicted damage areas due to landslides in Gangwon Province and estimated the scale of damage to roads, buildings, and forests on the local government level. By using old research findings to predict landslides, the study established techniques to make maps for landslide vulnerability, occurrence possibility, and risk. The scale of damage to roads, buildings, and forests was estimated at the local government level by making a landslide risk map for 100mm, 200mm, and 300mm of accumulated rainfall. The scale of damage to roads, buildings, and forests was estimated to be greatest in Hongcheon-gun, Jeongseon-gun, and Hongcheon-gun, respectively, in case of 100mm~200mm accumulated rainfall, in Chuncheon City, Pyeongchang-gun, and Hongcheon-gun, respectively, in case of 200mm~300mm accumulated rainfall, and in Hongcheon-gun in case of 300mm accumulated rainfall or more. Those estimation results of scale of damage by landslides at the local government level will help to set priorities in landslide prevention and provide basic data for budget decisions.

• Journal of Korean Society of Forest Science
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• v.103 no.2
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• pp.233-239
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• 2014

This study was carried out to propose the criteria for the prediction of landslide occurrence through analysis the influence of each factor by using the quantification theory. The results obtained from this study are summarized as follows. From a stepwise regression analysis between the landslide area($m^2$) and environmental factors, the factors strongly affecting the landslide sediment($m^2$) were the Parents rock (igneous), cross slope(complex), coniferous forests (forest type) and slope gradient ($21{\sim}30^{\circ}$). According to the range, it was shown in order of Cross slope (0.2922), Parents rock (0.2691), Forest type (0.2631) and Slope gradient (0.2312). The range of prediction score of landslide occurrence has been distributed between score 0 and score 1.0556, the median value was score 0.5278. The prediction for class I was over 0.7818, for class II was 0.5279 to 0.7917, for class III 0.2694 to 0.5278 and for class IV was below 0.2693. The prediction on landslide occurrence appeared relatively high accuracy rate as 72% for class I and II. Therefore, this score table for landslide will be very useful for judgement of dangerous slope.