• Journal of Korean Society for Geospatial Information Science
• /
• v.19 no.3
• /
• pp.33-40
• /
• 2011

This paper presents the prediction and evaluation of landslide using LRA(logistic regression analysis) and ANN (Artificial Neural Network) methods. In order to assess the landslide, we selected Sarabong, Byeoldobong area and Mt. Song-ak in Jeju Island. Five factors which affect the landslide were selected as: slope angle, elevation, porosity, dry density, permeability. So as to predict and evaluate the landslide, firstly the weight value of each factor was analyzed by LRA(logistic regression analysis) and ANN(Artificial Neural Network) methods. Then we got two prediction maps using AcrView software through GIS(Geographic Information System) method. The comparative analysis reveals that the slope angle and porosity play important roles in landslide. Prediction map generated by LRA method is more accurate than ANN method in Jeju. From the prediction map, we found that the most dangerous area is distributed around the road and path.

• The Journal of Engineering Geology
• /
• v.34 no.1
• /
• pp.51-65
• /
• 2024

Landslides are natural disasters that causes significant property damage worldwide every year. In Korea, damage due to landslides is increasing owing to the effects of climate change, and it is important to identify the factors that increase the prevalence of landslides in order to reduce the damage they cause. Therefore, this study used a random forest model to analyze the importance of 14 factors in influencing landslide damage in a specific area of Chungju, Chungcheongbuk-do province, Korea. The random forest model performed accurately with an AUC of 0.87 and the most-important factors were ranked in the order of aspect, slope, distance to valley, and elevation, suggesting that topographic factors such as aspect and slope more greatly influence landslide damage than geological or soil factors such as rock type and soil thickness. The results of this study are expected to provide a basis for mapping and predicting landslide damage, and for research focused on reducing landslide damage.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.29 no.5
• /
• pp.535-541
• /
• 2011

A debris flow is caused by torrential rain in mountainous regions and carries mixture of fragmental matter from slope failure, deposit soils from a valley floor and a large amount of water. It seriously damages facilities, houses, and human lives in its path. We tried to apply debris flow behavior estimation model developed in foreign country to domestic case. The study area is Inje-county, Gangwon-do and aerial photos and GPS surveying were used to collect information of starting and end point of the landslide and debris flow. The analysis showed that L/H for forecasting the travel distances of debris flows has the mean of 4.93 and standard deviation of 0.98. This model tended to overestimate the scale and extent of debris flows. In Inje-county's case, a debris flow is caused by multiple simultaneous small-scale landslide. This is quite different from the foreign cases in which a large-scale landslide cause a large-scale debris flow. Thus, an empirical model suitable for domestic conditions needs to be developed.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2010.05a
• /
• pp.1817-1821
• /
• 2010

본 연구에서의 지리정보시스템(GIS)을 이용한 사방댐 입지선정모델 개발은 산사태 발생 예측을 위한 사면안정성 평가 기준을 개발하여 사방댐 지점을 선정하기 위하여 체계적으로 표준화된 시스템을 구축하는 것이 목표이며, 2002년 태풍 '루사'와 2003년 태풍 '매미'에 의하여 토석류와 산사태가 발생한 서부경남 지역의 38개 지점에 대하여 항공사진 수집 및 현장조사를 수행하고, 산사태 발생에 관계되는 강우, 지형, 지질 및 토양, 임상 등을 인자로서 규정하였다. 연구결과 서부경남지역에서 발생한 산사태는 지리산, 가야산, 좌굴산 등 EL. 500m 이상의 비교적 고도가 높은 산악지역에서 지형성 집중호우에 의하여 발생하는 것으로 분석되었으며, 강우량과 산사태의 상관분석결과 시강우량 70mm 이상 및 누가강우량 230mm 이상에서 산사태의 발생빈도가 높은 것으로 나타났다. 또한 산사태 발생지점에서의 고도(평균해수면 기준)와 능선의 고도와의 비를 백분율로 계산하여 빈도를 살펴보면 산사태 발생지점이 능선의 90% 이상의 고도에서 산사태의 발생빈도가 53%로 가장 높고, 80-90%는 21%, 70-80% 16%의 순으로 산사태 발생빈도가 감소하고 있으며, 고도가 더욱 낮아져 산사태 발생지점이 60% 이하로 내려가면 산사태 발생빈도는 급격히 감소한다. 예를 들어 능선의 고도가 1000m일 경우 900m 이상의 고도(90% 이상)에서 산사태 발생빈도가 가장 높고 600m 이하의 고도(70% 이하)에서는 발생빈도가 급격히 저하하는 것으로 나타났다. 산사태 발생지점의 표면 굴곡도에 따른 산사태의 발생빈도는 대부분의 평행사면에서 74%, 약간 오목사면에서 26%가 발생하는 것으로 나타났다. 각 지구의 지질 및 토양별 산사태 발생빈도는 화성암계열의 지질 및 자갈/암괴 섞인 토사의 토양에서 발생하는 것으로 분석되었고, $34-40^{\circ}$ 사면경사에서 40%, $26-34^{\circ}$ 사면경사에서 26%, $26^{\circ}$ 이하의 사면경사에서 22%가 주로 발생하였으며, $40^{\circ}$ 이상의 높은 사면경사에서는 극히 미미하였다. 또한 임상 기준으로는 침엽수림에서 주로 발생하는 것으로 나타났다. 본 연구에서는 이상의 결과를 기초로 매우 안정, 안정, 부분적 안정, 불안정, 매우 불안정, 위험 지역으로 구분하고, 평가한 결과는 불안정 33개소, 매우 불안정 5개소 등 38개소 지점 모두에 사방댐 설치가 필요한 것으로 분석되었다.

• Journal of Korea Water Resources Association
• /
• v.54 no.12
• /
• pp.1205-1214
• /
• 2021

Precipitation is one of a major causes of landslides by rising of pore water pressure, which leads to fluctuations of soil strength and stress. For this reason, precipitation is the most frequently used to determine the landslide thresholds. However, using only precipitation has limitations in predicting and estimating slope stability quantitatively for reducing false alarm events. On the other hand, Soil Moisture (SM) has been used for calculating slope stability in many studies since it is directly related to pore water pressure than precipitation. Therefore, this study attempted to evaluate the appropriateness of applying soil moisture in determining the landslide threshold. First, the reactivity of soil saturation level to precipitation was identified through time-series analysis. The precipitation threshold was calculated using daily precipitation (P_daily) and the Antecedent Precipitation Index (API), and the hydrological threshold was calculated using daily precipitation and soil saturation level. Using a contingency table, these two thresholds were assessed qualitatively. In results, compared to P_daily only threshold, Goesan showed an improvement of 75% (P_daily + API) and 42% (P_daily + SM) and Changsu showed an improvement of 33% (P_daily + API) and 44% (P_daily + SM), respectively. Both API and SM effectively enhanced the Critical Success Index (CSI) and reduced the False Alarm Rate (FAR). In the future, studies such as calculating rainfall intensity required to cause/trigger landslides through soil saturation level or estimating rainfall resistance according to the soil saturation level are expected to contribute to improving landslide prediction accuracy.

• The Journal of Engineering Geology
• /
• v.18 no.3
• /
• pp.303-309
• /
• 2008

A probability of slope hazards was predicted at a natural terrain around the stone relics of Jinjeon-saji area, which is located in Yangyang, Kangwon Province. As the analyzing results of field investigation, laboratory test and geology and geomorphology data, the effect factors of landslides occurrence were evaluated. Also, the landslides prediction map was made up using the prediction model by the effect factors. The landslide susceptibility of stone relics was investigated as the grading classification of occurrence probability. In the landslides prediction map, the high probability area was $3,489m^2$ and it was 10.1% of total prediction area. The high probability area has over 70% of occurrence probability. If landslides are occurred at the predicted 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.

• The Journal of Engineering Geology
• /
• v.26 no.1
• /
• pp.23-32
• /
• 2016

This study evaluates landslide susceptibility using statistical analysis by SVM (support vector machine) and the illite index of clay minerals extracted from ASTER(advanced spaceborne thermal emission and reflection radiometer) imagery which can be use to create mineralogical mapping. Landslide locations in the study area were identified from aerial photographs and field surveys. A GIS spatial database was compiled containing topographic maps (slope, aspect, curvature, distance to stream, and distance to road), maps of soil properties (thickness, material, topography, and drainage), maps of timber properties (diameter, age, and density), and an ASTER satellite imagery (illite index). The landslide susceptibility map was constructed through factor correlation using SVM to analyze the spatial database. Comparison of area under the curve values showed that using the illite index model provided landslide susceptibility maps that were 76.46% accurate, which compared favorably with 74.09% accuracy achieved without them.

• Journal of Korean Society of Forest Science
• /
• v.103 no.2
• /
• pp.218-225
• /
• 2014

Landslides, one of earth's natural disasters, increase every year due to heavy rainfall, and cause damage to human life and assets. This study used the SHALSTAB to predict places at risk of landslides, in accordance with the intensity of rainfall. The parameter value of transmissivity was $19.58m^2/day$, the internal friction angle $36.3^{\circ}$, and the saturated unit weight $2.03t/m^3$. The slope stability status was classified into four categories, namely: unconditionally stable, stable, unstable and unconditionally unstable. In order to evaluate the applicability of the SHALSTAB, actual landslide areas were checked, with the unstable area under 263 mm rainfall. 85.1% of them were consistent. And so we can identify the distribution of places at risk of landslides, on the basis of the intensity of rainfall by means of SHALSTAB.

• Journal of the Korean Geosynthetics Society
• /
• v.16 no.3
• /
• pp.1-10
• /
• 2017

Recently, one of the natural disasters, landslide is causing huge damage to people and properties. In order to minimize the damage caused by continuous landslide, a scientific management system is needed for technologies related to measurement and monitoring system. This study aims to establish a management system for landslide damage by prediction of slope failure. Ground behavior was predicted by surface ground deformation in case of slope failure, and the change in ground displacement was observed as slope surface. As a result, during the slope failure, the ground deformation has the collapse section, the after collapse precursor section, the acceleration section and the burst acceleration section. In all cases, increase in displacement with time was observed as a slope failure, and it is very important event of measurement and maintenance of risky slope. In the future, it can be used as basic data of slope management standard through continuous research. And it can contribute to reduction of landslide damage and activation of measurement industry.

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