• Smart Media Journal
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• v.12 no.3
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• pp.77-84
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• 2023

Forest fires are representative natural disasters resulting from dramatic global climate change in these modern times. When forest formation is insufficient due to forest damage caused by fire, secondary damages such as landslides occur during the winter thawing period and heavy rains. In most countries, only a limited area is managed as CCTV-centered monitoring systems for forest management. For the landslide prediction, markers containing 3D spatial coordinates were located on the slopes of the danger areas in advance. Then 3D mapping and angle of repose were obtained by periodic drone imaging. The recognition range and angle of view of markers were defined, and a new method for predicting signs of landslides in advance was presented in this study.

• The Journal of Engineering Geology
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• v.23 no.3
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• pp.305-314
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• 2013

Recent international and national research trends in landslide hazards were analyzed by performing a literature search of relevant scientific journals. For obtaining data from Korea, we used 'Information for Environmental Geology' (IEG), which covers 17 journals in the field of environmental geology. A total of 54 articles related to landslide hazards were found in 5 journals published in the period 2000-2012. The most common topic was landslide prediction or susceptibility (29 articles), followed by landslide mechanisms. For international information, we analyzed 1,851 articles from the 'Web Of Science' published from 2003 to the present. Researchers in Italy have published the greatest number of papers in this field, while papers from Korea rank first in terms of the citation index.

• Proceedings of the KSEEG Conference
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• 2007.04a
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• pp.322-324
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• 2007

• Geotechnical Engineering
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• v.6 no.2
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• pp.55-66
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• 1990

Many landslides have been taken place during the wet season in Korea. Rainfall in one of the most significant factors relevant to the landsildes, which cause a great loss of lived and properties every year, However, forecast systems for landslides have not been sufficiently established in Korea. In order to minimize a disaster due to landslides, the relationship between landslides and rainfall was investigated based on meteorological records and landslides occrrence ranging from 1977 to 1987. According to rainfall patterns which cause landslides, such as the daily rainfall on failure day or the cumulative rainfalls before failure day, the area in which landslides were taken place, could be divided into three groups of Middl area, Young- Ho Nam area, and Young-Dong Area. And the frequency of landslides was also dependent on the hourly rainfall intensity. It shows from the analyses that prediction of landslides can be made based on both the cumulative rainfall and the hourly rainfall intensity.

• Journal of the Korean Geotechnical Society
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• v.20 no.8
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• pp.67-75
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• 2004

The landslide is one of the most significant natural disasters, which cause a lot of loss of human lives and properties. The landslides in natural slopes generally occur by complicated problems such as soil properties, topography, and geology. Artificial Neural Network (ANN) model is efficient computing technique that is widely used to solve complicated problems in many research fields. In this paper, the ANN model with application of error back propagation method was proposed for estimation of landslide hazard in natural slope. This model can evaluate the possibility of landslide hazard with two different approaches: one considering only soil properties; the other considering soil properties, topography, and geology. In order to evaluate reasonably the landslide hazard, the SlideEval (Ver, 1.0) program was developed using the ANN model. The evaluation of slope stability using the ANN model shows a high accuracy. Especially, the prediction of landslides using the ANN model gives more stable and accurate results in the case of considering such factors as soil, topographic and geological properties together. As a result of comparison with the statistical analysis(Korea Institute of Geosciences and Mineral Resources, 2003), the analysis using the ANN model is approximately equal to the statistical analysis. Therefore, the SlideEval (Ver. 1.0) program using ANN model can predict landslides hazard and estimate the slope stability.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.26-26
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• 2021

본 연구에서는 2020년 산사태가 발생한 곡성지역을 대상으로 무인항공기 사진측량을 통하여 산사태 지역의 범위와 변위를 조사하고 이를 기반으로 산사태에 의한 피해범위를 LS-RAPID에서 분석하였다. LS-RAPID는 지진과 강우의 영향을 반영하는 산사태 시뮬레이션 모델이며, 산사태 운동시작여부를 평가하며 만일 발생 시 토사의 이동, 퇴적 범위, 토사층의 깊이를 예측할 수 있다. 산사태 시뮬레이션에서 중요한 변수 중의 하나는 지중의 활동층의 깊이와 분포이다. 재해현장에서 이런 자료를 신속하고 정량적으로 측정하기 위한 방법으로서 무인항공기를 이용한 측량을 실시하였다. 또한 산사태 토사의 이동과 퇴적을 검증하기 위한 자료도 획득하였다. 매개변수의 추정 시선행연구에서 제시된 값을 참고하여, 재해현장의 피해범위와 규모를 비교하여 매개변수를 추정하여 다른 연구사례에서 이용한 값들과 비교, 분석하였다. 또한, 시뮬레이션의 지형입력자료로서 무인항공기 사진 측량자료에서 생성된 DSM(Digital Surface Model)과 수지지도에서 생성한 DEM(Digital Elevation Model)을 적용한 경우, 시뮬레이션 결과에 영향을 비교, 분석하였다. 결과적으로 DEM보다 DSM을 적용하는 것이 퇴적범위가 크게 확대되지 않으며, 현장을 잘 반영한 결과가 얻어지는 것으로 평가되었다.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.1
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• pp.75-87
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• 2005

This study aims at predicting and mapping of the landslide susceptibility in the Geumho river watershed using GIS and Remote Sensing techniques. We constructed the spatial database of affecting factors such as slope angle, slope aspect, lithology, landuse, and vegetation index (NDVI) at a $30m{\times}30m$ resolution. The landslide susceptibility of the study area was predicted through overlay analysis and adding up estimation matrix, and the predicted map of landslide susceptibility with six categories (stable, very low, low, moderate, high, very high) was constructed. As the results, it showed that the very high susceptibility zones made up approximately 0.3% of the total study area, and these zones were mainly distributed in the forest area with the high slope angle and low vegetation index.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.705-705
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• 2012

우리나라는 최근 여름철 태풍 및 집중호우로 인해 많은 토석류 및 산사태가 발생하고 있다. 작년 7월에도 집중호우로 인해 서울시 우면산 일대와 강원도 춘천에 많은 인적 물적 피해를 입었다. 해마다 반복되는 토석류나 산사태의 위험을 감소시키기 위해서는 보다 정확한 위험지역 예측모델을 필요로 한다. 본 연구는 토석류 및 산사태의 위험과 취약지역을 예측하기 위하여 GIS기반의 Weight of Evidence 기법을 적용하여 위험지역을 분석 하고자 한다. 2006년 태풍 에위니아에 의해 많은 토석류 피해를 입은 강원도 인제군 가리산일대를 대상으로 하였으며 토석류 및 산사태 위치 자료는 2005년, 2006년 토석류 발생 전후 항공사진의 중첩분석을 토대로 발생 지역을 추출하였다. 토석류 및 산사태발생에 영향을 미치는 지형, 지질, 토양, 수문, 임상 등의 인자들은 GIS를 이용하여 DB로 구축하였다. 베이시안 확률기법(Bayesian Method)에 기반 하여 구축된 DB와 결합하여 각각의 인자의 가중 값 W+, W-를 계산하여 상관관계를 분석하고 Weight of Evidence 기법을 적용하여 위험지역을 정량적으로 평가하고자 한다.

• Journal of the Korean Geotechnical Society
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• v.30 no.12
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• pp.15-25
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• 2014

In this study, types of rock slope failure are analyzed by considering both joint characteristics investigated on previous landslide regions located at northern part of Mt. Jiri and geographic features of natural slopes deduced from GIS. The landslide prediction map was produced by superposing the frequency ratio layers for the six geographic features including elevation, slope aspect, slope angle, shaded relief, curvature and stream distance, and then the landslide risk map was deduced by combination of the prediction map and the damage map obtained by taking account of humanity factors such as roads and buildings in the study area. According to analysis on geographic features for previous landslide regions, the landslides occurred as following rate: 88% at 330~710 m in elevation, 77.7% at $90{\sim}270^{\circ}$ in slope aspect, 93.9% at $10{\sim}40^{\circ}$ in slope angle, 82.78% at grade3~7 in shaded relief, 86.28% at -5~+5 in curvature, and 82.92% within 400m in stream distance. Approximately 75% of the landslide regions belongs to the region of 'high' or 'very high' grade in the prediction map, and 13.27% of the study area is exposed to 'high risk' of landslide.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.2
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• pp.59-66
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• 2018

This study examined the correlation among topography, forest type, soil and geology in Inje area where landslides occurred during heavy rainfall from July 11 to July 18, 2006 to assess the landslide susceptibility. In order to assess the susceptibility of future landslides, landslides occurred in Inje area were classified into slide type and flow type, and slope angle, aspect, curvature, ridge and valley were extracted from the area. The landslide susceptibility was assessed by applying diameter class, age class, density, and forest type to Bayesianbased LR (Logistic Regression) model and WOE (Weight of Evidence) model, and the fitness of modeling was verified by predict rate curve. As the results of susceptibility assessment, using all landslides without no distintion, it was found that 75% of the LR model and 73% of the WOE model were fit in terms of the top 20% of the landslides. According to slide type and flow type in the top 20% of the landslides, it was found that 71% of the LR model and 69% of the WOE model were fit in terms of the slide type. Whereas, it was found that 86% of the LR model and 82% of the WOE model were fit in terms of the flow type. That is, the results of the LR model showed higher fitness than the results of the WOE model, and the fitness of the flow type was higher than that of the slide type. Consequently, it suggests that it is reasonable to assess and verify the landslide susceptibility according to the types of landslides.