• Journal of Korean Society of societal Security
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• v.2 no.2
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• pp.85-90
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• 2009

In recent days, climatic change cause abnormal weather all over the world and we have a great loss of life and property every year. In Korea, we suffer from landslide problem because large regions of Korea Peninsula are composed of mountain. In order to detect rapidly and to take follow-up measures of disaster, the remote sensing is being used actively as conventional field survey has many restrictions in accessibility because of more time and man power requirement. In additions interferometric SAR is one of the techniques that have our attention because it can provide many kinds of accurate surface information without restriction of atmospheric and ground conditions by using L-band. In this study, we aimed to monitor the displacement of mountain area in Kangwondo and this results will be used for detecting landslide. Also we build the web system for detecting and analyzing the landslide.

• Journal of the Korean Society of Safety
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• v.27 no.6
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• pp.172-177
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• 2012

In areas around Deoksan Li and Deokjeon Li, Inje Eup, Inje Gun, located between $38^{\circ}2^{\prime}55^{{\prime}{\prime}}N$ and $38^{\circ}5^{\prime}50^{{\prime}{\prime}}N$ in latitude and $128^{\circ}11^{\prime}20^{{\prime}{\prime}}E$ and $128^{\circ}18^{\prime}20^{{\prime}{\prime}}E$ in longitude, large-sized avalanche disasters occurred due to Typhoon Ewiniar in 2006. As a result, 29 people were dead or missing, along with a total of 37.25 billion won of financial loss(Gangwon Province, 2006). To evaluate such landslide and debris flow risk areas and their vulnerability, this study applied a technique called 'Weight of Evidence' based on GIS. Especially based on the overlay analysis of aerial images before the occurrence of landslides and debris flows in 2005 and after 2006, this study extracted 475 damage-occurrence areas in a shape of point, and established a DB by using such factors as topography, hydrologic, soil and forest physiognomy through GIS. For the prediction diagram of debris flow and landslide risk areas, this study calculated W+ and W-, the weighted values of each factor of Weight Evidence, while overlaying the weighted values of factors. Besides, the diagram showed about 76% in prediction accuracy, and it was also found to have a relatively high correlationship with the areas where such natural disasters actually occurred.

• The Journal of Engineering Geology
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• v.18 no.2
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• pp.191-196
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• 2008

This study is a case study for application of the RTI warning model to Korea which was previously developed to predict landslide potential and occurrence time during a rainfall event. The rainfall triggering index (RTI) is defined as the product of the rainfall intensity I (mm/hr) and the effective accumulated rainfall $R_t$ (mm). This index is used to evaluate the landslide and debris-flow occurrence potential at time t during a rainfall event. The upper critical value ($RTI_{UC}$) of RTI and the lower critical value ($RTI_{LC}$) of RTI can be determined by historical rainfall data of a certain area. When the rainfall intensity exceeds the upper critical value, there are high potential to occur land-slides. The analysis result can predict landslide occurrence time of an area during a rainfall event as well as land-slide potential. The result can also be used as an important data to issue early-warning of landslides. In order to apply the RTI warning model to Korea this study analyzed rainfall data and landslides data in Inje county, Gangwon province, Korea from July 13 to July 19, 2006. According to the analysis result, the rainfall intensity exceeded the upper critical value 23 hours ago, 11 hours ago, and 9 hours ago from 11:00 in the morning, July 16. Therefore, landslide warnings would be issued three times for people evacuation for avoiding or reducing hurts and dam-ages from landslides in mountainous areas of Inje.

• The Journal of Engineering Geology
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• v.28 no.1
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• pp.47-59
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• 2018

At present, there has been a wide range of studies on debris flow in Korea, more specifically, on rainfall characteristics that trigger debris flow including rainfall intensity, rainfall duration, and preceding rainfall. the prediction of landslide / debris flow relies on the criteria for landslide watch and warning by the Korea Forest Service (KFS, 2012). Despite this, it has been found that most incidents of debris flow were caused by rainfall above the level of landslide watch, maximum hourly rainfall, extensive damage was caused even under the watch level. Under these circumstances, we calculated a rainfall triggering index (RTI) using the main factors that trigger debris flow-rainfall, rainfall intensity, and cumulative rainfall-to design a more sophisticated watch / warning criteria than those by the KFS. The RTI was classified into attention, caution, alert, and evacuation, and was assessed through the application of two debris flow incidents that occurred in Umyeon Mountain, Seoul, and Cheongju, Inje, causing serious damage and casualties. Moreover, we reviewed the feasibility of the RTI by comparing it with the KFS's landslide watch / warning criteria (KFS, 2012).

• Journal of Industrial Technology
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• v.36
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• pp.57-63
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• 2016

In this study characteristics of debris flow landslide were investigated on the focus of debris flow disaster occurred by heavy rainfall in 2013 at Goeun-ri around Kaeryoung Mt. in Chuncheon-si. Appropriate method for estimating scale of debris flow was investigated by comparing those values from soil loss by Universal Soil Loss Equation, debris flow yield rate obtained by field survey of investigating debris flow path from initiation and erosion to deposition and other methods. As results of this study, it might be an opportunity of contributing to construct the data base for determining the size of erosion control facilities in future.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.2
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• pp.1-10
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• 2009

Recently the large scale of forest disaster such as landslide and forest fire gives a very bad impact on not only forest ecosystem but also farm business so that it has became the main issue of environmental problems. In this study, the landslide hazard area forecast method was developed by considering not only the topographic thematic maps based on GIS and satellite images but also amount of rainfall data, which are very important factors of landslide. Uljin-gun was selected as the study area and the GIS weight score and overlay analysis were applied to topographical map and meteorological observation map. Finally the landslide area distribution map was constructed by considering the evaluation criteria. Also, the accuracy could be acquired by comparing the landslide hazard area forecast map and real damaged area extracted from satellite image.

• Journal of Korean Society of societal Security
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• v.1 no.4
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• pp.57-64
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• 2008

Korea has been suffered from serious damages of lives and properties, due to landslides that are triggered by heavy rains in every monsoon season. This study developed the physically based landslide prediction model which consists of 3 parts, such as 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 areas predicted on the GIS map. The matching rate of this model to the actual data was 84.8%. The relation between hydrological and landform factors and potential landslide were analyzed.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.03a
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• pp.444-455
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• 2009

Great concerns on geotechnical risk & hazard assessment have been increased due to human and economic damage by natural disasters with recent global climate changes. In this paper, geotechnical problems in particular, landslides which is interested in European countries and North America, were mainly discussed. For these, 18 key topics on geotechnical risk and hazards which had been discussed at the LARAM 2008 workshop in Italy were analyzed after grouping by subjects. Main topic contents consisted of applications such as field measurement, early warning systems, uncertainty analysis of parameters using radar, optical data and statistical theory and so on. And the problems related to analysis of vulnerability and deformation due to earthquakes, investigation of gas zone using seismic reflection data in a landslide area, risk quantification and hazard assessment of landslide movements and multi-dimensional analysis for stability of complex slopes were attracted. Also, there were studies on risk matters of cultural heritage, the blockglide of clayey ground, simulations of debris flows based on GIS, quantification of the failure processes of rock slopes, a meshless method for 3D crack modelling, and finally risk assessment for cryological processes due to global warming.

• Journal of the Korean Geotechnical Society
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• v.28 no.4
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• pp.23-33
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• 2012

Recently, landslide disasters caused by severe rain storms and typhoons have been frequently reported. Due to the geomorphologic characteristics of Korea, considerable portion of urban area and infrastructures such as road and railway have been constructed near mountains. These infrastructures may encounter the risk of landslide and debris flow. It is important to evaluate the highly risky locations of landslide and to prepare measures for the protection of landslide in the process of construction planning. In this study, a landslide-risk prediction equation is proposed based on the statistical analysis of 423 landslide data set obtained from field surveys, disaster reports on national road, and digital maps of landslide area. Each dataset includes geomorphologic characteristics, soil properties, rainfall information, forest properties and hazard history. The comparison between the result of proposed equation and actual occurrence of landslide shows 92 percent in the accuracy of classification. Since the input for the equation can be provided within short period and low cost, and the results of equation can be easily incorporated with hazard map, the proposed equation can be effectively utilized in the analysis of landslide-risk for large mountainous area.

• Journal of The Geomorphological Association of Korea
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• v.27 no.1
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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.