• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.353-356
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• 2008

최근 대규모 산림재해로 인한 산림환경 훼손 및 산림 농가의 피해는 물론 산림생태계에도 나쁜 영향을 미치고 있으며 이는 사회적으로 매우 민감한 환경문제로서 국민의 주요 관심사가 되고 있다. 본 연구에서는 울진군 전체를 대상으로 GIS 및 RS 기법을 이용하여 다양한 산사태 관련 인자들을 추출 하여 이를 기반으로 GIS 중첩 및 가중치 분석을 통하여 울진군의 산사태 발생 가능 위험지역의 분포도를 작성하고자 한다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.1
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• pp.181-194
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• 2013

Landslides are known as gravitational mass movements that can carry the flow materials ranging in size from clay to boulders. The various types of landslides are differentiated by rate and depositional features. Indeed, flow characteristics are observed from very slow-moving landslides (e.g., mud slide and mud flow) to very fast-moving landslides (e.g., debris avalanches and debris flows). From a geomechanical point of view, shear-rate-dependent shear strength should be examined in landslides. This paper presents the design of advanced ring-shear apparatus to measure the undrained shear strength of debris flow materials in Korea. As updated from conventional ring-shear apparatus, this apparatus can evaluate the shear strength under different conditions of saturation, drainage and consolidation. We also briefly discussed on the ring shear apparatus for enforcing sealing and rotation control. For the materials with sands and gravels, an undrained ring-shear test was carried out simulating the undrained loading process that takes place in the pre-existing slip surface. We have observed typical evolution of shear strength that found in the literature. This paper presents the research background and expected results from the ring-shear apparatus. At high shear speed, a temporary liquefaction and grain-crushing occurred in the sliding zone may take an important role in the long-runout landslide motion. Strength in rheology can be also determined in post-failure dynamics using ring-shear apparatus and be utilized in debris flow mobility.

• Economic and Environmental Geology
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• v.46 no.4
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• pp.301-312
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• 2013

Landslides are caused by complex interaction among a large number of interrelated factors such as topography, geology, forest and soils. In this study, a comparative study was carried out using fuzzy relationship method and artificial neural network to evaluate landslide susceptibility. For landslide susceptibility mapping, maps of the landslide occurrence locations, slope angle, aspect, curvature, lithology, soil drainage, soil depth, soil texture, forest type, forest age, forest diameter and forest density were constructed from the spatial data sets. In fuzzy relation analysis, the membership values for each category of thematic layers have been determined using the cosine amplitude method. Then the integration of different thematic layers to produce landslide susceptibility map was performed by Cartesian product operation. In artificial neural network analysis, the relative weight values for causative factors were determined by back propagation algorithm. Landslide susceptibility maps prepared by two approaches were validated by ROC(Receiver Operating Characteristic) curve and AUC(Area Under the Curve). Based on the validation results, both approaches show excellent performance to predict the landslide susceptibility but the performance of the artificial neural network was superior in this study area.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_1
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• pp.431-437
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• 2013

Landslides are caused by earthquakes or heavy rains. Recently the incidence of landslides has been increased. However, it is impossible to predict the occurrence of landslide exactly. The purpose of this research is that subdivide the classes of elements in the landslide management system by using spatial analysis technique and AHP method. The existing landslide management system is only comprised of weighted value the slope elements without weighted value about the slop direction elements. For the accuracy improvement in landslide occurrence point, weighted value about the slope direction should be considered. This research is focused on segmentation in slope direction three categories. If the direction of landslide does not affect the structure, I do not think the subject is worth considerating. Based on these results will discuss the improvements in Landslides management systems. Analysis results, segmentation on the slope and the slope direction are needed. Segmented categories about topography elements will be increase the accuracy of landslides management system. Also, since topography of the elements is only considered, segmentation of different elements is needed.

• Korean Journal of Soil Science and Fertilizer
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• v.24 no.3
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• pp.183-191
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• 1991

A study was conducted to determine the influence of soil chemical properties on the distribution and forms of cadmium (Cd), lead (Pb), copper(Cu) and Zinc(Zn) in paddy soils near zinc mines. A sequential extraction procedure was used to fractionate the heavy metals in soils into the designated from of water soluble, exchangeable, organically bound, oxide/carbonate, and sulfide/residual. The predominant form of Cd, Pb, Cu and Zn in the soils was found to be sulfide/residual form. Oxide/carbonate Cd and Pb and organically bound Cu were high, while exchangeable Pb and Cu were very low. Water soluble Cd, Pb and Cu were not detected in the soils. The percentages of the heavy metals content in exchangeable fractions were inversely correlated with those in sulfide/residual fractions in the soils. Exchangeable Cd and Zn and the oxide/carbonate Pb were shifting to the sulfide/residual form with soil depth and the chemical forms of Cu were not changed. Organically bound Cu was positively correlated with soil organic matter content but Cd, Pb and Zn were not. The percentages of Cd, Pb and Zn content in exchangeable forms decreased with soil pH, while those in oxide/carbonate and sulfide/residual forms increased with soil pH. The amounts of oxide/carbonate and sulfide/residual forms of pb were higher than those of Cd and Zn at same soil pH.

• Economic and Environmental Geology
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• v.45 no.6
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• pp.697-707
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• 2012

The quantitative landslide susceptibility assessment methods can be divided into statistical approaches and geomechanical approaches based on the consideration of the triggering factors and landslide models. The geomechanical approach is considered as one of the most effective approaches since this approach proposes physical slope model and considers geomorphological and geomechanical properties of slope materials. Therefore, the geomechanical approaches has been used widely in landslide susceptibility analysis using the infinite slope model as physical slope model. However, the previous studies assumed constant groundwater level for broad study area without the consideration of rainfall intensity and hydraulic properties of soil materials. Therefore, in this study, landslide susceptibility assessment was implemented using the coupled infinite slope model with hydrologic model. For the analysis, geomechanical and hydrualic properties of slope materials and rainfall intensity were measured from the soil samples which were obtained from field investigation. For the practical application, the proposed approach was applied to Jinbu area, Gangwon-Do which was experienced large amount of landslides in July 2006. In order to compare to the proposed approach, the previous approach was used to analyze the landslide susceptibility using randomly selected groundwater level. Comparison of the results shows that the accuracy of the proposed method was improved with the consideration of the hydrologic model.

• Journal of the Korean Geographical Society
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• v.50 no.5
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• pp.485-503
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• 2015

This study aims at evaluating geomorphological classification systems to predict the occurrence of landslides in mountainous region in Korea. Geomorphological classification systems used in this study are Catena, TPI, and Geomorphons. Study sites are Gapyeong-gun, Hoengseong-gun, Gimcheon-si, Yeoju-si/Yicheon-si in which landslide occurrence data were collected by local governments from 2001-2014. Catena method has objective classification standard to compare among regions objectively and understand the result intuitively. However, its procedure is complicated and hard to be automated for the general public to use it. Both TPI and Geomorphons have simple procedure and GIS-extension, therefore it has high accessibility. However, the results of both systems are highly dependent on the scale, and have low relevance to geomorphological formation process because focusing on shape of terrain. Three systems have low compatibility, therefore unified concept are required for broad use of landform classification. To assess the effectiveness of prediction on landslide by each geomorphological classification system, 50% of geomorphological classes with higher landslide occurrence are selected and the total landslide occurrence in selected classes are calculated and defined as 'predictive ability'. The ratio of terrain categorized by 'predictive ability' to whole region is defined as 'vulnerable area ratio'. An indicator to compare three systems which is predictive ability divided by vulnerable area ratio was developed to make a comprehensive judgment. As a result, Catena ranked the highest in suitability.

• Spatial Information Research
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• v.20 no.1
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• pp.9-18
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• 2012

South Korea has many landslides caused by heavy rains during summer time recently and the landslides continue to cause damages in many places. These landslides occur repeatedly each year, and the frequency of landslides is expected to increase in the coming future due to dramatic global climate change. In Korea, 81.5% of the population is living in urban areas and about 1,055 million people are living in Seoul. In 2011, the landslide that occurred in Seocho-dong killed 18 people and about 9% of Seoul's area is under the same land conditions as Seocho-dong. Even the size of landslide occurred in a city is small, but it is more likely to cause a big disaster because of a greater population density in the city. So far, the effort has been made to identify landslide vulnerability and causes, but now, the new dem and arises for the prediction study about the areal extent of disaster area in case of landslides. In this study, the diffusion model of the landslide disaster area was established based on Cellular Automata(CA) to analyze the physical diffusion forms of landslide. This study compared the accuracy with the Seocho-dong landslide case, which occurred in July 2011, applying the SCIDDICA model and the CAESAR model. The SCIDDICA model involves the following variables, such as the movement rules and the topographical obstacles, and the CAESAR model is also applied to this process to simulate the changes of deposition and erosion.

• Journal of Gifted/Talented Education
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• v.24 no.6
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• pp.1039-1051
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• 2014

This study aims to measure intelligence (cognitive characteristics), self-esteem, mathematical attitudes, and scientific attitudes (affective characteristics) of gifted students from low-income families, and to identify the relationship among these variables. 147 students in the lower grades of elementary schools who were enrolled to university-based gifted education centers were participants of the study. The results showed that the percentile scores of each variable were 85% for intelligence, 75.6% for self-esteem, 73.3% for mathematical attitudes, and 71.3% for mathematical attitudes. There was no statistically significant relationship between intelligence and the affective characteristics (i.e., self-esteem, mathematical attitudes, and scientific attitudes), while statistically significant relationships were shown between self-esteem and mathematical attitudes (r=.448, p=.000), between self-esteem and scientific attitudes (r=.522, p=.000), and between mathematical attitudes and scientific attitudes (r=.448, p=.000). The results suggest that although the gifted students from low-income families show lower levels compared to other gifted student groups, their potential level of giftedness is considerably high, which calls for appropriate educational support systems designed for this population.

• 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.