• 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.

• Magazine of the Korean Society of Agricultural Engineers
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• v.53 no.1
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• pp.27-40
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• 2011

토양정보는 식량생산, 지속적인 토지이용 계획, 종다양성 평가에 사용되는 기본적인 자료이다. 우리나라 토양조사의 역사, 다양한 축척의 토양도 구축과 토양검정, 토양도와 토양검정 자료의 특성, 농업환경변동 모니터링을 통한 일반농경지 및 취약농경지 토양, 토양정보의 전산화에 따른 토양데이터베이스와 토양정보시스템 소개, 구축된 토양정보의 활용과 향후 방향에 대해 논하였다. 40여년 동안 수행되었던 국책 토양조사사업 결과 두 종류의 토양 데이터베이스가 구축되었는데, 다양한 축척의 토양도(1:250,000, 1:50,000, 1:25,000, 1:5,000)를 GIS DB로 전산화한 수치토양도 DB와 필지 단위로 조사된 화학성 위주의 토양분석 성적을 구축한 토양비옥도 DB이다. 흙토람(http://soil.rda.go.kr)에 서 제공하는 토양전자지도는 총 111종으로 토성, 경사, 지형, 모재, 배수등급, 자갈함량, 유효토심 등 토양 GIS 주제도 50종, 사과, 배, 마늘, 수박 등 작물 재배적지 61종이고, 82종의 토양통계 정보를 제공하고 있다. 최근 에는 친환경농업육성법 시행령에 따른 경작형태 및 오염원별 농경지 토양의 이화학성 및 농업용수 수질 조사 자료를 GIS DB로 구축하여 공간적인 분포와 시계열적인 변화를 분석하는 자료로 활용하고 있다. 농업환경변동 정보는 농업환경자원 인벤토리를 기반으로 일반농경지의 화학성과 농업용수의 수질, 토양 미생물 등의 공간적인 분포와 시간적인 변화 정보를 전문가 그룹에 제공하고 있다. 앞으로는 자연자원의 분포와 변동 정보를 바탕으로 보다 일반적이고 알기 쉽게 가공하여 일반인들에게 농업환경과 자연자원 보전의 중요성을 이해시키는 정보로 제공할 예정이다. 기존의 토양정보와 농업환경정보를 통합하여 컨텐츠를 보다 내실있게 하고 정보 수요자별로 손쉽게 목적에 맞는 접근을 할 수 있는 시스템을 만들어 사용자의 편의성을 강화시키는 방향으로 이끌어 가고자 한다.

• Korean Journal of Environment and Ecology
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• v.22 no.2
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• pp.119-127
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• 2008

This research managed to establish the space information on incidence factors of landslide targeting Bukhansan National Park and aimed at suggesting a basic data for disaster prevention of a landslide for the period to come in Bukhansan National Park through drawing up the map indicating vulnerability to a landslide and ecological risks by the use of overlay analysis and adding-up estimation matrix analysis methods. This research selected slope angle, slope aspect, slope length, drainage, vegetation index(NDVI) and land use as an assessment factor of a landslide and constructed the spatial database at a level of '$30m\times30m$' resolution. The analysis result was that there existed high vulnerability to a landslide almost all over Uidong and Dobong valleys. As for ecological risks, Dobong valley, Yongueocheon valley, Jeongneung valley and Pyeongchang valley were analyzed to be higher, so it is judged that the impact on a landslide risk should be also considered in time of establishing a management plan for these districts for the time to come.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.446-446
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• 2012

농업은 다른 산업과 달리 원천적으로 기후 조건과 변화에 크게 좌우되는 분야로, 기후변화로 인한 영향에 가장 민감한 분야라고 할 수 있다. 안정적이고 지속적인 작물 생산을 위해서는 기후변화가 농업수자원에 미치는 영향에 대하여 정확히 파악하고, 이로 인해 발생할 수 있는 부정적 효과를 최소화하기 위한 연구가 필요하다. 특히 기온 상승, 강수량 및 강우강도 변화, 증발산량 및 일조시간 변화 등의 기후변화는 우리나라의 가뭄 발생의 양상에도 변화를 야기하게 될 것이다. 따라서 현 상황을 바탕으로 미래에 발생할 가뭄에 대하여 예측하고, 그 취약성을 줄이기 위한 합리적인 계획이 필요하다. 즉 기후변화에 대처하기 위해서는 향후 발생할 수 있는 가뭄의 특성을 파악하여 미래 수자원 관리에 활용하기 위한 가뭄특성 분석이 필요하다. 가뭄은 기상학적 가뭄, 기후학적 가뭄, 농업적 가뭄, 대기학적 가뭄, 수문학적 가뭄, 사회경제적 가뭄 등으로 구분할 수 있는데, 일반적으로 강우량 등의 기상조건을 분석하는 방법에서부터 저수량과 유역 유출량, 그리고 토양수분 등의 수문학적 조건들로 가뭄을 분석하는 방법들까지 매우 다양하다. 가뭄의 정량화는 가뭄을 표현하는 대상의 특성에 따라 평가방법이 달라질 수 있다. 가뭄의 경향이나 그 정도를 파악하기 위해서는 하나의 가뭄 지수가 아닌 다양한 항목을 바탕으로 평가가 이루어져 한다. 현재 기후변화와 관련한 가뭄 연구에 있어서 기상학적 가뭄지수인 SPI (Standardized Precipitation Index) 중심으로 많은 연구 이루어졌을 뿐, 농업적 가뭄지수를 바탕으로 한 연구는 이루어지지 않고 있다. 따라서 본 연구에서는 기후변화에 따른 우리나라의 농업가뭄 특성을 분석하기 위하여 토양수분지수 (Soil Moisture Index)를 이용하여 중권역별 가뭄 평가하고 그 변화를 분석하였다. 본 연구를 위하여 이를 위하여 CGCM3.1 (Coupled Global Climate Model Ver. 3.1) 및 LARS-WG (Long Ashton Research Station Weather Generator)를 이용하여 2011년부터 2100년까지의 A1B, A2 및 B1 시나리오별로 기상자료를 생성하고, 이를 바탕으로 SMI 지수를 산정하여 유역별 가뭄 발생 빈도 및 심도를 시나리오별로 분석하였다. 본 연구 결과는 향후 기후변화로 인한 농업가뭄 발생의 양상 및 특성을 파악하고 전망함으로써, 추후 발생할 수 있는 부정적 효과를 최소화하기 위한 대응 전략 및 농업수자원 정책의 기초 자료로 활용될 수 있을 것으로 판단된다.

• Journal of the Korean GEO-environmental Society
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• v.15 no.7
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• pp.39-50
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• 2014

The goal of this work is to develop a framework of methods to entirely evaluate effects of LID (Low Impact Development) on soil-groundwater environmental quality as well as land-scape and ecological factors. For this study, we conducted an extensive literature review. As outcomes, soil-groundwater environmental quality is newly conceptualized as a comprehensive index reflecting (i) groundwater pollution sensitivity (hydrogeological factor), (ii) biochemical contamination, and (iii) biodegradability. The methods of classifying and indexing is shown by combining selection of the items to be measured for soil-groundwater environmental quality and integrating the resulted items comprehensively. In addition, from soil-groundwater environmental quality, land-scape and ecological factors in existing environmental impact assessment a method was developed an overall index which can evaluate effects to environment by using GIS (Geographic Information System) and AHP (Analytic Hierachy Process). For optimizing LID planning, designing and post-evaluation, LCIA (Life Cycle Impact Assessment) was regarded as an appropriate method.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.299-316
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• 2019

The purpose of this study was to analyze landslide susceptibility in the Pyeongchang area using Weight of Evidence (WOE) and Evidential Belief Function (EBF) as probability models and Artificial Neural Networks (ANN) as a machine learning model in a geographic information system (GIS). This study examined the widespread shallow landslides triggered by heavy rainfall during Typhoon Ewiniar in 2006, which caused serious property damage and significant loss of life. For the landslide susceptibility mapping, 3,955 landslide occurrences were detected using aerial photographs, and environmental spatial data such as terrain, geology, soil, forest, and land use were collected and constructed in a spatial database. Seventeen factors that could affect landsliding were extracted from the spatial database. All landslides were randomly separated into two datasets, a training set (50%) and validation set (50%), to establish and validate the EBF, WOE, and ANN models. According to the validation results of the area under the curve (AUC) method, the accuracy was 74.73%, 75.03%, and 70.87% for WOE, EBF, and ANN, respectively. The EBF model had the highest accuracy. However, all models had predictive accuracy exceeding 70%, the level that is effective for landslide susceptibility mapping. These models can be applied to predict landslide susceptibility in an area where landslides have not occurred previously based on the relationships between landslide and environmental factors. This susceptibility map can help reduce landslide risk, provide guidance for policy and land use development, and save time and expense for landslide hazard prevention. In the future, more generalized models should be developed by applying landslide susceptibility mapping in various areas.

• Korean Journal of Environmental Agriculture
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• v.21 no.1
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• pp.7-16
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• 2002

Contamination of groundwater by agrochemicals used in the regional-scale Is now a major environmental problem, and this is especially true for Cheju island where virtually all potable water is from groundwater. The objective of this study was to assess leaching potential of eight pesticides in soils of citrus orchards using groundwater ubiquity score (GUS), retardation factor (RF) and attenuation factor (AF). Considering GUS estimated in 30 citrus orchard soils, metribuzin and metolachlor were classified as leacher, alachlor in volcanic ash soils and linuron in non-volcanic soils were classified as leacher, but chlorothalonil and chlorpyrifos were classified as non-leacher. For RF values, metribuzin was classified to be mobile in soils of low organic carbon, metolachlor and alachlor were classified to be moderately immobile in most soils, but linuron, diuron, diniconazole, chlorothalonil and chlorpyrifos were all classified to be very immobile. For AF values, diniconazole, chlorothalonil, and chlorpyrifos were classified to be very unlikely leachable in all of the soils, metribuzin was classified to be likely leachable, and metolahclor, alachlor, linuron and diuron were classified to be leachable only in non-volcanic soils. Although there were some variations in the relative potential of teachability of pesticides estimated with the three different indices, the ranking was essentially determined on the base of the intrinsic properties of the chemicals and environmental properties. Among the eight pesticides, metribuzin, metolachlor, and alachlor, which have high water solubility and low $K_{oc}$ values, have a significant leaching potential especially in non-volcanic ash soils of low organic carbon. But diniconazole, chlorothalonil, and chlorpyrifos, which have low water solubility and high $K_{oc}$ values, were classified to be very immobile in all of the soils. Therefore, to lower the possibility of pesticide contamination of the groundwater in Cheju island, those pesticides which have high water solubility and low $K_{oc}$ values should be used with care in soils of low organic carbon including non-volcanic ash soils.

• Journal of Agricultural Extension & Community Development
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• v.31 no.3
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• pp.153-175
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• 2024

This study aims to identify the factors influencing farmers' barriers to adopting climate-smart agriculture (CSA) practices in the coastal area of Bangladesh. We have used a semi-structured, pre-tested questionnaire to collect quantitative and qualitative data from 160 coastal farmers who had at least 10 years of farming experience. We found that internal consistency (Cronbach's alpha) values for the items of agricultural vulnerability, adopted CSA practices, and perceived barriers to adopting CSA practices were 0.72, 0.74, and 0.79, respectively. The Agricultural Vulnerability Index (AGVI) found increased soil salinity in the dry season, reduced freshwater resources, poor seed germination, and more pests and diseases as vulnerabilities in agriculture. The Adoption Index (ADI) identified most adopted CSA practices as including growing HYVs of vegetables on high land, short-duration HYVs of rice, using compost, proper fertilizer management, and sarjon cultivation methods. The Barrier Index (BI) showed that high initial investment costs, poor embankment infrastructure, low crop prices, a lack of solar-powered irrigation systems, and insufficient technical assistance from local extension organizations are the main barriers to the adoption of CSA practices. Farmers' age, education, training experience, job satisfaction, and use of information sources have influenced barriers to adopting CSA practices. The study suggested policies on coastal farmer competency development, ensuring crop insurance, providing interest-free credit policies, and a fair pricing system for crops.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.3
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• pp.187-200
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• 2023

This study analyzed the seismic fragility of bored tunnels based on their surrounding conditions and suggested a representative seismic fragility model. By analyzing the existed seismic fragility models developed for bored tunnels, we developed weighted combination models for each surrounding conditions, such as ground conditions and depth of the tunnel. The seismic fragility curves use the peak ground acceleration (PGA) as a parameter. When the PGA was 0.3 g, the probability of damage exceeding minor or slight damage was 20% for depth of 50 m or less, 10% for depth between 50 m and 100 m, and 3% for depth of 100 m or more. It was also found that the probability of damage was higher for the same PGA and depth when the surrounding ground was rock rather than soil. The probability of damage decreases as the depth increase. This study is expected to be used for developing a comprehensive seismic fragility function for tunnels in the future.

• Journal of the Korean Geotechnical Society
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• v.33 no.10
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• pp.5-14
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• 2017

The hazard maps for predicting collapse on natural slopes consist of a combination of topographic, hydrological, and geological factors. Topographic factors are extracted from DEM, including aspect, slope, curvature, and topographic index. Hydrological factors, such as soil drainage, stream-power index, and wetness index are most important factors for slope instability. However, most of the urban areas are located on the plains and it is difficult to apply the hazard map using the topography and hydrological factors. In order to evaluate the risk of subsidence of flat and low slope areas, soil depth and groundwater level data were collected and used as a factor for interpretation. In addition, the reliability of the hazard map was compared with the disaster history of the study area (Gangnam-gu and Yeouido district). In the disaster map of the disaster prevention agency, the urban area was mostly classified as the stable area and did not reflect the collapse history. Soil depth, drainage conditions and groundwater level obtained from boreholes were added as input data of hazard map, and disaster vulnerability increased at the location where the actual subsidence points. In the study area where damage occurred, the moderate and low grades of the vulnerability of previous hazard map were 12% and 88%, respectively. While, the improved map showed 2% high grade, moderate grade 29%, low grade 66% and very low grade 2%. These results were similar to actual damage.