• Title/Summary/Keyword: Duksan-ri

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Analyses of Debris Flow Characteristics through Site Investigation (현장 조사를 통한 토석류 특성 분석)

  • Yoo, Nam-Jae;Choi, Young-June;Lee, Cheol-Ju
    • Journal of Industrial Technology
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    • v.29 no.A
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    • pp.135-143
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    • 2009
  • Most of debris flow occurred in Korea have been known to be caused by the heavy rainfall at the soil deposits on the mother rock, affected by conditions of rainfall, topography and geology, especially terrain deposits. A study on debris flow behavior should be carried out by investigating various types of debris flow systematically and analyzing their complicate characteristics in the engineering view points. Tremendous debris flows occurred at Duksan-ri in Inje-gun of Gangwon province during summer in 2006. These sites are selected to study the characteristics of debris flow by investigating the influencing factors on it and analyzing their correlations between them. Most of data about influencing factors were obtained by visiting sites in field.

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Landslide Risk Assessment in Inje Using Logistic Regression Model (로지스틱 회귀분석을 이용한 인제군 산사태지역의 위험도 평가)

  • Lee, Hwan-Gil;Kim, Gi-Hong
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.30 no.3
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    • pp.313-321
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    • 2012
  • Korea has been continuously affected by landslides, as 70% of the land is covered by mountains and most of annual rainfall concentrates between June and September. Recently, abrupt climate change affects the increase of landslide occurrence. Gangwon region is especially suffered by landslide damages, because the most of the part is mountainous, steep, and having shallow soil. In this study, a landslide risk assessment model was developed by applying logistic regression to the various data of Duksan-ri, Inje-eup, Inje-gun, Gangwon-do, which has suffered massive landslide triggered by heavy rain in July 2006. The information collected from field investigation and aerial photos right after the landslide of study area were stored in GIS DB for analysis. Slope gradient entered in two ways-as categorical variable and as linear variable. Error matrix for each case was made, and developed model showed the classification accuracy of 81.4% and 81.9%, respectively.