Landslide Susceptibility Apping and Comparison Using Probabilistic Models: A Case Study of Sacheon, Jumunzin Area, Korea

확률론적 모델을 이용한 산사태 취약성 지도 분석: 한국 사천면과 주문진읍을 중심으로

  • Park, Sung-jae (Division of Science Education, Kangwon National University) ;
  • Kadavi, Prima Riza (Division of Science Education, Kangwon National University) ;
  • Lee, Chang-wook (Division of Science Education, Kangwon National University)
  • 박성재 (강원대학교 과학교육학과) ;
  • ;
  • 이창욱 (강원대학교 과학교육학과)
  • Received : 2018.05.18
  • Accepted : 2018.07.19
  • Published : 2018.10.31


The purpose of this study is to create landslide vulnerability using frequency ratio (FR) and evidential belief functions (EBF) model which are two methods of probability model and to select appropriate model for each region through comparison of results in Sacheon-myeon and Jumunjin-eup of Gangneung. 762 locations in Sacheon-myeon and 548 landscapes in Jeonju-eup were constructed based on the interpretation of aerial photographs. Half of each landslide point was randomly selected for modeling and remaining landslides were used for verification purposes. Twenty landslide-inducing factors classified into five categories such as topographic elements, hydrological elements, soil maps (1:5,000), forest maps (1:5,000), and geological maps (1:25,000) were considered for the preparation of landslide vulnerability in the study. The relationship between landslide occurrence and landslide inducing factors was analyzed using FR and EBF models. The two models were then verified using the AUC (curve under area) method. According to the results of verification, the FR model (AUC = 81.2%) was more accurate than the EBF model (AUC = 78.9%) at Jeonjun-eup. In the Sacheon-myeon, the EBF model (AUC = 83.6%) was more accurate than the FR model (AUC = 81.6%). Verification results show that FR model and EBF model have high accuracy with accuracy of around 80%.


Supported by : 한국연구재단


  1. Althuwaynee, O.F., B. Pradhan, and S. Lee, 2012. Application of an evidential belief function model in landslide susceptibility mapping, Computers & Geosciences, 44: 120-135.
  2. Conoscenti, C., M. Ciaccio, N.A. Caraballo-Arias, A. Gomez-Gutierrez, E. Rotigliano, and V. Agnesi, 2015. Assessment of susceptibility to earth-flow landslide using logistic regression and multivariate adaptive regression splines: a case of the Bence River basin (western Sicily, Italy), Geomorphology, 242: 49-64.
  3. Daum, 2018. Daum Map,, Accessed on Dec. 12, 2017.
  4. Dehnavi, A., I.N. Aghdam, B. Pradhan, and M.H.M. Varzandeh, 2015. A new hybrid model using step-wise weight assessment ratio analysis (SWAM) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran, Catena, 135: 122-148.
  5. Grozavu, A., S. Plescan, C.V. Patriche, M.C. Margarint, and B. Rosca, 2013. Landslide susceptibility assessment: GIS application to a complex mountainous environment, In: Jacek, K., Katarzyna, O., Andrzej, B., Bartlomiej, W. (Eds.), The Carpathians: Integrating Nature and Society Towards Sustainability, Springer, Berlin, Heidelberg, Germany, pp. 31-44.
  6. Lee, S. and B. Pradhan, 2007. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models, Landslides, 4(1): 33-41.
  7. Lee, S., J. Choi, and I. Woo, 2004. The effect of spatial resolution on the accuracy of landslide susceptibility mapping: a case study in Boun, Korea, Geosciences Journal, 8(1): 51-60.
  8. Lee, S., S. M. Hong, and H. S. Jung, 2017. A support vector machine for landslide susceptibility mapping in Gangwon Province, Korea, Sustainability, 9(1): 48-62
  9. Mondal, S. and R. Maiti, 2013. Integrating the analytical hierarchy process (AHP) and the frequency ratio (FR) model in landslide susceptibility mapping of Shiv-khola watershed, Darjeeling Himalaya, International Journal of Disaster Risk Science, 4(4): 200-212.
  10. Nasiri Aghdam, I., M.H.M. Varzandeh, and B. Pradhan, 2016. Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran), Environmental Earth Sciences, 75(7): 553-572.
  11. Park, S., C. Choi, B. Kim, and J. Kim, 2013. Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea, Environmental Earth Sciences, 68(5): 1443-1464.
  12. van Westen, C.J., T.W.J. van Asch, and R. Soeters, 2006. Landslide hazard and risk zonation-why is it still so difficult?, Bulletin of Engineering Geology and the Environment, 65(2): 167-184.