한국측량학회지 (Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography)

  • 제34권6호
  • /
  • Pages.597-607
  • /
  • 2016
  • /
  • 1598-4850(pISSN)
  • /
  • 2288-260X(eISSN)
한국측량학회 (Korean Society of Surveying, Geodesy, Photogrammetry and Cartography)
권호 표지
DOI QR코드

DOI QR Code

A Comparative Study of the Frequency Ratio and Evidential Belief Function Models for Landslide Susceptibility Mapping

  • Yoo, Youngwoo (Dept. of Urban Engineering, Dongeui University) ;
  • Baek, Taekyung (Dept. of Urban Engineering, Dongeui University) ;
  • Kim, Jinsoo (Dep. of Spatial Information Engineering, Pukyong National University) ;
  • Park, Soyoung (Graduate School of Earth Environmental Hazard System, Pukyong National University)
  • 투고 : 2016.11.25
  • 심사 : 2016.12.28
  • 발행 : 2016.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

The goal of this study was to analyze landslide susceptibility using two different models and compare the results. For this purpose, a landslide inventory map was produced from a field survey, and the inventory was divided into two groups for training and validation, respectively. Sixteen landslide conditioning factors were considered. The relationships between landslide occurrence and landslide conditioning factors were analyzed using the FR (Frequency Ratio) and EBF (Evidential Belief Function) models. The LSI (Landslide Susceptibility Index) maps that were produced were validated using the ROC (Relative Operating Characteristics) curve and the SCAI (Seed Cell Area Index). The AUC (Area under the ROC Curve) values of the FR and EBF LSI maps were 80.6% and 79.5%, with prediction accuracies of 72.7% and 71.8%, respectively. Additionally, in the low and very low susceptibility zones, the FR LSI map had higher SCAI values compared to the EBF LSI map, as high as 0.47%p. These results indicate that both models were reasonably accurate, however that the FR LSI map had a slightly higher accuracy for landslide susceptibility mapping in the study area.

키워드

참고문헌

  1. Akgun, A. (2012), A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at Izmir, Turkey, Landslides, Vol. 9, No. 1, pp. 93-106. https://doi.org/10.1007/s10346-011-0283-7
  2. Althuwaynee, O.F., Pradhan, B., and Lee, S. (2012), Application of an evidential belief function model in landslide susceptibility mapping, Computers & Geosciences, Vol. 44, pp. 120-135. https://doi.org/10.1016/j.cageo.2012.03.003
  3. Awasthi, A. and Chauhan, S.S. (2011), Using AHP and Dempster-Shafer theory for evaluating sustainable transport solutions, Environmental Modelling & Software, Vol. 26, No. 6, pp. 787-796. https://doi.org/10.1016/j.envsoft.2010.11.010
  4. Beven, K.J. and Kirkby, M.J. (1979), A physically based, variable contributing area model of basin hydrology, Hydrological Sciences Journal, Vol. 24, No. 1, pp. 43-69. https://doi.org/10.1080/02626667909491834
  5. Busan Metropolitan City Namgu (2016), Statistics annual report, Busan Metropolitan City Namgu, Busan, http://www.bsnamgu.go.kr (last date accessed: 19 November 2016).
  6. Bui, D.T., Pradhan, B., Lofman, O., Revhaug, I., and Dick, O.B. (2012), Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models, Catena, Vol. 96, pp. 28-40. https://doi.org/10.1016/j.catena.2012.04.001
  7. Cruden, D.M. (1991), A simple definition of a landslide, Bulletin of Engineering Geology and the Environment, Vol. 43, No. 1, pp. 27-29.
  8. Gerath, R., Hunger, O., and van Dine, D. (1996), Terrain stability mapping in British Columbia, Earth Science Task Force of the British Columbia Resources Inventory Committee, Victoria, BC.
  9. Ermini, L., Catani, F., and Casagli, N. (2005), Artificial neural networks applied to landslide susceptibility assessment, Geomorphology, Vol. 66, No. 1, pp. 327-343. https://doi.org/10.1016/j.geomorph.2004.09.025
  10. Fawcett, T. (2006), An introduction to ROC analysis, Pattern recognition letters, Vol. 27, No. 8, pp. 861-874. https://doi.org/10.1016/j.patrec.2005.10.010
  11. Hong, H., Chen, W., Xu, C., Youssef, A.M., Pradhan, B., and Tien Bui, D. (2016), Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy, Geocarto International, pp. 1-16.
  12. Jang, D.H., Park, N.W., Chi, K.H., Kim, M.K., and Chung, C.J. (2004), Landslide susceptibility analysis in the Boeun area using a GIS-based bayesian prediction model, Journal of the Korean Geomorphological Association, Vol. 11, No. 3, pp. 13-23. (in Korean with English abstract)
  13. Korean Statistical Information Service (2016), Present condition of landslide damage, Statistics Korea, Daejeon, http://kosis.kr (last date accessed: 19 November 2016).
  14. Lee, S. and Pradhan, B. (2007), Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models, Landslides, Vol. 4, No. 1, pp. 33-41. https://doi.org/10.1007/s10346-006-0047-y
  15. Lee, S. and Sambath, T. (2006), Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models, Environmental Geology, Vol. 50, No. 6, pp. 847-855. https://doi.org/10.1007/s00254-006-0256-7
  16. Lee, S., Hwang, J., and Park, I. (2013), Application of datadriven evidential belief functions to landslide susceptibility mapping in Jinbu, Korea, Catena, Vol. 100, pp. 15-30. https://doi.org/10.1016/j.catena.2012.07.014
  17. Lee, M.J., Lee, S., and Jeon, S.W. (2012), Landslide hazard mapping and verification using probability rainfall and artificial neural networks, Journal of the Korean Association of Geographic Information Studies, Vol 15, No. 2, pp. 57-70. (in Korean with English abstract) https://doi.org/10.11108/kagis.2012.15.2.057
  18. Mohammady, M., Pourghasemi, H.R., and Pradhan, B. (2012), Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster-Shafer, and weights-of-evidence models, Journal of Asian Earth Sciences, Vol. 61, pp. 221-236. https://doi.org/10.1016/j.jseaes.2012.10.005
  19. Moore, I. D. and Wilson, J. P. (1992), Length-slope factors for the Revised Universal Soil Loss Equation: Simplified method of estimation, Journal of Soil and Water Conservation, Vol. 47, No. 5, pp. 423-428.
  20. Moore, I.D., Grayson, R.B., and Ladson, A.R. (1991), Digital terrain modelling: a review of hydrological, geomorphological, and biological applications, Hydrological Processes, Vol. 5, No. 1, pp. 3-30. https://doi.org/10.1002/hyp.3360050103
  21. Oh, H.J. (2010), Landslide detection and landslide susceptibility mapping using aerial photos and artificial neural networks, The Korean Society of Remote Sensing, Vol. 26, No. 1, pp. 47-57. (in Korean with English abstract) https://doi.org/10.7780/kjrs.2010.26.1.47
  22. Pourghasemi, H.R. and Kerle, N. (2016), Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran, Environmental Earth Sciences, Vol. 75, No. 3, pp. 1-17. https://doi.org/10.1007/s12665-015-4873-x
  23. Pourghasemi, H.R., Pradhan, B., Gokceoglu, C., and Moezzi, K.D. (2013), A comparative assessment of prediction capabilities of Dempster-Shafer and weights-of-evidence models in landslide susceptibility mapping using GIS. Geomatics, Natural Hazards and Risk, Vol. 4, No. 2, pp. 93-118. https://doi.org/10.1080/19475705.2012.662915
  24. Pradhan, B. (2013), A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS, Computers & Geosciences, Vol. 51, pp. 350-365. https://doi.org/10.1016/j.cageo.2012.08.023
  25. Saito, H., Nakayama, D., and Matsuyama, H. (2009), Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: the Akaishi Mountains, Japan, Geomorphology, Vol. 109, No. 3, pp. 108-121. https://doi.org/10.1016/j.geomorph.2009.02.026
  26. Schuster, R.L. (1996), Socioeconomic significance of landslides, Landslides: Investigation and Mitigation. Washington (DC): National Academy Press. Transportation Research Board Special Report, 247, pp. 12-35.
  27. Su, C., Wang, L., Wang, X., Huang, Z., and Zhang, X. (2015), Mapping of rainfall-induced landslide susceptibility in Wencheng, China, using support vector machine, Natural Hazards, Vol. 76, No. 3, pp. 1759-1779. https://doi.org/10.1007/s11069-014-1562-0
  28. Suzen, M.L. and Doyuran, V. (2004), A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate, Environmental Geology, Vol. 45, No. 5, pp. 665-679. https://doi.org/10.1007/s00254-003-0917-8
  29. Tangestani, M.H. (2009), A comparative study of Dempster-Shafer and fuzzy models for landslide susceptibility mapping using a GIS: An experience from Zagros Mountains, SW Iran, Journal of Asian Earth Sciences, Vol. 35, No. 1, pp. 66-73. https://doi.org/10.1016/j.jseaes.2009.01.002
  30. Wang, H.B. and Sassa, K. (2005), Comparative evaluation of landslide susceptibility in Minamata area, Japan, Environmental Geology, Vol. 47, No. 7, pp. 956-966. https://doi.org/10.1007/s00254-005-1225-2
  31. Williams, C.J., Lee, S.S., Fisher, R.A., and Dickerman, L.H. (1999), A comparison of statistical methods for prenatal screening for Down syndrome, Applied Stochastic Models in Business and Industry, Vol. 15, No. 2, pp. 89-101. https://doi.org/10.1002/(SICI)1526-4025(199904/06)15:2<89::AID-ASMB366>3.0.CO;2-K
  32. Yalcin, A., Reis, S., Aydinoglu, A.C., and Yomralioglu, T. (2011), A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey, Catena, Vol. 85, No. 3, pp. 274-287. https://doi.org/10.1016/j.catena.2011.01.014
  33. Yeon, Y.K. (2011), Evaluation and analysis of Gwangwondo landslide susceptibility using logistic regression, The Korean Association of Geographic Information Studies, Vol. 14, No. 4, pp. 116-127. (in Korean with English abstract) https://doi.org/10.11108/kagis.2011.14.4.116
  34. Yilmaz, I. (2009), Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat-Turkey), Computers & Geosciences, Vol. 35, No. 6, pp. 1125-1138. https://doi.org/10.1016/j.cageo.2008.08.007