عنوان مقاله [English]
Landslides are major natural hazards and adopting a regional strategy is very necessary to reduce its damages and maintains natural and human resources. The purposes of this study are the recognition of effective factors in landslide and the zonation and assessment of in terms of the occurrence of this phenomenon using the Dempster-Shafer theory and GIS technique. In this research with integration of Landslide map and effective factors maps such as lithology, land use, slope angle, slope aspect, elevation, precipitation, distance to fault, distance to road, and density of drainage were done analysis of hazard. Finally, landslide occurrence zones were recognized from very low risk to very high risk. Total area of region is 1780516, 12/66 percentage of area (237259) existed in very high risk,12/78 percentage of area (239045) existed in high resk, 21/24 percentage of area (397316) existed in medium,29/33 percentage of area (548649) existed in low and 23/96 percentage of area (448247) existed in very low class. Model evaluated using one to third of landslide points, Frequency Ratio (FR), Seed Cell Area Index (SCAI) and ROC. The results show that Frequency Ratio and Seed Cell Area Index indicate appropriate accuracy of classification to 5 class. Also accuracy of ROC in Dempster-Shafer theory with AUC (%73) indicate high correlation between Risk map and Landslide hazard map and good evaluation of model.The results of these studies can be used as fundamental information by environmental managers and planners.
10. Dai, F.C. and C.F. Lee. 2001. Terrain-based mapping of landslide susceptibility using a geographical information system: a case study. Canadian Geotechnical Journal, 38: 911–923.
11. Dempster, A.P. 1967. Upper and lower probabilities induced by a multivalued mapping. Annals of Mathematical Statistics, 38: 325–339.
12. Denoeux, T. 2000. A neural network classiﬁer based on Dempster–Shafer theory. IEEE transactions on systems, man and cybernetics. System and Humans, 30(2): 131–150.
13. Fatemi Aghda, S.M., J. Ghayoumian, M. Teshnehlab and A. Ashghali Farahani. 2005. Assessment of landslide hazard by using fuzzy logic (case study: Rudbar area). Journal of Science, 1: 43-64.
14. Greco, R., M. Sorriso–Valvo and E. Catalano. 2007. Logistic regression analysis in the evaluation of mass movement's susceptibility, case study: Calabria, Italy. Engineering Geology, 89: 47-66.
15. Gorsevski, P.V., P.E. Gessler, J. Boll, W.J. Elliot and R.B. Foltz. 2006. Spatially and temporally distributed of landslide susceptibility. Geomorphology, 80: 178- 198.
16. Izadi, Z. 2007. Risk zonation of landslide occurrence using statistical methods (case study: Fereydonshahr Basin(. AM. Thesis, university of Isfahan, 125 pages (in Persian).
17. Kayastha, P., M.R. Dhital and F.D. Smedt. 2012. Landslide susceptibility mapping using the weight of evidence method in the Tinau Watershed, Nepal. Nat Hazards, 63: 479-498.
18. Kohlas, J. and P.A. Monney. 1995. A mathematical theory of Hints. An approach to the Dempster-Shafer theory of evidence. Springer-Verlag, Berlin.
19. Komac, M. 2006. A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in Perialpine, Slovenia. Geomorphology, 74(1-4): 17-28.
20. Lan, H.X., C.H. Zhou, L.J. Wang and H.J. Zhang. 2004. Landslide watershed, Yunnan, China. Engineering Geology, 76: 101-128.
21. Lee, S. and J. Choi. 2004. Landslide susceptibility mapping using GIS and the weight of evidence model. International Journal of Geographical Information Science, 18(8): 789- 814.
22. Lee, S. and T. Sambath. 2006. Landslide susceptibility mapping in the Damrei Romel area. Cambodia using frequency ratio and logistic regression models. Environmental Geology, 50: 847-855.
23. Mathew, J., V.K. Jha and G.S. Rawat. 2007. Weights of evidence modeling for landslide hazard zonation mapping in part of Bhagirathi valley, Uttarakhand. Current Science, 92(5): 628-638.
24. Nefeslioglu, H.A., T.Y. Duman and S. Durmaz. 2008. Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Easten Black Sea Region of Turkey). Geomorphology, 94: 401-418.
25. Ohlamcher, G.C. and J.C. Davis. 2003. Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Engineering Geology, 69: 331-343.
26. Ownegh, M. 2002. Landslide hazard and risk assessment in the southern Sunbirds of Newcastle. Sabbatical research. University of Newcastle, Australia, 2: 85.
27. Pourghasemi, H.R., S.M. Fatemi aghda and M. Mohammadi. 2007. Introduction of fuzzy methods and its application in Risk zonation of landslide. Collection of articles in natural resources and sustainable development in southern of Caspian Sea, 800-850 (in Persian).
28. Shafer, G. 1990. Perspectives on the theory and practice of belief functions. International Journal of Approximate Reasoning, 3: 1-40.
29. Shahabi, M.A. and S. Sadodin. 2009. Beysian decision network for forecasting of effects of managemental works on wheat lands in Golestan Province. Ghorgan, Iran (in Persian).
30. Shafer, G. 1976. A mathematical theory of evidence. Princeton University Press, 254 pages.
31. Shirani, K., A. Safe and A. Nasr. 2013. Analysis of effective factors of massive movement basis on risk zonation of landslide. Earth Science Journal, 89: 3-10 (in Persian).
32. Smets, P.H. and R. Kennes. 1994. The transferable belief model. Artificial Intelligence, 66: 191–243.
33. Smets, P.H. 2004. Analyzing the combination of conflicting belief functions. Artificial Intelligence, 170(2006): 909–924.
34. Smets P.H. 1990. The combination of evidence in the transferable Belief Model. Artificial Intelligence, 170(11): 909–924.
35. Suzen, M.L. and V. Doyuran. 2004. Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu Catchment, Turkey. Engineering Geology, 71: 303–352.
36. Swets, J.A. 1988. Measuring the accuracy of diagnostic systems. Science, 240: 1285-1293.
37. Schocken, S. and R.A. Hummel. 1993. On the use of the Dempster–Shafer model in information indexing and retrieval applications. International Journal of Man–Machine Studies, 39(5): 843–879.
38. Yalcin, A. 2008. GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations. CATENA, 72: 1-12.
39. Yager, R.R. 2001. Dempster–Shafer belief structures with interval valued focal weights. International Journal of Intelligent Systems, 16: 497–512.
40. Yilmaz, C., T. Topal and M.L. Suzen. 2012 .GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey). Environmental Earth Sciences, 65: 2161–2178.
41. Zhu, C. and X. Wang. 2009. Landslide susceptibility mapping: a comparison of information and weights-of evidence methods in Three Gorges Area. International Conference on Environmental Science and Information Application Technology, DOI 10.1109/ESIAT.2009. 187: 342-346.