مدل‌سازی فضایی زمین‌لغزش: ارزیابی کارایی ترکیب روش داده محور EBF و روش دانش محور AHP (مطالعه موردی: حوضه فریدون شهر)

1.Arabameri, A.R., Shirani, K., and Rezaei, K.H. 2017a. Landslide land capability zonation using Dempster-shafer and frequency ratio models. J. Water Soil Cons. 24: 3. 41-57.

2.Arabameri, A.R., Shirani, K., and Rezaei, K.H. 2017b. A Comparative Assessment between Weights-of-Evidence and Frequency Ratio Models for Landslide Hazard Zonation in Vanak Basin. J. Water. Manage. Res. 8: 15. 147-160.

3.Arabameri, A.R., Shirani, K., and Tazeh, M. 2017c. Assessment of logistic and multivariate regression Models for Landslide hazard zonation (Case study: Marbor basin). Range and watershed management. 70: 1. 151-168.

4.Arabameri, A.R., and Shirani, K. 2016. Identification of Effective Factors on Landslide Occurrence and its Hazard Zonation Using Dempster-Shafer theory (Case study: Vanak Basin, Isfahan Province). Watershed Engineering and Management. 8: 1. 93-106.

5.Arabameri, A.R., Shirani, K., and Halabian, A.H. 2016. Evaluation of prediction capability of the Statistical and Logestic models for mapping landslide susceptibility (Case study: Vanakbasin). Physical Geomorphology. 9: 32. 123-140.

6.Arabameri, A.R., Halabian, A.H. 2015. Landslide Hazard Zonation Using Statistical Model of AHP (Case
study: Zarand Saveh Basin). Physical Geomorphology. 8: 28. 65-86.

7.Achour, Y., Boumezbeur, A., Hadji, R., Chouabbi, A., Cavaleiro, V., Bendaoud, E.A. 2017. Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine. Algeria, Arab. J. Geosci. 10: 194.

8.Cui, K., Lu, D., and Li, W. 2017. Comparison of landslide susceptibility mapping based on statistical index, certainty factors, weights of evidence and evidential belief function models. Geocarto International. 32: 9. 935-955.

9.Chen, W., Xie, X., Wang, J., Pradhan, B., Hong, H., Bui, D.T., Duan, Z., and Ma, J. 2017a. A comparative study of logistic model tree, random forest and classification and regression tree models for spatial prediction of landslide susceptibility. CATENA. 151: 147-160.

10.Chen, W., Xie, X., Peng, J., Wang, J., Duan, Z., and Hong, H. 2017b. GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naıve-Bayes tree, and alternating decision tree models. Geomatics, natural hazards and risks. 8: 2. 950-973.

11.Chen, W., Chai, H., Sun, X., Wang, Q., Ding, X., and Hong, H. 2016a. A GIS-based comparative study of frequency ratio, statistical index and weights-of-evidence models in landslide susceptibility mapping. Arab. J. Geosci. 9: 1-16.

12.Chen, W., Ding, X., Zhao, R., and Shi, S. 2016b. Application of frequency ratio and weights of evidence models in landslide susceptibility mapping for the Shangzhou District of Shangluo City, China. Environ. Earth Sci. 75: 1-10.

13.Chen, W., Li, W., Chai, H., Hou, E., Li, X., and Ding, X. 2016c. GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China. Environ. Earth Sci. 75: 1-14.

14.Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., and Boehner, J. 2015. System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geosci. Model Dev. 8, 1991-2007, doi:10.5194/gmd-8-1991-2015.

15.Ding, Q., Chen, W., and Hong, H. 2016. Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping. Geocarto Int. 6: 32. 619-639.

16.Ercanoglu, M., and Gokceoglu, C. 2002. Assessment of landslide susceptibility for a landslide prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ Geol. 41: 6. 720-730.

 17.Environment for Visualizing Images (ENVI). 1977. www.harrisgeospatial.com.18.Expert Choice. 1983. www.expertchoice.com.

19.Guo-liang, D., Yong-shuang, Z., Javed, I., Zhi-hua, Y., and Xin, Y. 2017. Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu province, China. J. Mt. Sci. 14: 2. 249-268.

20.Ghorbani Nejad, S., Falah, F., Daneshfar, M., Haghizadeh, A., and Rahmati, O. 2017. Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models. Geocarto International. 32: 2. 167-187.

21.Gorum, T., Fan, X., van Westen, C.J., Huang, R.Q., Xu, Q., Tang, C., and Wang, G. 2011. Distribution pattern of earthquake-induced landslides triggered by the 12 May 2008 Wenchuan earthquake. Geomorphology. 133: 152-167.

22.Hengl, T., Gruber, S., and Shrestha, D.P. 2003. Digital terrain analysis in ILWIS. International Institute for Geo-Information Science and Earth Oservation Enschede. The Netherlands, 62p.

23.Hong, H., Chen, W., Xu, C., Youssef, A.M., Pradhan, B., and Tien Bui,
D. 2017. Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor and index of entropy. Geocarto Int. 32: 139-154.

24.Jenness, J. 2002. Surface Areas and Ratios from Elevation Grid. Jenness Enterprises.

25.Lee, S., and Pradhan, B. 2007. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides. 4: 1. 33-41.

26.Mallick, J., Al-Wadi, H., and Atiqur Rahman, M. 2014. Landscape dynamic characteristics using satellite data from a mountainous watershed of Abha, Kingdom of Saudi Arabia. Environ Earth Sci. 72: 12. 4973-4984.

27.Mahmoudi, F. 2001. Dynamic geomorphology, fourth edition, Tehran University Press.

28.Mahalingam, R., Olsen, M.J., and O’Banion, M.S. 2016. Evaluation of landslide susceptibility mapping techniques using lidarderived conditioning factors (Oregon case study). Geomat Nat Haz Risk. 7: 1884-1907.

29.Malamud, B.D., Turcotte, D.L., Guzzetti, F., and Reichenbach, P. 2004. Landslide inventories and their statistical properties. Earth Surf Proc Landforms. 29: 687-711.

30.Myronidis, D., Papageorgiou, C., and Theophanous, S. 2016. Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP). Nat Hazards. 81: 245-263.

31.Moore, I.D., Grayson, R.B., and Ladson, A.R. 1991. Digital terrain modeling: a review of hydrological, geomorphological and biological applications. Hydrol Process. 5: 3-30.

32.Moore, I.D., and Burch, G.J. 1986. Sediment transport capacity of sheet and rill flow: application of unit stream power theory. Water Resour. Res. 22: 1350-1360.

33.Organization of forests, rangelands and watershed management of the country, 2010. language/fa-IR/Default.aspx.

34.Nsengiyumva, J.B., Luo, G., Nahayo, L., Huang, X., and Cai, P. 2017. Landslide Susceptibility Assessment Using Spatial Multi-Criteria Evaluation Model in Rwanda. Int. J. Environ. Res. Public Health. 15: 243.

35.Pourghasemi, H.R., and Rossi, M. 2016. Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods. Theor Appl Climatol. 130: 1-2. 609-633.

36.Pourghasemi, H.R., and Kerle, N. 2016. Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environ. Earth Sci. 75: 185.

37.Shahabi, H., Hashim, M., and Ahmad, B.B. 2015. Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression and fuzzy logic methods at the central Zab basin, Iran. Environ Earth Sci. 73: 8647-8668.

38.Shirani, K., and Seif, A. 2013. Landslide Hazard Zonation by Using Statistical Methods (Pishkuh Region in Fereydonshahr province). Geoscience. 22: 85. 149-158.

39.Saaty, T.L., and Vargas, G.L. 2001. Models, Methods, Concepts and Applications of the Analytic ‎Hierarchy Process. Kluwer Academic Publisher, Boston.

‎40.Su¨zen, M.L., and Doyuran, V. 2004. A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environ. Geol. 45: 5. 665-679.

41.Tahmassebipoor, N., Rahmati, O., Noormohamadi, F., and Lee, S. 2016. Spatial analysis of groundwater potential using weights of evidence and evidential belief function models and remote sensing. Arab. J. Geosci. 9: 1-18.

42.Vakhshoori, V., and Zare, M. 2016. Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic and frequency ratio methods. Geomat. Nat. Haz. Risk. 7: 1731-1752.

43.Wang, Q., Wang, D., Huang, Y., Wang, Z., Zhang, L., Guo, Q., Chen, W., Chen, W., and Sang, M. 2015. Landslide Susceptibility Mapping Based on Selected Optimal Combination of Landslide Predisposing Factors in
a Large Catchment. Sustainability. 7: 16653-16669.

44.Xie, Z., Chen, G., Meng, X., Zhang, Y., Qiao, L., and Tan, L. 2017. A comparative study of landslide susceptibility mapping using weight of evidence, logistic regression and support vector machine and evaluated by SBAS-InSAR monitoring: Zhouqu to Wudu segment in Bailong River Basin, China. Environ. Earth Sci. 76: 313.

45.Yesilnacar, E.K. 2005. The application of computational intelligence to landslide susceptibility mapping in Turkey, PhD Thesis. Department of Geomatics the University of Melbourne. 423p.

46.Zhuo, C., Liang, S., Ke, Y., Yang, Z., and Zhao, H. 2017. Landslide susceptibility assessment using evidential belief function, certainty factor and frequency ratio model at Baxie River basin, NW China, Geocarto international. Pp: 1-20.

47.Zhao, H., Yao, L., Mei, G., Liu, T., and Ning, Y. 2017. A Fuzzy Comprehensive Evaluation Method Based on AHP and Entropy for a Landslide Susceptibility Map, Entropy. 19: 396.

48.Zhang, K., Wu, X., Niu, R., Yang, K., and Zhao, L. 2017. The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area, China. Environ. Earth Sci. 76: 405.