تهیه نقشه حساسیت زمین‌لغزش با استفاده از مدل‌های نسبت فراوانی، رگرسیون لجستیک و فرآیند تحلیل سلسله مراتبی در بخشی از استان گلستان
محورهای موضوعی :
مدیریت بلایای طبیعی
مجید محمدی
1
,
شنطیا جمال
2
,
آرمان منصوری
3
1 - استادیار دانشکده منابع طبیعی دانشگاه سمنان، سمنان، ایران. * (مسوول مکاتبات)
2 - دانش‌آموختگان دانشکده منابع طبیعی دانشگاه سمنان، سمنان، ایران
3 - دانش‌آموختگان دانشکده منابع طبیعی دانشگاه سمنان، سمنان، ایران
تاریخ دریافت : 1395/03/31
تاریخ پذیرش : 1395/10/08
تاریخ انتشار : 1400/09/01
کلید واژه:
رگرسیون لجستیک,
زمین‌لغزش,
نسبت فراوانی,
تحلیل سلسله مراتبی,
چکیده مقاله :
زمینه و هدف: ایران به ویژه در مناطق شمالی به خاطر شرایط اقلیمی و توپوگرافی، همواره در معرض خطر زمین‌لغزش است. شناخت نواحی مستعد وقوع زمین لغزش و خطرات ناشی از آن یکی از اقدامات اولیه در مدیریت منابع طبیعی و برنامه ریزی های توسعه ای و عمرانی است. بررسی زمین لغزش جهت تهیه نقشه های حساسیت و شناسایی مناطق مستعد زمین لغزش و همچنین شناسایی مکان‌های امن برای توسعه سکونت‌گاه های جدید در آینده مورد توجه برنامه ریزان قرار دارد. هدف اصلی این تحقیق تهیه نقشه حساسیت‌پذیری زمین‌لغزش در بخشی از استان گلستان است.روش بررسی: 78 لغزش در بخشی از استان گلستان شناسایی و نقشه پراکنش زمین لغزش ها در سال 1395 تهیه گردید. نقشه‌های عوامل موثر شامل درجه شیب، جهت شیب، شکل شیب، طبقه ارتفاعی، سنگ شناسی، کاربری اراضی، فاصله از جاده، فاصله از گسل، فاصله از آبراهه و طول شیب در محیط GIS تهیه شد. سه روش فرآیند تحلیل سلسله مراتبی، نسبت فراوانی و رگرسیون لجستیک به‌منظور تهیه نقشه حساسیت زمین‌لغزش به کار برده شد. همچنین از منحنی ROC برای ارزیابی دقت نقشه‌های حساسیت استفاده شد.یافته‌ها: اولویت بندی فاکتورهای موثر با استفاده از AHP نشان داد فاصله از جاده، شیب، فاصله از آبراهه و فاصله از مناطق مسکونی بیشترین تاثیر را بر وقوع زمین‌لغزش دارند. نقشه‌های حساسیت تهیه شده با استفاده از سه مدل با استفاده از منحنی تشخیص عملکرد نسبی و سطح زیرمنحنی با یکدیگر مقایسه شدند. نتایج نشان داد مدل نسبت فراوانی با سطح زیر منحنی 8/0 بیشترین دقت را در تهیه نقشه حساسیت زمین‌لغزش دارد.بحث و نتیجه‌گیری: به طور کلی نتایج نشان داد منطقه مورد مطالعه پتانسیل زیادی برای وقوع زمین‌لغزش دارد. شناسایی مناطق حساس کمک می‌کند تا حد امکان از تغییرات حالت طبیعی این مناطق جلوگیری نموده و باعث تحریک این مناطق نشویم.
چکیده انگلیسی:
Background and objective: Iran is always exposed to landslide hazard especially in the north because of climatic and topographic conditions. Identification of landslide prone areas and its hazards is one of the first works in natural resources management and development programs. Policymakers pay high attention to landslide investigation in order to landslide susceptibility mapping and identifying susceptible areas and stable locations for development of new settlements in the future. The main goal of his research is landslide susceptibility mapping in the part of Golestan province.Material and Methodology: 78 landslides were identified from the field surveys in the part of Golestan province, and then landslide inventory map was created in year 2016. Effective factor maps such as slope degree, slope aspect, plan curvature, altitude, lithology, land use, distance from road, distance from fault, distance from drainage and slope-length (LS), were prepared in the GIS environment. Three methods such as analytical hierarchy process, frequency ratio and logistic ratio were applied to landslide susceptibility mapping. Also ROC curve was used to accuracy assessment of susceptibility mapsFindings: prioritization of effective factors using AHP showed that distance from road, slope, distance from drainage and distance from residential area have the most effect on landslide occurrence. Landslide susceptibility map obtained from three models was compared using Relative Operating Characteristic (ROC) and Area under Curve (AUC). The result showed that frequency ratio model with the AUC equal to 0.8 has the most accuracy to landslide susceptibility mapping.Discussion and Conclusion: In general, the results showed that the study area has a high potential for landslides occurrence. Identifying susceptible areas help to prevent changes in the natural state of these areas as much as possible.
منابع و مأخذ:
Glade, T., 1997. Establishing the frequency and magnitude of landslide-triggering rainstorm events in New Zealand, Environmental Geology, Vol. 35, pp. 160–174.
Shadfar, M., Yamani, M., Namaki, M., 2011. Zoning land slide hazard by Information Value Method (IVM), Density Area (DA) and Landslide Numerical Risk Factor (LNRF) model in Chalkrood. J. Watershed Manage. Eng: 3(1): 40-47. (In Persian)
Barredo, J.I., Benavidesz, A., Herh, J., Van Westen, C.J., 2000. Comparing heuristic landslide hazard assessment techniques using GIS in the Tirajana basin, Gran Canaria Island, Spain, International Journal Applied Earth Observation, Vol. 2, pp. 9–23.
Ayalew, L., Yamagishi, H., Marui, H., Kanno, T., 2005. Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications. Engineering Geology, Vol. 81, pp. 432–445.
Yalcin, A., 2008. GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations, Catena, Vol. 72, pp. 1–12.
Hasekiogullari, G.D., Ercanoglu, M., 2012. A new approach to use AHP in landslide susceptibility mapping: a case study at Yenice (Karabuk, NW Turkey). Natural Hazards, Vol. 63(2), pp. 1157–1179.
7. Demir, G., Aytekin, M., Akgun, A., Ikizler, S.B., Tatar, O., 2013. A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods, Natural Hazards, Vol. 65(3), pp. 1481-1506.
Ohlmacher, G.C. Davis, J.C., 2003. Using multiple logistic regression and GIS technology to predict landslide hazard in Northeast Kansas, USA, Engineering Geology, 69, pp. 331-343.
Ayalew, L., Yamagishi, H., 2005. The Application of GIS – based logistic regression for landslide susceptibility mapping in the Kakuda–Yahiko Mountains, central Japan, Geomorphology, Vol. 65, pp. 15-31.
Hosseinzadeh M.M., Sarvati M.R., Mansori A., Mirbaghari B., Khazri S. 2009. Zoning the risk of mass movement occurrences using logistic regression model, case study in vicinity of Sanandaj-Dehgolan road. Iranian Journal of geology, 3(11): 27-37. (In Persian)
Bai, S.B., Wang, J., Lü, G., Zhou, P., Hou, S.S., Xu, S.N., 2010. GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China, Geomorphology, 115, pp. 23–31.
Kavzoglu, T., Sahin, E.K., Colkesen, I., 2013. Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides. doi:10.1007/s10346-013-0391-7.
Mohammady, M., Pourghasemi, H.R., Pradhan, B., 2012. Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster–Shafer, and weights of evidence models. Journal Asian Earth Science, 61, pp. 221–236.
Jaafari, A., Najafi, A., Pourghasemi, H.R., Rezaeian, J., Sattarian, A., 2014. GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran, International Journal of Environmental Science and Technology, DOI: 10.1007/s13762-013-0464-0
Ghodsipour, SH., 2005. Analytical Hierarchy process (AHP), Amirkabir University of technology publication. 222 p. (In Persian)
Kincal, C., Akgun, A., Yalcın Koca, M., 2009. Landslide susceptibility assessment in the Izmir (West Anatolia, Turkey) city center and its near vicinity by the logistic regression Method, Environmental Earth Science, 59, pp. 745–756
Pradhan, B., Lee, S., 2010. Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environmental Earth Sciences, 60, pp. 1037–1054.
O’Brien, R.M., 2007. A caution regarding rules of thumb for variance inflation factors, Quality and Quantity, Vol. 41(5), 673–690
Pradhan, B., Buchroithner, M.F., 2010. Comparison and validation of landslide susceptibility maps using an artificial neural network model for three test areas in Malaysia. Environment Engineering and Geoscience, 16(2), pp. 107–126
Pourghasemi, H.R., Gokceoglu, C., Pradhan, B., Deylami Moezzi, K., 2012. Landslide susceptibility mapping using a spatial multi criteria evaluation model at Haraz Watershed, Iran. B. Pradhan and M. Buchroithner (eds.), Terrigenous Mass Movements, 23-49. Springer-Verlag Berlin Heidelberg.
Yesilnacar, E.K., 2005. The Application of Computational Intelligence to Landslide Susceptibility Mapping in Turkey, Ph.D Thesis. Department of Geomatics the University of Melbourne, pp. 423
Feyznia, S., Ahmadi, H., Hassanzadeh Nafuti, M. 2001. Landslide hazard zonation in Shalmanrood basin in Gilan Province. Iranian Journal of natural resources, 54(3): 207-219. (In Persian)
Fatemi Aghda, S.M., Ghayoumian, J., Ashghali Faraahani, A. 2003. Evaluation of statistical methods in landslide hazard analysis. 11(47-48): 28- 47. (In Persian)
Chau, K.T., Chan, J.E., 2005. Regional bias of landslide data in generating susceptibility maps using logistic regression: Case of Hong Kong Island, Landslides, 2, pp. 280-290
Devkota, K.C., Regmi, A.D., Pourghasemi, H.R., Yoshida, K., Pradhan, B., Ryu, I.C., Dhital, M.R., Althuwaynee, O.F., 2013. Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya, Natural Hazards, Vol. 65, pp. 135–165
Dai, F.C., Lee, C.F., 2002. Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong, Geomorphology, 42 (3/4): 213-228
Fatemi Aghda, S.M., Ghayoumian, J., Teshnehlab, M., Ashghali Faraahani, A. 2005. Landslide hazard zonation using fuzzy logic, case study Rudbar area, Journal of sciences. 31(1): 43-64. (In Persian)
Oh, H.J., Pradhan, B., 2011. Application of a neuro-fuzzy model to landslide susceptibility mapping for shallow landslides in a tropical hilly area. Computer and Geoscience, 37(9), pp. 1264–1276
Pourtaghi, Z.S., Pourghasemi, H.R., GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province, Iran, Hydrogeology, DOI: 10.1007/s10040-013-1089-6.
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Glade, T., 1997. Establishing the frequency and magnitude of landslide-triggering rainstorm events in New Zealand, Environmental Geology, Vol. 35, pp. 160–174.
Shadfar, M., Yamani, M., Namaki, M., 2011. Zoning land slide hazard by Information Value Method (IVM), Density Area (DA) and Landslide Numerical Risk Factor (LNRF) model in Chalkrood. J. Watershed Manage. Eng: 3(1): 40-47. (In Persian)
Barredo, J.I., Benavidesz, A., Herh, J., Van Westen, C.J., 2000. Comparing heuristic landslide hazard assessment techniques using GIS in the Tirajana basin, Gran Canaria Island, Spain, International Journal Applied Earth Observation, Vol. 2, pp. 9–23.
Ayalew, L., Yamagishi, H., Marui, H., Kanno, T., 2005. Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications. Engineering Geology, Vol. 81, pp. 432–445.
Yalcin, A., 2008. GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations, Catena, Vol. 72, pp. 1–12.
Hasekiogullari, G.D., Ercanoglu, M., 2012. A new approach to use AHP in landslide susceptibility mapping: a case study at Yenice (Karabuk, NW Turkey). Natural Hazards, Vol. 63(2), pp. 1157–1179.
7. Demir, G., Aytekin, M., Akgun, A., Ikizler, S.B., Tatar, O., 2013. A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods, Natural Hazards, Vol. 65(3), pp. 1481-1506.
Ohlmacher, G.C. Davis, J.C., 2003. Using multiple logistic regression and GIS technology to predict landslide hazard in Northeast Kansas, USA, Engineering Geology, 69, pp. 331-343.
Ayalew, L., Yamagishi, H., 2005. The Application of GIS – based logistic regression for landslide susceptibility mapping in the Kakuda–Yahiko Mountains, central Japan, Geomorphology, Vol. 65, pp. 15-31.
Hosseinzadeh M.M., Sarvati M.R., Mansori A., Mirbaghari B., Khazri S. 2009. Zoning the risk of mass movement occurrences using logistic regression model, case study in vicinity of Sanandaj-Dehgolan road. Iranian Journal of geology, 3(11): 27-37. (In Persian)
Bai, S.B., Wang, J., Lü, G., Zhou, P., Hou, S.S., Xu, S.N., 2010. GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China, Geomorphology, 115, pp. 23–31.
Kavzoglu, T., Sahin, E.K., Colkesen, I., 2013. Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides. doi:10.1007/s10346-013-0391-7.
Mohammady, M., Pourghasemi, H.R., Pradhan, B., 2012. Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster–Shafer, and weights of evidence models. Journal Asian Earth Science, 61, pp. 221–236.
Jaafari, A., Najafi, A., Pourghasemi, H.R., Rezaeian, J., Sattarian, A., 2014. GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran, International Journal of Environmental Science and Technology, DOI: 10.1007/s13762-013-0464-0
Ghodsipour, SH., 2005. Analytical Hierarchy process (AHP), Amirkabir University of technology publication. 222 p. (In Persian)
Kincal, C., Akgun, A., Yalcın Koca, M., 2009. Landslide susceptibility assessment in the Izmir (West Anatolia, Turkey) city center and its near vicinity by the logistic regression Method, Environmental Earth Science, 59, pp. 745–756
Pradhan, B., Lee, S., 2010. Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environmental Earth Sciences, 60, pp. 1037–1054.
O’Brien, R.M., 2007. A caution regarding rules of thumb for variance inflation factors, Quality and Quantity, Vol. 41(5), 673–690
Pradhan, B., Buchroithner, M.F., 2010. Comparison and validation of landslide susceptibility maps using an artificial neural network model for three test areas in Malaysia. Environment Engineering and Geoscience, 16(2), pp. 107–126
Pourghasemi, H.R., Gokceoglu, C., Pradhan, B., Deylami Moezzi, K., 2012. Landslide susceptibility mapping using a spatial multi criteria evaluation model at Haraz Watershed, Iran. B. Pradhan and M. Buchroithner (eds.), Terrigenous Mass Movements, 23-49. Springer-Verlag Berlin Heidelberg.
Yesilnacar, E.K., 2005. The Application of Computational Intelligence to Landslide Susceptibility Mapping in Turkey, Ph.D Thesis. Department of Geomatics the University of Melbourne, pp. 423
Feyznia, S., Ahmadi, H., Hassanzadeh Nafuti, M. 2001. Landslide hazard zonation in Shalmanrood basin in Gilan Province. Iranian Journal of natural resources, 54(3): 207-219. (In Persian)
Fatemi Aghda, S.M., Ghayoumian, J., Ashghali Faraahani, A. 2003. Evaluation of statistical methods in landslide hazard analysis. 11(47-48): 28- 47. (In Persian)
Chau, K.T., Chan, J.E., 2005. Regional bias of landslide data in generating susceptibility maps using logistic regression: Case of Hong Kong Island, Landslides, 2, pp. 280-290
Devkota, K.C., Regmi, A.D., Pourghasemi, H.R., Yoshida, K., Pradhan, B., Ryu, I.C., Dhital, M.R., Althuwaynee, O.F., 2013. Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya, Natural Hazards, Vol. 65, pp. 135–165
Dai, F.C., Lee, C.F., 2002. Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong, Geomorphology, 42 (3/4): 213-228
Fatemi Aghda, S.M., Ghayoumian, J., Teshnehlab, M., Ashghali Faraahani, A. 2005. Landslide hazard zonation using fuzzy logic, case study Rudbar area, Journal of sciences. 31(1): 43-64. (In Persian)
Oh, H.J., Pradhan, B., 2011. Application of a neuro-fuzzy model to landslide susceptibility mapping for shallow landslides in a tropical hilly area. Computer and Geoscience, 37(9), pp. 1264–1276
Pourtaghi, Z.S., Pourghasemi, H.R., GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province, Iran, Hydrogeology, DOI: 10.1007/s10040-013-1089-6.
