آنالیز عددی عوامل مؤثر در رخداد زمین‌لغزش و پهنه‌بندی حساسیت آن با روش‌های رگرسیون لجستیک ‏و رگرسیون چندمتغیره خطی (مطالعه موردی: حوضه ماربر)‏‏

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری ژئومورفولوژی دانشگاه تربیت مدرس، تهران، ایران.‏

2 استادیار مرکز تحقیقات کشاورزی و منابع طبیعی استان اصفهان، اصفهان، ایران.‏

3 استادیار دانشکده منابع طبیعی، دانشگاه اردکان، یزد، ایران.‏

چکیده

هدف از این پژوهش شناسایی عوامل مؤثر در رخداد زمین‌لغزش و پهنه‌بندی حساسیت آن با استفاده از روش‌های ‏رگرسیون لجستیک و رگرسیون چند متغیره خطی است. بدین منظور در ابتدا با استفاده از تفسیر عکس‌های هوایی ‏با مقیاس 1:40000، نقشه‌های توپوگرافی، زمین‌شناسی و عملیات میدانی با استفاده از GPS‎، نقشه پراکنش زمین‌لغزش‌ها به‌صورت سطح به‌عنوان متغیر وابسته تهیه گردید. برای تعیین عوامل مؤثر در رخداد زمین‌لغزش از آنالیز ‏مقادیر عددی پارامترها با روش ماشین‌های بردار پشتیبان در محیط نرم‌افزار ‏Rapid Miner‏ استفاده گردید و از ۲۱ لایه ‏اطلاعاتی انتخابی، ۱۵ لایه اطلاعاتی انتخاب و جهت تهیه نقشه پهنه‌بندی به‌عنوان متغیر مستقل در محیط‎ ArcGIS ‎‎10.1‎‏ تهیه و رقومی گردیدند. پس از وزن دهی به لایه‌ها، نقشه پهنه‌بندی با استفاده از روش‌های انتخابی در ۵ کلاس ‏خیلی کم، کم، متوسط، زیاد و خیلی زیاد تهیه گردید. نتایج وزن دهی لایه‌ها نشان داد که در هر دو روش، کاربری اراضی ‏و جهت شیب بیشترین تأثیر را در وقوع زمین‌لغزش داشته‌اند. منحنی ‏ROC‏ و مساحت زیر منحنی ‏‎(AUC)‎‏ برای نقشه‌های پهنه‌بندی ترسیم و از ‏AUC‏ برای صحت سنجی استفاده گردید و مقادیر حاصل از آن نشان داد که مدل چند ‏متغیره خطی (‏‎ ‎‏۸۹۰/۰) دارای کارایی بالاتری نسبت به مدل لجستیک (۸۲۹/۰) جهت پهنه‌بندی خطر زمین‌لغزش است. بر اساس نتایج مدل برتر (چند متغیره خطی)، ‏۱/۱۶۰۴۶‏ هکتار (۱۳/۲۰ درصد) از منطقه در رده خطر زیاد ‏و ‏۲/۱۵۶۷۱‏ هکتار (۶۶/۱۹ درصد) از منطقه در رده خطر خیلی زیاد قرار گرفته است.

کلیدواژه‌ها


 [1] Arabameri, A.R., Klorajan, A., Karami, J., Alimoradi, M. and Shirani, K. (2014). Zonation of Landslide Hazard Using Artificial Neural Network the Case Study: Marbor Basin, Geodynamics Research International Bulletin, 03, 44-59.
[2] Arabameri, A.R. and Halabian, A.H. (2015). Landslide Hazard Zonation Using Statistical Model of AHP ‎‎ (Case ‎Study: Zarand Saveh Basin), Physical Geomorphology, 28, 65-86.‎
[3] 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.‎
[4] Ayalew, L., Yamagishi, H., Marui, H. and Kanno, T. (2005). Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications, Eng. Geology, 81, 432–445.
[5] Begueria, S. and Lorente, A. (1999). Landslide Hazard Mapping by Multivariate Statistics: ‎Comparison of Methodes and Case Study in the Spanish Pyrenees.‎
[6] Bui, D.T., Pradhan, B., Lofman, O., Revhaug, I. and Dick, O. B. (2012). Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS, Computers and Geosciences doi:10.1016/j.cageo.2011.10.031.
[7] Blöchl, A. and Braun, B. (2005). Economic assessment of landslide risks in the Swabian Alb, Germany –research framework and first results of homeowners and rxperts surveys, Natural Hazards and Earth System Sciences, 5, 389-396.
[8] Can, T., Nefeslioglu, H.A., Gokceoglu, C., Sonmez, H. and Duman, Y. (2005). Susceptibility assessments of shallow earth flows triggered by heavy rainfall at three catchment's by logistic regression analysis, Geomorphology, 82, 250-271.
[9] Constantin, M., Bednarik, M., Jurchescu, M.C. and Vlaicu, M. (2010). Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania), Environmental Earth Science, DOI 10.1007/s12665-010-0724-y.
[10] Chang, K.T., Chiang, S.H. and Hsu, M.L. (2007). Modeling phoon- and earthquake-induced landslides in a mountainous watershed using logistic regression, Geomorphology, 89, 335-347.
[11] Chau, K.T. and Chan, J.E.‎ (2005). Regional bias of landslide data in generating susceptibility maps ‎using logistic regression for Hong Kong Island, Landslides, 13, 280-290.‎
[12] Dong, J.J., Tung, Y.H., Chen, C.C., Liao, J.J. and Pan, Y.W. (2010). Logistic regression model ‎for predicting the failure probability of a landslide dam, Engineering Geology, 117, 52-61.‎
[13] Duman, T. Y., Can, T., Gokceoglu, C., Nefeslioglu, H. A. and Sonmez, H. (2006). Application of Logistic Regression for Landslide Susceptibility Zoning of Cekmece Area Istanbul Turkey, Environmental Geology, 51, 241-256.
[14] Ercanoglu, M. and Candan, G.P. (2004). Use of fuzzy relation to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey), Engineering Geology, 75, 229-250.
[15] Evans I.S. (1972). General geomorphometry, derivatives of altitude and descriptive statistics, In R. J. Chorley (Ed), Spatial Analysis in Geomorphology, 17-90.
[16] Fall, M., Azzam, R. and Noubactep, C. (2006). A multi-method to approach to study the   stabilityof nature slpoes and landslide susceptibility mapping, Engineering Geology, 82, 241-263.
[17] Fatemi Aghda, M., Ghiomian, J. and Eshgheli Farahani, A. (2004). Evaluation efficiency statistics methods in determined Landslide hazard potential, Geosciences, 11, 28-47.
[18] Garsia–Rodriguez, M.J., Malpica, J.A., Benito, B. and Diaz, M. (2008). Susceptibility assessment of earthquake–triggered landslides in El Salvador using logistic regression, Geomorphology, 95, 172-191.
[19] Guzzetti, F., Cardinali, M., Relchenbach, P. and Carrara, A. (2000). Comparing Landslide Maps, (Case Study in the Upper Tiber River Basin, Central Italy), Journal of Environmental Management, 3, 247-263.
[20] Hasanzade Nafati, M. (2000). Landslide hazard zonation in Shalmanrud watershed area. Watershed Management Engineering, M.Sc. Thesis Natural Resources Faculty, Tehran University. 125 pp.
[21] Hosseinzadeh, M., Servati, M. R. and Mansouri, A. (2009). Zonation of Mass Movements Occurring Risk using Logistic Regression Model, IRAN Geology Quarterly, 3 (11), 27-37.
[22] Ilinca, V. and Gheuca, I. (2011). The red lake landslide (Ucigau Mountain, Romania), ‎Carpathian Jour. Earth Environ, 23, 263-272.‎
[23] Kayastha, P., Dhital, M.R. and Smedt, F.D. (2012). Landslide susceptibility mapping using the ‎weight of evidence method in the Tinau watershed (Case Study: Nepal), Nat Hazards, 63,479-498.‎
[24] Komakpanah, A. and Hafezi Moghadas, S. (1995). Method of landslide hazard zonation. Proceedings of the first workshop examined strategies to reduce landslide losses in the country.
[25] Komac, M. (2006). A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia, Geomorphology, 74, 17-28.
[26] Lee, S. and Sambath, T. (2006). Landslide susceptibility mapping in the Damreiromal ‎area, Cambodia using frequency ratio and logistic regression models, Journal of Environmental Geology, 50, 847-855‎
[27] Nefeslioglu, H.A., Gokceoglu, C. and Sonmez, H. (2008). An assessment on the use of ‎logistic regression and artificial neural networks with different sampling strategies for the ‎preparation of landslide susceptibility maps, Engineering Geology, 97, 171-191. ‎
[28] Ohlmacher, G. C. and Davis, J. C. (2003). Using Multiple Logistic Regression and GIS Technology to Predict Landslide Hazard in Northeast Kansas USA, Engineering Geology, 69, 331-343.
[29] Ohlmacher, G., Davis, C. and Jhon, C. (2003). Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Engineering Geology, 69, 331-343.
[30] Pradhan, B. and Lee, S. (2010). Landslide susceptibility assessment and factor effect analysis: back propagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modeling, Environmental Modelling & Software, 25, 747-759.
[31] Pike, R.J. (2000). Geomorphology - Diversity in quantitative surface analysis, Progress in Physical Geography, 24, 1-20.
[32] Regmi, N.R., Giardino, J.R.‎ and Vitek, J.D. (2010). Modeling susceptibility to landslides using ‎the weight of evidence approach (Case Study: Western Colorado, USA), ‎Geomorphology, 115, 172–‎‎187.‎ 
[33] Roostaei, S.H. and Ahmadzadeh, H. (2005). GIS-based Zonation of Environmental Hazards Influence upon Linear Structures (Case Study: Tabriz-Mianeh Area). International Conference of Geo Hazard and Natural Disasters, University of Tabriz, Tabriz, Iran.
[34] Safari, A., Alimoradi, M. and Hatamifard, R. (2013). Landslide Hazard Zonation Using Multivariate Regression Method, Quantities Geomorphology Researches, 3, 59-74. 
[35] Sefidgari, R. (2003). Evaluation Landslide Hazard Zonation Methods at a Scale of 1:50000, ‎Case Study: Damavand Drainage Basin, M.Sc. Thesis, University of Tehran.‎
[36] Shirani, K. (2003). Evaluation of the most important of zonation hazard landslide methods for selection appropriate method in south of Isfahan Province (Case Study: Semirom Region across Marbor River). Final report of research plan. Record No. 83/961.
[37] Shirani, M. (2004). Evaluation of landslide hazard zonation methods against suitable model selection for Semirom. Final report of investigative scheme. Research center of Watershed Management and Soil Conservation. Record No. 961, Press, 95p.
[38] Shirani, K., Hajihashemijazi, M.R., Niknezhad, S.A. and Rakhsha, S. (2012). Landslide Risk Zoning Potential by Analytical Hierarchy Process (AHP) and Multivariate Regression (MR) (Case Study: Upstream of North Karoon Basin), Journal of Range and Watershed Management, 65 (3), 595-409.
[39] Shirani, K. and Arabameri, A.R. (2015). Landslide Hazard Zonation Using Logistic Regression ‎Method ‎‎(Case ‎Study:Dez-e-Oulia Basin), J. Sci. & Technol. Agric. & Natur. Resour., Water ‎and Soil Sci., Isf. ‎Univ. ‎Technol., Isf., Iran 72, 321-334.‎
[40] Soori, S. (2010). Evaluation of landslide hazard zonation in Keshvari basin. The 5th international Symposium of Geology and Environment, Tehran: 171-176.
[41] Song, R.H., Hirim, U.D., Kazutoki, A., Usio, K. and Sumio, M. (2008). Modeling the potential distribution of shallow-seated landslides using the weights of evidence method and a logistic regression model (case study: the Saba Area), International Journal of Sediment Research, 23, 106-118.
[42] Shary. P., Sharaya. L. and Mitusov. A. (2002). Fundamental quantitative methods of land surface analysis, Geoderma, 107, 1-32.
[43] Suzen, M.L. and Doyuran, V. (2004). Data driven bivariate landslide susceptibility assessment using ‎geographical information systems: a method and application to Asarsuyu catchment, ‎Turkey. Eng Geol, 71, 303–352.‎
[44] Swets, J.A. (1988). Measuring the accuracy of diagnostic systems. Science. 240, 1285-1293.‎
[45] Van Den Eeckhout, M., Vanwalleghem, T., Poesen, T., Govers, G., Verstraeten, G. and Vandekerckhove, L. (2006). Prediction of landslide susceptibility using rare events logistic regression (case study: Flemish Ardennes). Geomorphology, 76, 392-410.
[46] Van Westen, C.J., Rengers, N.‎, Terline, ‎ M.T.J. and Soeters, R.‎ (1997). Predication of the ‎Occurrence of Slope Instability Phenomena through GIS-Based Hazard Zonation, ‎Geologisches Rundschau, 86, 404-414.‎
[47] Van Western, C.J., Van Asch, T.H. and Soeters, R. (2005). Landslide Hazard and Risk Zonation: Why is it Still so Difficult, Bulletin of Engineering Geology and theEnvironment, 2, 176-184.
[48] Wood, J. D. (1996). The Geomorphologic Characterization of digital elevation models. Ph.D. Dissertation, University of Leicester, UK.
[49] Yilmaz, I. (2009). Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison (case study: Kat landslides), Computer and Geosciences, 35, 1125-1138.