مقایسه ی روشهای انتخابی تجربی و روشهای آماری و شبکه ی عصبی مصنوعی برای پهنه بندی خطر زمین لغزش(مطالعه ی موردی در مخزن سد بهشت آباد)

نوع مقاله : مقاله پژوهشی

نویسندگان

دانشکده علوم، دانشگاه اصفهان، اصفهان، ایران

چکیده

به منظور کاهش خسارتهای ناشی از زمین‌لغزش، شناسایی پهنه‌های دارای پتانسیل خطر زمین‌لغزش و به نقشه در آوردن آن‌ها امری ضروری و اجتناب‌ناپذیر است. برای انجام این امر، روش‌های متعددی توسط محققین در کشورهای مختلف مورد استفاده قرار‌گرفته است که هر یک از آنها تحت شرایط ویژه‌ای ارائه شده‌اند. در این تحقیق، پس از تهیه‌ی نقشه ‌پراکنش زمین‌لغزش‌های قدیمی و بررسی عوامل مؤثر در زمین‌لغزش‌های محدوده مخزن سد بهشت‌آباد و نیز مقایسه هریک از روش‌های پهنه‌بندی و محل ابداع این روش‌ها با مورد مطالعه، روش‌های تجربی نیلسن و مورا-وارسون به همراه روش آماری دومتغیره و روش شبکه‌ی عصبی مصنوعی برای پهنه‌بندی خطر زمین‌لغزش انتخاب شده‌اند. در ادامه پس از تهیه‌ی نقشه‌های پهنه‌بندی خطر زمین‌لغزش، برای ارزیابی دقت این نقشه‌ها، از روش‌های احتمال تجربی، اندیس لغزش و نیز ترسیم منحنی ROC استفاده شده است. در نهایت نتایج حاصل از ترسیم منحنی ROC و محاسبه سطح زیر این منحنی مبنای ارزیابی دقت روش‌های پهنه‌بندی قرار گرفت که نتیجه آن در این محدوده، انتخاب روش آماری و روش شبکه‌ی عصبی مصنوعی(ANN) به عنوان روش‌هایی مناسب برای تهیه‌ی نقشه‌ی پهنه‌بندی خطر زمین‌لغزش بوده است.

کلیدواژه‌ها


عنوان مقاله [English]

Comparison Between Selected Experimental Methods And Statistical And Artificial Neural Network For Landslide Hazard Zonation Case Study: Behesht Abad Dam Reservoir

نویسندگان [English]

  • Mohsen Gholami
  • Rasoul Ajalloeian
Geology Engineering, Faculty of Science, Isfahan University, Isfahan, Iran
چکیده [English]

In order to decrease the destructions due to landslides, it’s important and unavoidable to recognize and to map the hazard zonations. For this, different mehods are utilized by researchers in other countries with specific conditions. In this paper, landslide inventory map has been prepared and then the effective parameters on the landslides in the study area have been investigated. Finally, some empirical methods such as Mora-Varson and Nilson methods with bivariate Statistical and Artificial Neural Network(ANN) methods are selected by using comparison of various methods between original locations and this study area in Behesht Abad Dam reservoir.
In consequence of landslide hazard zonation mapping by above mentioned methods, some relations including empirical Probability Factor(P), Landslide Index(Li) and Reciever Operating Characteristic(ROC) Curves are used to evaluate the accuracy of each method. Finally, the results of ROC curves and calculation of Area Under ROC Curve(AUC) are based for evaluation of accuracy. Therefore, Artificial Neural Network and Statistical methods are selected to provide suitable maps in this area.

کلیدواژه‌ها [English]

  • Landslide Hazard Zonation
  • Nilson Method
  • Mora-Vahrson Method
  • Statistical Method
  • Artificial Neural Network (ANN)
  • Reciever Operating Characteristic
  • (ROC) Curve
[1] Wu, Y.P; Chen,L; Cheng,C; Yin,K.L; Török ,Á; “GISbased landslide hazard predicting system and its realtime test during a typhoon, Zhejiang Province, Southeast China”, Engineering Geology 175, 9- 21, 2014.
[2] Kayastha, P; Dhital, M.R; Smedt, F.D; “Landslide susceptibility mapping using the weight of evidence method in the Tinau watershed, Nepal”, Natural hazards 63 (2), 479- 498, 2012.
[3] Guzzetti, F; Carrara, A; Cardinali, M; “Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy”,Geomorphology 31 (1- 4), 181- 216, 1999.
[4] Schicker, R; Moon, V; ”Comparison of bivariate and multivariate statistical approaches in landslide susceptibility mapping at a regional scale”,Geomorphology 161- 162, 40- 57, 2012.
[5] Lan, H. X.; Zhau, C. H.; Wang, L. J.; Zhang, H. Y.; Li,R. H.; ”Landslide Hazard Spatial Analysis and Prediction Using GIS in the Xiaojiang Watershed, Yunnan: China”,engineering geology 76, 109- 128, 2004
[6] Kayastha, P; Dhital,M.R; Smedt, F.D; “Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: a case study from the Tinau watershed, west Nepal”, Comput. Geosci. 52, 398- 408,2013.
[7] Atkinson, P.M; Massari, R; “Generalized linear modelling of landslide susceptibility in the central Apennines, Italy”, Comput. Geosci. 24, 373- 385, 1998.
[8] Ercanoglu, M; Gokceoglu, C; “Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey)”, Eng. Geol. 75(3&4), 229- 250, 2004.
[9] Soyoung, P; Chuluong, C; Byungwoo, K; Jinsoo, K; “Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea”,Environ. Earth Sci. 68 (5), 1443- 1464, 2013.
[10] Ermini, L.; Catani, F.; Casagli, N; “Artificial Neural Networks Applied to Landslide Susceptibility Assessment”, geomorphology 66, 327- 343, 2005.
[11] Ercanoglu, M., “Landslide Susceptibility Assessment of SE Bartin(West Black Sea region, Turkey) by Artificial Neural Networks”, Natural Hazards and Earth System Sciences 5, 979- 992. 2005.
[12] Kanungo, D.P; Arora, M.K; Sarkar, S; Gupta, R.P;“A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas”, Eng. Geol. 85, 347- 366, 2006.
[13] Ning, J; Yasuhiro, M; Mowen, X; Ibrahim, D; “Shallow landslide hazard assessment using a three-dimensional deterministic model in a mountainous area”, Comput. Geotech. 45, 1- 10, 2012.
[14] Nash, D; “A comparative review of limit equilibrium methods of slope stability analysis”, In: Anderson, M.G.,Richards, K.J. (Eds.), Slope Stability. Wiley, New York,pp. 11- 75, 1987.
[15] Montgomery, D.R; Dietrich, W.E; “A physically based model for the topographic control on shallow landsliding”, Water Resources Research 30 (4), 1153-1171, 1994.
[16] Lee, S; Ryu, J.H; Min, K; Won, J.S; “Landslide susceptibility analysis using GIS and artificial neural network”, Earth Surface Processes and Landforms 28,1361- 1376, 2003.
[17] Jade, S., Sarkar, S.; ”Statistical Models for Slope Instability Classification”, engineering geology 6, 91- 98,1993.
[18] Yilmaz, I; “Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey:conditional probability, logistic regression, artificial neural networks, and support vector machine”, Environ.Earth Sci. 61, 21- 836, 2010.
[19] Yesilnacar, E; Topal, T; “Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey)”, Eng. Geol. 79 (3- 4), 251- 266, 2005.
[20] Nefeslioglu, H.A; Gokceoglu, C; Sonmez, H; “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 (3- 4), 171- 191, 2008.
[21] Gorsevski, P.V; Gessler, P.E; Foltz, R.B; Elliot, W.J;“Spatial prediction of landslide hazard using logistic regression and ROC analysis”, Transactions in GIS 10 (3), 395- 415, 2006.
[22] Purghasemi, H. R; Moradi, H. R; Mohammadi,M; Mostafazadeh, R; Golijirandeh, A, “A Landslide susceptibility mapping by usin Bayesian Theory”,Articultural Magazine. 62, 1391. (In Persian)
[23] Frattini ,P; Crosta, G; Carrara, A; “Techniques for evaluating the performance of landslide susceptibility models”, Engineering Geology 111, 62- 72, 2010.
[24] ZayandAb CO; “Geology report of BeheshtAbad Dam reservoir”, Preliminary Report, Isfahan, 1382. (In Persian)
[25] Coates, D. R; “Landslide Perspectives, In: Landslides”,Geological Society of America, pp.3- 28, 1977.
[26] Van Western, C.J; “Use of weights of evidence modeling for landslide susceptibility mapping”,International Institute for Geoinformation Science and Earth Observation”, 21 pp, 2002.
[27] Jalali, N; "Assessment of common methods for “Landslide susceptibility mapping in Taleqan watersheld area”, 1th Symposium on Landslides researches, Tehran,pp. 103-115, 1381. (In Persian)
[28] Van Westen, C. J; “Application of Geographic Information Systems to landslide Hazard Zonation”, vol.1,Theory, International Institute for Aerospace Survey and Earth Sciences (ITC) Publication, No. 15, 1993.
[29] Gholami, M; Ajalloeian, R; “Determination of most effective parameters in landslide occurrence in Behesht Abad Dam reservoir by using CF model”, 3th Iranian Rock Mechanics conference, Tehran, Iran, 1386. (In Persian)
[30] Gholami, M; Musazadeh, M; Ajalloeian, R; “Application of ANN to determine the weight of effective parameters on landslide, case study”, 11th Iranian Geology Association conference, Mashhad, Iran, 1386. (In Persian)
[31] Research Center of Natural catastrophes; “A guideline to prepare Landslide Hazard Zonation Maps in Iran”,Final report, Tehran, 108p, 1384. (In Persian)
[32] Rakei, B; “Landslide Hazard Zonation by using ANN in Sefidargole, Semnan Porvince”, MS.c Thesis, Modarres university, 1382. (In Persian)
[33] Gee, M. D; “Classification of Landslide Hazard Zonation Methods and a Test of Predictive Capability”, 6th International Symposium on Landslides: Christchurch,New Zealand, pp. 947- 952, 1992.
[34] Demuth, H; Beale, B; “Neural Network Toolbox(User's Guide) For Use With MATLAB”, 1994.
[35] Zhu, C; Wang. X; “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 187, 342- 346, 2009.