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مدل‌سازی و پهنه‌بندی حساسیت به زمین‌لغزش مناطق جنگلی به‌منظور طراحی مسیر جاده جنگلی با استفاده از سامانه استنتاج عصبی- فازی تطبیقی | ||
| تحقیقات جنگل و صنوبر ایران | ||
| مقاله 14، دوره 22، شماره 3، آذر 1393، صفحه 509-526 اصل مقاله (681.57 K) | ||
| نوع مقاله: علمی- پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijfpr.2014.12435 | ||
| نویسندگان | ||
| اسماعیل قجر1؛ اکبر نجفی* 2 | ||
| 1استادیار، دانشگاه گیلان | ||
| 2دانشیار، دانشگاه تربیت مدرس | ||
| چکیده | ||
| پژوهش پیش‌رو با استفاده از سیستم استنتاج عصبی- فازی تطبیقی و سیستم اطلاعات جغرافیایی و باتوجه به ویژگی‌های فیزیوگرافی زمین به ارائه مدلی می‌پردازد که قادر به برآورد حساسیت به زمین‌لغزش برای طراحی کم لغزش‌ترین مسیرهای جاده جنگلی باشد. با استخراج شش عامل شیب، جهت، زمین‌شناسی، شکل شیب، فاصله از رودخانه و فاصله از گسل در نقاط لغزشی برداشت‌شده در سطح منطقه موردمطالعه و با استفاده از سیستم استنتاج عصبی- فازی تطبیقی مدل ساخته شد. نتایج شاخص‌های آماری بهترین مدل، ضریب‌تبیین 73/0 و مجذور میانگین مربعات خطای 26/0 را نشان داد. یافته‌های تحلیل حساسیت مدل نشان داد که مهمترین عامل‌های مؤثر در ایجاد حساسیت به زمین‌لغزش به‌ترتیب فاصله از رودخانه‌های اصلی، نوع تشکیلات زمین‌شناسی، شیب زمین، شکل زمین، فاصله از گسل و جهت جغرافیایی بوده‌اند. ارزیابی جاده‌های موجود ازنظر میزان عبور از عرصه‌های حساس به زمین‌لغزش طبق برآورد مدل نشان داد بیشترین سطح جاده‌ها روی طبقات حساسیت «متوسط» و «زیاد» قرار گرفته است. | ||
| کلیدواژه‌ها | ||
| زمین‌لغزش؛ عصبی- فازی؛ مدل؛ جاده جنگلی | ||
| عنوان مقاله [English] | ||
| Modeling landslide susceptibility of a mountain forests using Adaptive Neuro-Fuzzy Inference System (ANFIS) for forest road planning | ||
| نویسندگان [English] | ||
| Ismaeil Ghajar1؛ Akbar Najafi2 | ||
| 1Associate Prof., Department of Forestry, Faculty of Natural Resources, Tarbiat Modares University, Noor, I.R. Iran | ||
| چکیده [English] | ||
| This study presents landslide susceptibility (LS) prediction model using the Adaptive Neuro Fuzzy Inference System (ANFIS) and Geographic Information System (GIS) which incorporates the physiographic information. Such models are is useful for forest road planning. To this aim, a set of factors including the terrain slope, aspect, geology formation, curvature, distance to rivers, and distance to faults at occurred landslide points were integrated into the ANFIS model. The modeling using a subtractive clustering method returned a coefficient of determination (R2) of 0.73 and a root mean square error (RMSE) of 0.27 for the best model. The sensitivity analysis indicated the distance to the rivers, geology formation, terrain slope, curvature, distance to the faults, and aspect as the most effective factors on the landslide occurrence. Furthermore, an evaluation of existing roads on simulated LS map showed that the majority of the currently existing roads are located on “medium” and “high” LS classes. | ||
| کلیدواژه‌ها [English] | ||
| Landslide susceptibility, Neuro-fuzzy, model, ANFIS, forest road | ||
| مراجع | ||
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- Akay, A.E., Boston, K. and Sessions, J. 2005. The evolution of computer-aided road design systems. International Journal of Forest Engineering, 16(2): 73-79. - Andrews, R., Diederich, J. and Tickle, A. 1995. A survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Base Systems, 8: 373-389. - Anonymous, 2011. Baladeh geology map. National Geoscience Database of IRAN. www.ngdir.ir. - Das, I., Sahoo, S., Van Westen, C., Stein, A. and Hack, R. 2010. Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India). Geomorphology, 114: 627-637. - Ercanoglu, M. and Gokceoglu, C. 2002. Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environmental Geology, 41: 720-730. - {Farokhnia, 2010 #3860}Gail, M., Brion, T.R. and Neelakantan, S.L. 2002. A neural-network-based classification scheme for sorting sources and ages of fecal contamination in water. Water Research, 36(15): 3765-3774. - Ghajar, I., Najafi, A. and Ezzati, S. 2010. Skidding machines allocation (SMA) using fuzzy set theory, Croatian Journal of Forest Engineering, 31(2): 99-110. - Ghajar, I., Najafi, A., Torabi, S.A., Khamehchiyan, M. and Boston, K. 2012. An adaptive network-based fuzzy inference system for rock share estimation in forest road construction. Croatian Journal of Forest Engineering, 33(2): 313-328. - Jadda, M., Shafri, H.Z.M., Mansor, S.B., Sharifikia, M. and Pirasteh, S. 2009. Landslide susceptibility evaluation and factor effect analysis using probabilistic-frequency ratio model. European Journal of Scientific Research, 33(4): 654-668. - Kangas, A., Kangas, J. and Laukkanen, S. 2006. Fuzzy multicriteria approval method and its application to two forest planning problems. Forest Science, 52: 232-242. - Kanungo, D.P., Arora, M.K., Sarkar, S. and Gupta, R.P. 2006. A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Engineering Geology, 85: 347- 366. - Lee, S. and Evangelista, D.G. 2006. Earthquake-induced landslide-susceptibility mapping using an artificial neural network. Natural Hazards Earth System Science, 6: 687-695. - Melchiorre, C., Matteucci, M., Azzoni, A. and Zanchi, A. 2008. Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology, 94: 379-400. - Mendoza, G.A. and Sprouse, W. 1989. Forest planning and decision making under fuzzy environments: an overview and illustration. Forest Science, 35: 481-502. - Mendoza, G.A., Bare, B.B. and Zhou, Z. 1993. A fuzzy multiple objective linear programming approaches to forest planning under uncertainty. Agricultural Systems, 41: 257-274. - Moradi, H.R., Sepahvand, A.R. and Abdolmaleki, P. 2012. Assessment of the effect of input factors number in accuracy of artificial neural network for landslide hazard zonation (Case study: Haraz watershed). Journal of Range and Watershed Management, Iranian Journal of Natural Resources, 65(2): 243-255 (In Persian). - Pradhan, B. and Lee, S. 2009. Landslide risk analysis using artificial neural network model focusing on different training sites. International Journal of Physical Sciences. 3(11): 1-15. - Pradhan, B., Akcapinar Sezer, E., Gokceoglu, C. and Buchroithner, M.F. 2010. Land- slide susceptibility mapping by neuro-fuzzy approach in a landslide prone area (Cameron Highland, Malaysia). IEEE Transactions on Geoscience and Remote Sensing, 48(12): 4164-4177. - Pourghasemi, H.R., Pradhan B. and Gokceoglu, C. 2012. Application of fuzzy logic and analytic hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural Hazard, 63(2): 965-996. - Rakei, B., Khamehchiyan, M.,Abdolmalekei, P. and Giahchi, P. 2007. Application of artificial neural network for landslide hazard zonation. Journal of Science, 33(1): 57-64. - Roger, J.S. and Sun, C. 1993. Functional equivalence between radial basis function networks and fuzzy inference systems. IEEE Transaction Neural Network, 4: 156-159. - Roger, J.S. and Sun, C. 1993. Functional equivalence between radial basis function networks and fuzzy inference systems. IEEE Transaction Neural Network, 4: 156-159. - Soori, S, Lashkaripour, G., Ghafouri, M. and Farhadinajad, T. 2011. Landslide hazard zonation using artificial neural networks A case study: Keshvari watershed (Nozhiyan). Journal of Engineering Geology, 5(2): 1269-1286. - Sreekanth, P.D., Sreedevi, P.D., Ahmed, S. and Geethanjali, N. 2010. Comparison of FFNN and ANFIS models for estimating groundwater level. Environmental Earth Sciences, 62(6): 1301-1310. - Tecle, A., Duckstein, L. and Korhonen, P. 1994. Interactive multi-objective programming for forest resources management. Applied Mathematics and Computations, 63: 75-93. - Tunusluoglu, M.C., Gokceoglu, C., Nefeslioglu, H.A. and Sonmez, H. 2008. Extraction of potential debris source areas by logistic regression technique: a case study from Barla, Besparmak and Kapi mountains (NW Taurids, Turkey). Environmental Geology, 54(1): 9-22. - Van Westen, C.J., Castellanos, E. and Kuriakose, S.L. 2008. Spatial data for landslide susceptibility, hazard and vulnerability assessment: An overview. Engineering Geology, 102: 112-131. - Yilmaz, I. 2010. Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environmental Earth Sciences, 61(4): 821-836. - Youssef, A.M., Pradhan, B., Gaber, A.F.D. and Buchroithner M.F. 2009. Geomorphological hazard analysis along the Egyptian red sea coast between Safaga and Quseir. Natural Hazards and Earth System Science, 9(3): 751-766. - Zadeh, L.A. 1965. Fuzzy sets. Information and Control, 8: 338-353. | ||
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