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ارزیابی عملکرد رویکردهای مدلسازی گسترش آتشسوزی جنگل با استفاده از اتوماتای سلولی (پژوهش موردی: جنگلهای بخش لاکان شهرستان رشت) | ||
| تحقیقات جنگل و صنوبر ایران | ||
| مقاله 7، دوره 23، شماره 1، فروردین 1394، صفحه 64-78 اصل مقاله (601.16 K) | ||
| نوع مقاله: علمی- پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijfpr.2015.102359 | ||
| نویسندگان | ||
| طاهره قائمی راد1؛ محمد کریمی* 2 | ||
| 1دانشجوی کارشناسی ارشد مهندسی سیستمهای اطلاعات مکانی، دانشکده نقشهبرداری دانشگاه صنعتی خواجه نصیرالدین طوسی | ||
| 2استادیار، گروه مهندسی سیستمهای اطلاعات مکانی، دانشکده نقشهبرداری دانشگاه صنعتی خواجه نصیرالدین طوسی | ||
| چکیده | ||
| پیشبینی صحیح گسترش آتشسوزی جنگلی موضوعی حیاتی در کاهش اثرات مخرب ناشی از آن است. آتشسوزی جنگل به عاملهای متعددی ازجمله توپوگرافی، پوشش گیاهی و اقلیم بستگی دارد. در حال حاضر یکی از چالشهای موجود در مدلسازی آتشسوزی جنگلی نحوه ارتباط آن با مشخصههای ایستا و پویای مکانی و زمانی مؤثر بر گسترش آتشسوزی ازجمله سرعت و جهت باد است. در این پژوهش، سه رویکرد مطرح در مدلسازی این پارامترها شامل کارافیلیدیس (Karafyllidis)، برجاک (Berjak) و پروجیاس (Progias) بررسی و تحلیل شدند و اهمیت پارامترهای اندازه پیکسل و گامهای زمانی تغییر وضعیت در اتوماتای سلولی مورد توجه قرار گرفت. منطقه موردمطالعه، محدودهای از جنگلهای بخش لاکان شهرستان رشت بود. ابتدا دادههای توپوگرافی، پوشش گیاهی، سرعت و جهت باد جمعآوری و در محیط GIS آمادهسازی شدند. سپس سه رویکرد مطرح در منطقه موردمطالعه پیادهسازی شدند و با انجام آنالیز حساسیت مربوط به پارامترهای اندازه پیکسل و بازههای زمانی، میزان کارایی هر یک از طریق مقایسه جبهه آتش شبیهسازی شده با واقعیت و از طریق محاسبه ضریب کاپا، مورد ارزیابی و مقایسه قرار گرفت. نتایج بهدستآمده حاکی از آن بود که روش برجاک با طول ضلع پیکسل سه تا هفت متر برای مدلسازی گسترش آتش در مناطق جنگلی استان گیلان مناسب است. | ||
| کلیدواژهها | ||
| آتشسوزی جنگل؛ ابعاد پیکسل؛ اتوماتای سلولی؛ گام زمانی؛ مشخصههای پویای مکانی و زمانی | ||
| عنوان مقاله [English] | ||
| Evaluation performances of different forest fire spread models using cellular automata (case study: The forests of Lakan district in Rasht) | ||
| نویسندگان [English] | ||
| Tahereh Ghaemi Rad1؛ Mohammad Karimi2 | ||
| 1M.Sc. Student of GIS Engineering, Department of GIS Engineering, Faculty of Geomatics Engineering, K.N.Toosi University of Technology, Tehran, Iran | ||
| 2Assistant Prof., Department of GIS Engineering, Faculty of Geomatics Engineering, K.N.Toosi University of Technology, Tehran, Iran | ||
| چکیده [English] | ||
| Accurate prediction of forest fire spread is crucial in minimizing its destructive effects. Forest fire depends on various factors e.g. topography, vegetation and climate. One of the challenges in modeling forest fire concerns the way it interacts with static and dynamic spatiotemporal trajectories affecting its spread such as slope, wind speed and wind direction. In this study, three previously developed approaches Karafyllidis, Berjak and Progias for modeling those parameters were analyzed, followed by investigating the effects of pixel size and time steps in a cellular automata. The study was conducted in the Lakan forest district in the vicinity of Rasht in Guilan province. The available topographic, vegetation, wind speed and wind direction data were initially analyzed in GIS. Then the three modeling approaches were implemented, followed by a consequent sensitivity analysis on the pixel size and time steps of switching in cellular automata, The effectiveness of the approaches was compared by means of Kappa coefficient .The results indicate that the Berjak method with a 3-7 m pixel size is more appropriate for modeling the spread of fire across the study site. | ||
| کلیدواژهها [English] | ||
| forest fire, pixel size, Cellular Automata, time steps, dynamic spatiotemporal parameters | ||
| مراجع | ||
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- Alexandridis, A., Vakalis, D., Siettos, C.I. and Bafas, G.V., 2008. A cellular automata model for forest fire spread prediction: The case of the wildfire that swept through Spetses Island in 1990. Applied Mathematics and Computation, 204: 191-201. - Anderson, H., 1982. Aids to determining fuel models for estimating fire behavior. General Technical Report INT-122. - Artes, T., Cencerrado, A., Cortes, A. and Margalef, T., 2013. Relieving the effects of uncertainty in forest fire spread prediction by hybrid MPI-OpenMP parallel strategies. International Conference on Computational science, Procedia Computer Science, 18: 2278-2287. - Beer, T., 1989. The australian national bushfire model. Proceedings of 8th Biennial Conference and Bushfire Dynamics Workshop, Canberra, Australia, pp. 568-572. - Berjak, S. and Hearne, J., 2002. An improved cellular automation model for simulating fire in a spatially heterogeneous Savana system. Ecological Modeling, 148: 133-151. - Boboulos, M. and Purvis, M.I.R., 2009. Wind and slope effects on ROS during the fire propagation in East-Mediterranean pine forest litter. Fire Safety Journal, 44: 764-769. - Brun, C., Artes, T., Margalef, T. and Cortes, A., 2012. Coupling wind dynamics into a DDDAS forest fire propagation prediction system. Procedia Computer Science, 9: 1110-1118. - Chamani Takladani, B., Sadi Mesgari, M. and Yaghoubkhani Ghiasvand, M., 2013. Agent based modeling for forest fire spreading. Third Conference of Environment Programming and Management (In Persian). - Cheney, N.P., 1981. Fire behavior. A.M. Gill, G.H. Groves and I.R. Noble, Editors, Fire and the Australian Biota, Australian Academy of Science, Canberra, 157-175. - Denham, M., Wendt, K., Bianchini, G., Cortés, A. and Margalef, T., 2012. Dynamic Data-Driven Genetic Algorithm for forest fire spread prediction. Journal of Computational Science, 3: 398-404. - Eskandari, S., Oladi Ghadikolaei, J. and Jalilvand, H., 2013. Efficiency evaluation of Dong model for determination of fire risk potential in Zarrin Abad Forests. Iranian Journal of Forest and Poplar Research, 21(3): 439-451 (In Persian). - Gazme, H., Chehreghan, A., Ale-Sheikh, A. and Karimi, M., 2013. Modeling forest fire front spreading using Cellular Automata, Spatial information system and Particle swarm optimization. Journal of Surveying Engineering and Spatial Information, 3: 71-85 (In Persian). - Hernandez Encinas, A., Hernandez Encinas, L., Hoya White, S., Martin del Rey, A. and Rodriguez Sanchez, G., 2007. Simulation of forest fire fronts using cellular automata. Advances in Engineering Software, 38: 372-378. - Innocenti, E., Silvani, X., Muzy, A. and Hill, D., 2009. A software framework for fine grain parallelization of cellular models with OpenMP. Application to Fire Spread, 24: 819-831. - Ivanilova, T.N., 1985. Set Probability identification in forest fire simulation. Proceedings of the 2nd international symposium on systems analysis and simulation, Pergamon, Berlin, 2: 185-188. - Jahdi, R., Darvishsefat, A. and Etemad, V., 2013. Predicting forest fire spread using fire behavior model (Case study: Malekroud Forest-Siahkal). Iranian Journal of Forest and Poplar Research, 5(4): 419-430 (In Persian). - Karafyllidis, I. and Thanailakis, A., 1997. A model for predicting forest fire spreading using cellular automata. Ecological Modeling, 99: 87-97. - Lett, C., Silber, C. and Barret, N., 1999. Comparison of a cellular automata network and an individual-based model for the simulation of forest dynamics. Ecological Modeling, 121: 277-293. - Lopes, A.M.G., Sousa, A.C.M. and Viegas, D.X., 1995. Numerical simulation of turbulent flow and fire propagation in complex topography. Numerical Heat Transfer, Part A, 27: 229-253. - Mallet, V., Keyes, D.E. and Fendell, F.E., 2009. Modeling wildland fire propagation with level set methods. Computers and Mathematics with Applications, 57: 1089-1101. - Nofarasti, S. and Tadayyone Tabrizi, Gh., 2009. Application of cellular automata for simulation of fire spread. National conference of software engineering, 319-327 (In Persian). - Pastor, E., Zarate, L., Planas, E. and Arnaldos, J., 2003. Mathematical models and calculation systems for the study of wildland fire behaviour. Progress in Energy and Combustion Science, 29: 139-153. - Porterie, B., Zekri, N., Clerc, J. and Loraud, J., 2007. Modeling forest fire spread and spotting process with small world networks. Combustion and Flame, 149: 63-78. - Progias, P. and Sirakoulis, G., 2013. An FPGA processor for modeling wildfire spreading. Mathematical and Computer Modeling, 57: 1436-1452. - Quartieri, J., Mastorakia, N., Iannone, G. and Guarnaccia, C., 2011. A Cellular Automata Model for Fire Spreading Prediction. Latest Trends on Urban Planning and Transportation, 173-179. - Rothermel, R.C., 1972. A mathematical model for predicting fire spread in wildland fuels. Technical Report, USDA Forest Service, Intermountain Forest and Range Experiment Station Ogden, Utah 84401, Research Paper INT-115. - Rothermel, R.C., 1983. How to predict the spread and intensity of forest and range fires, A publication of the National Wildfire Coordinating Group. - Sakr, G.E., Elhajj, I.H. and Mitri, G., 2011. Efficient forest fire occurrence prediction for developing countries using two weather parameters. Engineering Applications of Artificial Intelligence, 24: 888-894. - Sarkar, C. and Abbasi, S.A., 2006. Cellular automata-based forecasting of the impact of accidental fire and toxic dispersion in process industries. Journal of Hazardous Materials, A137: 8-30 (In Persian). - Sarkargar Ardakani, A., Valadane Zoj, M.J. and Mansourian, A., 2009.The spatial analysis of fire across the country using RS and GIS. Journal of Ecology, 52: 25-43 (In Persian). - Sirakoulis, Ch., Karafyllidis, I. and Thanailakis, A., 2002. A cellular automaton methodology for the simulation of integrated circuit fabrication processes. Future Generation Computer Systems, 18: 639-657. - Trunfio, G.A., 2004. Predicting Wildfire Spreading Through a Hexagonal Cellular Automata Model. in: Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 3305: 385-394. - Wolfram, S., 1984. Universality and complexity in cellular automata. Physica, 10D: 1-35. - Yassemi, S., Dragicevic, S. and Schmidt, M., 2008. Design and implementation of an integrated GIS-based cellular automata model to characterize forest fire behaviour. Ecological Modeling, 210: 71-84. - Yongzhong, Z., Feng, Z.D., Tao, H., Liyu, W., Kegong, L. and Xin, D., 2004. Simulating wildfire spreading processes in spatially heterogeneous landscapes using an improved cellular automaton model. IGARSS’04, Proceedings of the 2004 IEEE International, 3371-3374. - Yu, N., Li, M. and Ruan, X., 2005. Applications of cellular automata in complex system study. International Journal of Information and Systems Sciences, 1(3-4): 302-310. | ||
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