Giuseppe A. Trunfio
Donato D’Ambrosio
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Abstract
Cell-based methods for simulating wildfires can be computationally more efficient than techniques based on the fire perimeter expansion. In spite of this, their success has been limited by the distortions that plague the simulated shapes. This article presents a novel algorithm for wildfire simulation through Cellular Automata (CA), which is able to effectively mitigate the problem of distorted fire shapes. Such a result is obtained allowing spread directions that are not constrained to the few angles imposed by the lattice of cells and the neighborhood size. The characteristics of the proposed algorithm are empirically investigated under homogeneous conditions through some comparisons with the outcomes of a typical CA-based simulator. Also, using two significant heterogeneous landscapes, a comparison with the vector-based simulator FARSITE is discussed. According to the results of this study, the proposed approach performs significantly better, in terms of accuracy, than the CA taken as reference. In addition, at a far less computational cost, it provides burned regions that are equivalent, for practical purposes, to those given by FARSITE.
- Albini, F. and Reinhardt, E. 1995. Modeling the ignition and burning rate of large woody natural fuels. Int. J. Wildland Fire 5, 2, 81--92.Google Scholar
Cross Ref
- Albini, F. A. 1979. Spot fire distance from burning trees-a predictive model. Tech. rep. INT-56, USDA, Forest Service.Google Scholar
- Alexander, M. 1985. Estimating the length-to-breadth ratio of elliptical forest fire patterns. In Proceedings of the 8th Conference Fire and Forest Meteorology. 287--304.Google Scholar
- Anderson, D., Catchpole, E., DeMestre, N., and Parkes, T. 1982. Modeling the spread of grass fires. J. Aust. Math. Soc. (B) 23, 451--466.Google ScholarCross Ref
- Anderson, H. 1983. Predicting wind-driven wildland fire size and shape. Tech. rep. INT-305, USDA, Forest Service.Google Scholar
- Andrews, P. 1986. BEHAVE: Fire behavior prediction and fuel modeling system - burn subsystem, part 1. Tech. rep. INT-194, USDA, Forest Service.Google ScholarCross Ref
- Arca, B., Duce, P., Pellizzaro, M. L. G., Salis, M., and Spano, D. 2007. Evaluation of FARSITE simulator in mediterranean maquis. Int. J. Wildland Fire 16, 563--572.Google ScholarCross Ref
- Avolio, M. V., Crisci, G. M., Di Gregorio, S., Rongo, R., Spataro, W., and D’Ambrosio, D. 2006. Pyroclastic flows modelling using cellular automata. Comput. Geosci. 32, 7, 897--911.Google ScholarCross Ref
- Blecic, I., Cecchini, A., and Trunfio, G. A. 2009. A general-purpose geosimulation infrastructure for spatial decision support. Trans. Computat. Sci. 6, 200--218.Google Scholar
- Bose, C., Bryceb, R., and Dueckc, G. 2009. Untangling the Prometheus nightmare. In Proceedings of the 18th World IMACS/MODSIM Congress. 74--80.Google Scholar
- Carmel, Y., Paz, S., Jahashan, F., and Shoshany, M. 2009. Assessing fire risk using monte carlo simulations of fire spread. Forest Ecol. Manage. 257, 1, 370--377.Google ScholarCross Ref
- Catchpole, E., Alexander, M., and Gill, A. 1992. Elliptical-fire perimeter- and area-intensity distributions. Canad. J. Forest Res. 22, 968--972.Google ScholarCross Ref
- Cui, W. and Perera, A. H. 2008. A study of simulation errors caused by algorithms of forest fire growth models. Tech. rep. 167, Ontario Forest Research Institute.Google Scholar
- D’Ambrosio, D., Di Gregorio, S., Gabriele, S., and Gaudio, R. 2001. A cellular automata model for soil erosion by water. Phys. Chem. Earth-B 26, 1, 33--39.Google ScholarCross Ref
- D’Ambrosio, D., Iovine, G., Spataro, W., and Miyamoto, H. 2007. A macroscopic collisional model for debris-flows simulation. Environ. Model. Softw. 22, 10, 1417--1436. Google ScholarDigital Library
- Di Gregorio, S. and Serra, R. 1999. An empirical method for modelling and simulating some complex macroscopic phenomena by cellular automata. Future Gen. Comput. Syst. 16, 2-3, 259--271. Google ScholarDigital Library
- Di Gregorio, S., Serra, R., and Villani, M. 1999. Applying cellular automata to complex environmental problems: The simulation of the bioremediation of contaminated soils. Theoret. Comput. Sci. 217, 1, 131--156. Google ScholarDigital Library
- Finney, M. A. 2004. FARSITE: Fire area simulator-model development and evaluation. Tech. rep. RMRS-RP-4, USDA, Forest Service.Google Scholar
- Forthofer, J., Shannon, K., and Butler, B. 2009. Simulating diurnally driven slope winds with windninja. In Proceedings of 8th Symposium on Fire and Forest Meteorological Society.Google Scholar
- French, I. 1992. Visualisation techniques for the computer simulation of bushfires in two dimensions. M.S. thesis, Department of Computer Science, University of New South Wales - Australian Defence Force Academy.Google Scholar
- French, I., Anderson, D., and Catchpole, E. 1990. Graphical simulation of bushfire spread. Math. Comput. Model. 13, 67--71.Google ScholarDigital Library
- Green, D. G. 1983. Shapes of simulated fires in discrete fuels. Ecolog. Model. 20, 1, 21--32.Google ScholarCross Ref
- Green, D. G., Gill, A. M., and Noble, I. R. 1983. Fire shapes and the adequacy of fire-spread models. Ecolog. Model. 20, 1, 33--45.Google ScholarCross Ref
- Hu, X. and Ntaimo, L. 2009. Integrated simulation and optimization for wildfire containment. ACM Trans. Model. Comput. Simul. 19, 4, 1--29. Google ScholarDigital Library
- Johnston, P., Kelso, J., and Milne, G. 2008. Efficient simulation of wildfire spread on an irregular grid. Int. J. Wildland Fire 17, 614--627.Google ScholarCross Ref
- Kourtz, P. H. and O’Regan, W. G. 1971. A model for a small forest fire to simulate burned and burning areas for use in a detection model. Forest Sci. 17, 7, 163--169.Google Scholar
- Lopes, A. M. G., Cruz, M. G., and Viegas, D. X. 2002. Firestation - An integrated software system for the numerical simulation of fire spread on complex topography. Environ. Model. Softw. 17, 3, 269--285.Google ScholarCross Ref
- McAlpine, R., Lawson, B., and Taylor, E. 1991. Fire spread across a slope. In Proceedings of the 11th Conference on Fire and Forest Meteorology. 218--225.Google Scholar
- Moore, E. F. 1962. Machine models of self reproduction. In Proceedings of the Symposia in Applied Mathematics 14. 17--33.Google ScholarCross Ref
- O’Regan, W. G. 1976. Bias in the contagion analog to fire spread. Forest Sci. 22.Google Scholar
- Peet, G. 1967. The shape of mild fires in Jarrah forest. Australian Forestry 31, 121--127.Google ScholarCross Ref
- Peterson, S. H., Morais, M. E., Carlson, J. M., Dennison, P. E., Roberts, D. A., Moritz, M. A., and Weise, D. R. 2009. Using HFIRE for spatial modeling of fire in shrublands. Tech. rep. PSW-RP-259, USDA, Forest Service, Pacific Southwest Research Station, Albany, CA.Google Scholar
- Richards, G. D. 1990. An elliptical growth model of forest fire fronts and its numerical solution. Int. J. Numer. Meth. Eng. 30, 6, 1163--1179.Google ScholarCross Ref
- Richards, G. D. 1999. The mathematical modeling and computer simulation of wildland fire perimeters for heterogeneous fuel and meteorological conditions. Int. J. Wildland Fire 9, 3, 213--221.Google ScholarCross Ref
- Rongo, R., Spataro, W., D’Ambrosio, D., Avolio, M. V., Trunfio, G. A., and Di Gregorio, S. 2008. Lava flow hazard evaluation through cellular automata and genetic algorithms: An application to Mt. Etna Volcano. Fund. Inf. 87, 2, 247--267. Google ScholarDigital Library
- Rothermel, R. C. 1972. A mathematical model for predicting fire spread in wildland fuels. Tech. rep. INT-115, USDA, Forest Service, Intermountain Forest and Range Experiment Station, Ogden, UT.Google Scholar
- Rothermel, R. C. 1983. How to predict the spread and intensity of forest and range fires. Tech. rep. INT-143, USDA, Forest Service, Intermountain Forest and Range Experiment Station, Ogden, UT.Google Scholar
- Salles, T., Lopez, S., Cacas, M., and Mulder, T. 2007. Cellular automata model of density currents. Geomorphology 88, 1-2, 1--20.Google ScholarCross Ref
- Salles, T., Mulder, T., Gaudin, M., Cacas, M., Lopez, S., and Cirac, P. 2008. Simulating the Capbreton canyon turbidity current with a cellular automata model. Geomorphology 97, 3-4, 516--537.Google ScholarCross Ref
- Sanderlin, J. and Sunderson, J. 1975. A simulation for wildland fire management planning support (FIREMAN). Mission Research Corp. Contract 21-343, Spec. 222.Google Scholar
- Sullivan, A. 2009. Wildland surface fire spread modelling, 1990-2007. 3: Simulation and mathematical analogue models. Int. J. Wildland Fire 18, 387--403.Google ScholarCross Ref
- Tan, P.-N., Steinbach, M., and Kumar, V. 2005. Introduction to Data Mining 1st Ed. Addison-Wesley Longman Publishing Co., Inc. Google ScholarDigital Library
- Torrens, P. M. and Benenson, I. 2005. Geographic automata systems. Int. J. Geograph. Inf. Sci. 19, 4, 385--412.Google ScholarCross Ref
- Trunfio, G. A. 2004. Predicting wildfire spreading through a hexagonal cellular automata model. In Proceedings of the ACRI. Lecture Notes in Computer Science, vol. 3305, Springer, 385--394.Google Scholar
- Tymstra, C., Bryce, R., Wotton, B., Taylor, S., and Armitage, O. 2010. Development and structure of Prometheus: The Canadian wildland fire growth simulation model. Tech. rep. NOR-X-417, Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Edmonton, Alberta.Google Scholar
- Van Wagner, C. 1969. A simple fire growth model. Forestry Chron. 45, 103--104.Google ScholarCross Ref
- Van Wagner, C. E. 1977. Conditions for the start and spread of crown fire. Canad. J. Forest Res. 7, 1, 23--34.Google ScholarCross Ref
- Vasconcelos, J., Zeigler, B., and Pereira., J. 1995. Simulation of fire growth in GIS using discrete event hierarchical modular models. Adv. Remote Sensing 4, 3, 54--62.Google Scholar
- von Neumann, J. 1966. Theory of Self Reproducing Automata. University of Illinois Press, Urbana. Google ScholarDigital Library
- Wainer, G. A. and Castro, R. 2010. A survey on the application of the cell-devs formalism in cellular models. J. Cell. Automata 5, 6, 509--524.Google Scholar
- Yassemi, S., Dragicevic, S., and Schmidt, M. 2008. Design and implementation of an integrated GIS-based cellular automata model to characterize forest fire behaviour. Ecolog. Model. 210, 1-2, 71--84.Google ScholarCross Ref
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- A New Algorithm for Simulating Wildfire Spread through Cellular Automata
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