In this article, the authors discuss modeling and simulation of forest fire spread and suppression using the discrete event system specification (DEVS) cell space approach in DEVSJAVA. The event-based modeling approach enables efficient simulation of cell space and allows one to obtain timely simulation-based predictions of forest fire spread and suppression in uniform and nonuniform environmental conditions. This model represents an advance toward developing a real-time decision support simulation system for predicting forest fire spread and the effects of water-based suppression attempts.
Andrews, P. L. , and L. P. Queen . 2001. Fire modeling and information system technology . International Journal of Wildland Fire10: 343-352 .
2.
Zeigler B. P. , H. Praehofer , and T. G. Kim . 2000. Theory of modeling and simulation. 2nd ed.New York: Academic Press .
3.
Wainer, G. , and N. Giambiasi . 2001. Timed cell-DEVS: Modeling and simulation of cell spaces. In Discrete event modeling & simulation: Enabling future technologies, edited by H. S. Sarjoughian , 187-213. New York: Springer-Verlag .
4.
Zeigler, B. P.2003. Discrete event abstraction: An emerging paradigm for modeling complex adaptive systems. In Advances in adaptive complex systems, edited by L. Booker.Oxford, UK: Sante Fe Institute/Oxford Press .
5.
Ameghino, J. , A. Troccoli , and G. Wainer . 2001. Models of complex physical systems using Cell-DEVS . In Proceedings of the Annual Simulation Symposium, Seattle, WA.
6.
Muzy, A. , G. Wainer , E. Innocenti , A. Aiello , and J. F. Santucci . 2002. Comparing simulation methods for fire spreading across a fuel bed . In Proceedings of AIS’2002, Lisbon, Portugal.
7.
Hu, X. , and B. P. Zeigler . 2004. A high performance simulation engine for large-scale cellular DEVS models . In High Performance Computing Symposium (HPC’04), Advanced Simulation Technologies Conference.
8.
Zeigler, B. P. , and H. Sarjoughian . 2002. Introduction to DEVS modeling and simulation with JAVA: A simplified approach to HLA-compliant distributed simulations. Tucson: University of Arizona . Accessed from http://www.acims.arizona.edu
9.
Muzy, A. , E. Innocenti , J. F. Santucci , and D. R. C. Hill . 2003. Optimization of cell spaces simulation for the modeling of fire spreading . In Proceedings of the 36th Annual Simulation Symposium, Orlando, FL, pp. 289-296 .
10.
Vasconcelos, J. M. 1993. Modeling spatial dynamic ecological processes with DEVS-scheme and geographical information systems. Ph.D. diss., Department of Renewable and Natural Resources, University of Arizona, Tucson.
11.
Rothermel, R.1972. A mathematical model for predicting fire spread in wildland fuels. Research Paper INT-115, U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, Ogden, UT.
12.
Andrews, P. L.1986. BEHAVE: Fire behavior predictions and fuel modeling system-BURN subsystem part 1. General Tech. Rep. INT-194, U.S. Department of Agriculture, Forest Service, Inter-mountain Forest and Range Experiment Station, Ogden, UT.
13.
Anderson, H. E.1983. Predicting wind-driven fire size and shape. Research Paper INT-305, U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, Ogden, UT.
14.
Alexander, M. E. 1985. Estimating the length-to-breadth ratio of elliptical forest fire patterns . In Proceedings of the Eighth Conference on Fire and Forest Meteorology, Bethesda, MD, pp. 287-304 .
15.
Nelson, R. M., Jr.2002. An effective wind speed for models of fire spread . International Journal of Wildland Fire11: 153-161 .
16.
Byram, G. M.1959. Combustion of forest fuels. In Forest fire: Control and use, edited by K. P. Davis , 113-126. New York: McGraw-Hill .
17.
Albini, F.A.1976. Estimating wildfire behavior and effects. General Tech. Rep. INT-30, U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, Ogden, UT.
18.
Miller, C. , and D. L. Urban . 1999. Forest pattern, fire, and climatic change in the Sierra Nevada . Ecosystems2: 76-87 .
19.
Rothermel, R. C. , and G. C. Rinehard . 1983. Field procedures for verification and adjustment of fire behavior predictions. General Tech. Rep. INT-142, U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, Ogden, UT.
20.
Zeigler, B. P.1984. Multifacetted modelling and discrete event simulation. London: Academic Press .
21.
Zeigler, B. P.1990. Object-oriented simulation with hierarchical, modular models: Intelligent analysis and endomorphic systems. Boston: Academic Press .
22.
Morais, M.2001. Modeling fire behavior in chaparral with HFire. Accessed from http://www.physics.ucsb.edu/%7Ecomplex/research/hfire/fbehave/refs/morais_2001_hfire_pres.pdf
23.
Morais, M. 2001. Comparing spatially explicit models of fire spread through chaparral fuels: A new model based upon the Rothermel fire spread equation. M.A. thesis, University of California, Santa Barbara.
24.
Sneeuwjagt, R. J. , and W. H. Frandsen . 1977. Behavior of experimental grass fires vs. predictions based on Rothermel’s fire model . Canadian Journal of Forest Research7: 357-367 .
25.
Wilgen, B. W.V. , D. C. L. Maitre , and F. J. Kruger . 1985. Fire behavior in South African Fynbos (Macchia) vegetation and prediction from Rothermel’s fire model . Journal of Applied Ecology22: 207-216 .
26.
Vasconcelos, J. M. , B. P. Zeigler , and J. Pereira . 1995. Simulation of fire growth in GIS using discrete event hierarchical modular models . Advances in Remote Sensing4 (3): 54-62 .
