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Modeling and simulation of fire spreading through the activity tracking paradigm

Author

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  • Muzy, A.
  • Nutaro, J.J.
  • Zeigler, B.P.
  • Coquillard, P.

Abstract

Modeling and simulation is essential for understanding complex ecological systems. However, knowledge of the structure and behavior of these systems is limited, and models must be revised frequently as our understanding of a system improves. Moreover, the dynamic, spatial distribution of activity in very large systems necessitates mapping natural mechanisms as logically as possible onto computer structures. This paper describes theoretical and algorithmic tools for building component-based models and simulations of dynamic spatial phenomena. These methods focus attention on and exploit the irregular distribution of activity in ecological processes. We use the DEVS formalism as the basis for a component-based approach to modeling spatially distributed systems. DEVS is a mathematical theory of discrete-event systems that is well suited for describing large systems that are described by small parts with irregular, short-range interactions. This event-based approach to modeling leads naturally to efficient simulations algorithms which focus on the active parts of a large model. Ecological modeling benefits from these efficient the simulation algorithms and the reusability of the model’s basic components. Our event-based method is demonstrated with a physics-based model of fire spread.

Suggested Citation

  • Muzy, A. & Nutaro, J.J. & Zeigler, B.P. & Coquillard, P., 2008. "Modeling and simulation of fire spreading through the activity tracking paradigm," Ecological Modelling, Elsevier, vol. 219(1), pages 212-225.
  • Handle: RePEc:eee:ecomod:v:219:y:2008:i:1:p:212-225
    DOI: 10.1016/j.ecolmodel.2008.08.017
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    References listed on IDEAS

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    1. Lawrie, Jock & Hearne, John, 2007. "Reducing model complexity via output sensitivity," Ecological Modelling, Elsevier, vol. 207(2), pages 137-144.
    2. Alonso-Sanz, Ramón, 2007. "A structurally dynamic cellular automaton with memory," Chaos, Solitons & Fractals, Elsevier, vol. 32(4), pages 1285-1295.
    3. Yassemi, S. & Dragićević, S. & Schmidt, M., 2008. "Design and implementation of an integrated GIS-based cellular automata model to characterize forest fire behaviour," Ecological Modelling, Elsevier, vol. 210(1), pages 71-84.
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    Cited by:

    1. Carlos Díaz‐Avalos & Pablo Juan, 2022. "Modeling the spatial evolution wildfires using random spread process," Environmetrics, John Wiley & Sons, Ltd., vol. 33(8), December.
    2. Monedero, Santiago & Ramirez, Joaquin & Cardil, Adrián, 2019. "Predicting fire spread and behaviour on the fireline. Wildfire analyst pocket: A mobile app for wildland fire prediction," Ecological Modelling, Elsevier, vol. 392(C), pages 103-107.
    3. Hamed Adab & Kasturi Devi Kanniah & Karim Solaimani, 2021. "Remote sensing-based operational modeling of fuel ignitability in Hyrcanian mixed forest, Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(1), pages 253-283, August.

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