Forest Fire Simulation via Epidemiological Models in Graphs
Abstract
This paper explores the prediction of trajectory and expansion potential of wildfires in heterogeneous forests. Our proposed methodology consists of modeling the target area as a finite graph following a regular 2D grid topology, each node representing a specific terrain condition, over which wildfires propagate according to a heterogeneous adaptation of the well-known SIR epidemiological model. As a proof of concept, we performed multiple computer simulations for two distinct topologies and initial random locations for wildfires in each configuration. Our results highlight the model’s ability to simulate fire propagation under various conditions, offering potential for theoretical analysis and aiding in the development of fire containment strategies.References
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Kendall, D. G. (1956). Deterministic and stochastic epidemics in closed populations. In Neyman, J., editor, Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, Volume 4: Contributions to Biology and Problems of Health, pages 149–165. University of California Press.
Kermack, W. and McKendrick, A. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 115(772):700–721.
Kondylatos, S., Prapas, I., Ronco, M., Papoutsis, I., Camps-Valls, G., Piles, M., Fernández-Torres, M.-A., and Carvalhais, N. (2022). Wildfire danger prediction and understanding with deep learning. Geophysical Research Letters, 49(17).
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Pastor-Satorras, R. and Vespignani, A. (2001). Epidemic spreading in scale-free networks. Phys. Rev. Lett., 86:3200–3203.
Sullivan, A. and Gould, J. (2020). Wildland fire rate of spread. In Manzello, S., editor, Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires, pages 1095–1098. Springer, Cham.
Valente, T. W. (1996). Network models of the diffusion of innovations. Computational & Mathematical Organization Theory, 2(2):163–164.
Brasiel, H. C. and Lima, D. A. (2023). Exploring the influence of wind, vegetation and water sources on the spread of forest fires in the brazilian cerrado biome using cellular automata. In Anais do XIV Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais, pages 61–70, Porto Alegre, RS, Brasil. SBC.
Chernikova, A., Gozzi, N., Perra, N., Boboila, S., Eliassi-Rad, T., and Oprea, A. (2023). Modeling self-propagating malware with epidemiological models. Applied Network Science, 8(1):52.
Christakis, N. A. and Fowler, J. H. (2007). The spread of obesity in a large social network over 32 years. New England Journal of Medicine, 357(4):370–379.
de Castro, C. F., Serra, G., Parola, J., Reis, J., Lourenço, L., and Correia, S. (2006). Combate a Incêndios Florestais (Vol. XIII). Sintra: Escola Nacional de Bombeiros, 3ª edição, revista e actualizada edition. Manual de Formação Inicial do Bombeiro.
de Oliveira Alves, N., Vessoni, A. T., Quinet, A., Fortunato, R. S., Kajitani, G. S., Peixoto, M. S., Hacon, S. d. S., Artaxo, P., Saldiva, P., Menck, C. F. M., et al. (2017). Biomass burning in the amazon region causes dna damage and cell death in human lung cells. Scientific reports, 7(1):10937.
Duarte, M. L. (2022). Previsão da suscetibilidade à incêndios e queimadas utilizando um modelo baseado em inteligência artificial e sistema de inferência fuzzy. PhD thesis, Universidade Estadual Paulista (UNESP). Tese de Doutorado.
Ferro, I., Santos, A. L. S., and Martins, S. M. (2020). Queimadas e doenças respiratórias. Sociedade Brasileira de Medicina de Família e Comunidade (SBMFC). Material elaborado pelo Grupo de Trabalho de Problemas Respiratórios - GRESP-SBMFC. Publicado em: 10 de setembro de 2020.
Govindankutty, S. and Gopalan, S. P. (2024). Epidemic modeling for misinformation spread in digital networks through a social intelligence approach. Scientific Reports, 14(1):19100.
Kendall, D. G. (1956). Deterministic and stochastic epidemics in closed populations. In Neyman, J., editor, Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, Volume 4: Contributions to Biology and Problems of Health, pages 149–165. University of California Press.
Kermack, W. and McKendrick, A. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 115(772):700–721.
Kondylatos, S., Prapas, I., Ronco, M., Papoutsis, I., Camps-Valls, G., Piles, M., Fernández-Torres, M.-A., and Carvalhais, N. (2022). Wildfire danger prediction and understanding with deep learning. Geophysical Research Letters, 49(17).
Nascimento, L. (2024). Área queimada no Brasil até novembro quase dobra em relação a 2023. [link]. Accessed: 2024-12-22.
Pastor-Satorras, R. and Vespignani, A. (2001). Epidemic spreading in scale-free networks. Phys. Rev. Lett., 86:3200–3203.
Sullivan, A. and Gould, J. (2020). Wildland fire rate of spread. In Manzello, S., editor, Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires, pages 1095–1098. Springer, Cham.
Valente, T. W. (1996). Network models of the diffusion of innovations. Computational & Mathematical Organization Theory, 2(2):163–164.
Published
2025-07-20
How to Cite
CORREIA, Polyana Graf Finamor; SIMÕES, Jefferson Elbert.
Forest Fire Simulation via Epidemiological Models in Graphs. In: WORKSHOP ON COMPUTING APPLIED TO THE MANAGEMENT OF THE ENVIRONMENT AND NATURAL RESOURCES (WCAMA), 16. , 2025, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 326-335.
ISSN 2595-6124.
DOI: https://doi.org/10.5753/wcama.2025.9477.
