A Computational Model for Wildfire Propagation: The Case of Vale Natural Reserve
Resumo
This document describes a research project about the development and application of a wildfire propagation computational model, which simulates the occurrence and spread of wildfires in order to understand and predict their behaviour. The aim is to assist in the prevention, fighting and management of wildfires. It has been applied at Vale Natural Reserve (RNV) and Sooretama Biological Reserve (REBIO), in the State of Espírito Santo, southeast of Brazil. These reserves are a constant target for forest wildfires, and they represent approximately 13% of the remaining Atlantic Forest in the State of Espírito Santo. Initial results were obtained comparing three cases of observed wildfires with predicted area, which shows a satisfactory level of area agreement.
Palavras-chave:
Artificial Life and Real-Time Simulation, Decision Support Systems, Technologies for Wildfires Management
Referências
Alessandri, A., Bagnerini, P., Gaggero, M., and Mantelli, L. (2021). Parameter estimation of fire propagation models using level set methods. Applied Mathematical Modelling, 92:731–747.
Batty, M. (2000). Geocomputation using cellular automata. In Geocomputation, pages 95–126. New York: Taylor & Francis.
Cheney, P. and Sullivan, A. (2008). Grassfires: fuel, weather and fire behaviour. Csiro Publishing.
Clarke, K., Fischer, M., and Nijkamp, P. (2014). Cellular automata and agent-based models. Handbook of Regional Science.
Columbia, B. (2023). All about wildfire. [link]. Last accessed: 30 June 2023.
Encinas, A. H., Encinas, L. H., White, S. H., del Rey, A. M., and Sánchez, G. R. (2007). Simulation of forest fire fronts using cellular automata. Advances in Engineering Software, 38(6):372–378.
Filippi, J., Mallet, V., and Nader, B. (2014). Representation and evaluation of wildfire propagation simulations. International Journal of Wildland Fire, 23:46–57.
Finney, A. (2000). Efforts at comparing simulated and observed fire growth patterns. USDA Forest Service, Rocky Mountain Research Station, Fire Science Laboratory. Final Report INT-95066-RJV.
Fujioka, F. M. (2002). A new method for the analysis of fire spread modeling errors. International Journal of Wildland Fire, 11:193–203.
INPE (2024). Área queimada 1km. [link]. Last accessed: 12 June 2024.
Maduro, R. (2012). Modelagem da propagação do fogo como ferramenta de auxílio a tomada de decisao no combate e prevenção de incendios no parque nacional das emas, go. tese de doutorado do curso de pós-graduação em computação aplicada. instituto nacional de pesquisas espaciais – INPE.
McArthur, A. G. (1966). Weather and grassland fire behaviour. Leaflet. Forestry Timber Bureau Australia.
Noble, I., Gill, A., and Bary, G. (1980). Mcarthur’s fire-danger meters expressed as equations. Australian journal of ecology, 5(2):201–203.
Pyne, S. J., Andrews, P. L., and Laven, R. D. (1996). Introduction to wildland fire. John Wiley and Sons.
Ribeiro, M. C., Metzger, J. P., Martensen, A. C., Ponzoni, F. J., and Hirota, M. M. (2009). The brazilian atlantic forest: How much is left, and how is the remaining forest distributed? implications for conservation. Biological conservation, 142(6):1141–1153.
Silva, J., Marques, J., Gonçalves, I., Brito, R., Teixeira, S., Teixeira, J., and Alvelos, F. (2022). A systematic review and bibliometric analysis of wildland fire behavior modeling. Fluids, 7(12):374.
Sullivan, A. L. (2009). Wildland surface fire spread modelling, 1990–2007. 2: Empirical and quasi-empirical models. International Journal of Wildland Fire, 18(4):369–386.
Tonini, M., D’Andrea, M., Biondi, G., Degli Esposti, S., Trucchia, A., and Fiorucci, P. (2020). A machine learning-based approach for wildfire susceptibility mapping. the case study of the liguria region in italy. Geosciences, 10(3):105.
Weisstein, E. W. (2023). ”cellular automaton”. mathworld. [link]. Last accessed: 18 May 2023.
Wu, Z., Wang, B., Li, M., Tian, Y., Quan, Y., and Liu, J. (2022). Simulation of forest fire spread based on artificial intelligence. Ecological Indicators, 136:108653.
Batty, M. (2000). Geocomputation using cellular automata. In Geocomputation, pages 95–126. New York: Taylor & Francis.
Cheney, P. and Sullivan, A. (2008). Grassfires: fuel, weather and fire behaviour. Csiro Publishing.
Clarke, K., Fischer, M., and Nijkamp, P. (2014). Cellular automata and agent-based models. Handbook of Regional Science.
Columbia, B. (2023). All about wildfire. [link]. Last accessed: 30 June 2023.
Encinas, A. H., Encinas, L. H., White, S. H., del Rey, A. M., and Sánchez, G. R. (2007). Simulation of forest fire fronts using cellular automata. Advances in Engineering Software, 38(6):372–378.
Filippi, J., Mallet, V., and Nader, B. (2014). Representation and evaluation of wildfire propagation simulations. International Journal of Wildland Fire, 23:46–57.
Finney, A. (2000). Efforts at comparing simulated and observed fire growth patterns. USDA Forest Service, Rocky Mountain Research Station, Fire Science Laboratory. Final Report INT-95066-RJV.
Fujioka, F. M. (2002). A new method for the analysis of fire spread modeling errors. International Journal of Wildland Fire, 11:193–203.
INPE (2024). Área queimada 1km. [link]. Last accessed: 12 June 2024.
Maduro, R. (2012). Modelagem da propagação do fogo como ferramenta de auxílio a tomada de decisao no combate e prevenção de incendios no parque nacional das emas, go. tese de doutorado do curso de pós-graduação em computação aplicada. instituto nacional de pesquisas espaciais – INPE.
McArthur, A. G. (1966). Weather and grassland fire behaviour. Leaflet. Forestry Timber Bureau Australia.
Noble, I., Gill, A., and Bary, G. (1980). Mcarthur’s fire-danger meters expressed as equations. Australian journal of ecology, 5(2):201–203.
Pyne, S. J., Andrews, P. L., and Laven, R. D. (1996). Introduction to wildland fire. John Wiley and Sons.
Ribeiro, M. C., Metzger, J. P., Martensen, A. C., Ponzoni, F. J., and Hirota, M. M. (2009). The brazilian atlantic forest: How much is left, and how is the remaining forest distributed? implications for conservation. Biological conservation, 142(6):1141–1153.
Silva, J., Marques, J., Gonçalves, I., Brito, R., Teixeira, S., Teixeira, J., and Alvelos, F. (2022). A systematic review and bibliometric analysis of wildland fire behavior modeling. Fluids, 7(12):374.
Sullivan, A. L. (2009). Wildland surface fire spread modelling, 1990–2007. 2: Empirical and quasi-empirical models. International Journal of Wildland Fire, 18(4):369–386.
Tonini, M., D’Andrea, M., Biondi, G., Degli Esposti, S., Trucchia, A., and Fiorucci, P. (2020). A machine learning-based approach for wildfire susceptibility mapping. the case study of the liguria region in italy. Geosciences, 10(3):105.
Weisstein, E. W. (2023). ”cellular automaton”. mathworld. [link]. Last accessed: 18 May 2023.
Wu, Z., Wang, B., Li, M., Tian, Y., Quan, Y., and Liu, J. (2022). Simulation of forest fire spread based on artificial intelligence. Ecological Indicators, 136:108653.
Publicado
17/11/2024
Como Citar
VIADEMONTE, Sergio; SENNA, Mariana; CARNEIRO, Nikolas; BASTOS, Caio; QUADROS, André; ALMEIDA, Rodolfo.
A Computational Model for Wildfire Propagation: The Case of Vale Natural Reserve. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 21. , 2024, Belém/PA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 625-636.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2024.244806.
