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A data-based model for predicting wildfires in Chapada das Mesas National Park in the State of Maranhão

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Abstract

Chapada das Mesas National Park extends over an area of 160,046 ha in the municipalities of Carolina, Riachão, Estreito and Imperatriz in the south central region of the state of Maranhão, northeast Brazil, in a savanna-like biome known as the Cerrado. The park has a rich biodiversity, making the need for conservation all the more important. The weather conditions in the region increase the likelihood of wildfires, so that a monitoring and control system for the area is needed to help conservation efforts. This article proposes a methodology that uses data-mining techniques to predict outbreaks of wildfires in the park some hours in advance. Predictive models using wildfire records and a meteorology dataset for 11 months in 2010 were built. Two different classification techniques for predicting wildfires were used: artificial neural networks and classification rules. The two models built with these techniques showed good accuracy when tested with the validation samples and could be used as additional tools for predicting the risk of fires in the area.

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References

  • Gasull VG et al (2011) Computational intelligence applied to wildfire prediction using wireless sensor networks. In: IEEE 2011 Proceedings of the International Conference on Data Communication Networking (DCNET), pp 1–8

  • Arruda RSV (1999) Populações Tradicionais e a proteção de recursos naturais em unidades de conservação. Ambiente e Sociedade, São Paulo, v. ano II, no 5, pp 79–93

  • Beckage B, Platt WJ (2003) Predicting severe wildfire years in the Florida Everglades. Front Ecol Environ 1(5):235–239

    Article  Google Scholar 

  • Chu PS, Yan W, Fujioka F (2002) Fire-climate relationships and long-lead seasonal wildfire prediction for Hawaii. Int J Wildland Fire 11(1):25–31. doi:10.1071/WF01040

    Article  Google Scholar 

  • Das A, Dutta R, Aryal J (2013) A hybrid neural network based Australian wildfire prediction: a novel approach using environmental and satellite imagery. In: 20th International Congress on Modelling and Simulation (MODSIM2013), p 169. http://ecite.utas.edu.au/86618

  • de Medeiros JS (1999) Bancos de dados geográficos e redes neurais artificiais: tecnologias de apoio à gestão do território. Tese (Doutorado em Geografia Física)—Faculdade de Filosofia, Letras e Ciências Humanas, Universidade de São Paulo, São Paulo

  • Goslar A (2006) Ground vegetation biomass detection for fire prediction from remote sensing data in the lowveld region (Doctoral dissertation, University of Johannesburg)

  • Han J, Kamber H (2001) Data mining—concepts and techniques. Morgan Kaufmann Publishers, San Francisco

    Google Scholar 

  • Hanson HP et al (2000) The potential and promise of physics-based wildfire simulation. Environ Sci Policy 3(4):161–172

    Article  Google Scholar 

  • Haykin S (2001) Redes Neurais—Princípios e Prática, 2nd edn. Bookman, Porto Alegre

    Google Scholar 

  • IBAMA (2013) Instituto Brasileiro de Meio Ambiente e dos Recursos Naturais Renováveis (2012). Plano Operativo De Prevenção e Combate aos Incêndios Florestais do Parque Nacional da Chapada das Mesas. Disponível em http://www.ibama.gov.br/phocadownload/prevfogo/plano_operativo_parna_da_chapada_das_mesas.pdf. Acesso em 02 fev. 2013

  • Imada A (2014) A literature review: forest management with neural network and artificial intelligence. In: Neural networks and artificial intelligence. Springer International Publishing, pp 9–21

  • INMET (2012) Instituto Nacional de Meteorologia, 2012. Banco de Dados Meteorológicos para Ensino e Pesquisa—BDMEP. Disponível em http://www.inmet.gov.br/portal/index.php?r=bdmep/bdmep. Acesso em 15 set. 2012

  • INPE (2012) Instituto Nacional de Pesquisas Espaciais, 2012. Portal do Monitoramento de Queimadas e Incêndios. Disponível em http://queimadas.cptec.inpe.br. Acesso em 22 set. 2012

  • Liu B, Hsu W, Ma Y (1998) Integrating Classification and Association Rule Mining. In: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98, Plenary Presentation), New York, USA

  • Moita Neto JM, Moita GC (1998) Uma Introdução à Análise Exploratória de Dados Multivariados. Química Nova, São Paulo, SP, vol 21, n 4, pp 467–469

  • Moori RG, Marcondes RC, Avila RT (2002) Análise de Agrupamentos como Instrumento de Apoio à Melhoria da Qualidade dos Serviços aos Clientes. RAC. Revista de Administração Contemporânea, Curitiba, PR, vol 6, pp 63–82

  • Peng RD, Schoenberg FP, Woods JA (2005) A space–time conditional intensity model for evaluating a wildfire hazard index. J Am Stat Assoc 100:26–35

  • Santana RS (2008) Uma aplicação de CBIR à análise de imagens médicas de imuno-histoquímica utilizando Morfologia Matemática e espectro de padrões. Tese (Conclusão de curso em Engenharia da Computação)—Escola Politécnica de Pernambuco, Universidade de Pernambuco, Recife

  • Soares RV, Paez G (1973) Uma Nova Fórmula Para A Determinação do Grau de Perigo de Incêndios Florestais Na Regiao Centro-Paranaense. FLORESTA, Curitiba, PR, vol 4, n.3, pp 15–25

  • Souza FT (2004) Predição de Escorregamentos das Encostas do Município do Rio de Janeiro Através de Técnicas de Mineração de Dados. Tese (Doutorado em Engenharia Civil)—Universidade Federal do Rio de Janeiro, Rio de Janeiro

  • Souza FT (2014) A data-based model to locate mass movements triggered by seismic events in Sichuan, China. Environ Monit Assess 186(1):575–587. doi:10.1007/s10661-013-3400-3

    Article  Google Scholar 

  • Souza FT, Ebecken N (2012) A data based model to predict landslide induced by Rainfall in Rio de Janeiro City. Geotech Geol Eng 30:85–94

    Article  Google Scholar 

  • Sun R et al (2009) The importance of fire–atmosphere coupling and boundary-layer turbulence to wildfire spread. Int J Wildland Fire 18(1):50–60

    Article  Google Scholar 

  • Taylor SW et al (2013) Wildfire prediction to inform fire management: statistical science challenges. Stat Sci 28(4):586–615

    Article  Google Scholar 

  • Wherry RJ (1984) Contributions to correlational analysis. Academic Press, New York

    Google Scholar 

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Correspondence to Fábio Teodoro de Souza.

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de Souza, F.T., Koerner, T.C. & Chlad, R. A data-based model for predicting wildfires in Chapada das Mesas National Park in the State of Maranhão. Environ Earth Sci 74, 3603–3611 (2015). https://doi.org/10.1007/s12665-015-4421-8

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  • DOI: https://doi.org/10.1007/s12665-015-4421-8

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