Advertisement

Environmental Earth Sciences

, Volume 74, Issue 4, pp 3603–3611 | Cite as

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

  • Fábio Teodoro de SouzaEmail author
  • Tarcyzio Cezar Koerner
  • Rafael Chlad
Original Article

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.

Keywords

Wildfires Meteorological dataset Data mining Artificial neural networks Classification rules 

References

  1. 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 1–8Google Scholar
  2. 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 79–93Google Scholar
  3. Beckage B, Platt WJ (2003) Predicting severe wildfire years in the Florida Everglades. Front Ecol Environ 1(5):235–239CrossRefGoogle Scholar
  4. 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 CrossRefGoogle Scholar
  5. 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
  6. 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 PauloGoogle Scholar
  7. Goslar A (2006) Ground vegetation biomass detection for fire prediction from remote sensing data in the lowveld region (Doctoral dissertation, University of Johannesburg)Google Scholar
  8. Han J, Kamber H (2001) Data mining—concepts and techniques. Morgan Kaufmann Publishers, San FranciscoGoogle Scholar
  9. Hanson HP et al (2000) The potential and promise of physics-based wildfire simulation. Environ Sci Policy 3(4):161–172CrossRefGoogle Scholar
  10. Haykin S (2001) Redes Neurais—Princípios e Prática, 2nd edn. Bookman, Porto AlegreGoogle Scholar
  11. 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
  12. Imada A (2014) A literature review: forest management with neural network and artificial intelligence. In: Neural networks and artificial intelligence. Springer International Publishing, pp 9–21Google Scholar
  13. 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
  14. 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
  15. 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, USAGoogle Scholar
  16. Moita Neto JM, Moita GC (1998) Uma Introdução à Análise Exploratória de Dados Multivariados. Química Nova, São Paulo, SP, vol 21, n 4, pp 467–469Google Scholar
  17. 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 63–82Google Scholar
  18. Peng RD, Schoenberg FP, Woods JA (2005) A space–time conditional intensity model for evaluating a wildfire hazard index. J Am Stat Assoc 100:26–35Google Scholar
  19. 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, RecifeGoogle Scholar
  20. 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 15–25Google Scholar
  21. 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 JaneiroGoogle Scholar
  22. 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 CrossRefGoogle Scholar
  23. Souza FT, Ebecken N (2012) A data based model to predict landslide induced by Rainfall in Rio de Janeiro City. Geotech Geol Eng 30:85–94CrossRefGoogle Scholar
  24. Sun R et al (2009) The importance of fire–atmosphere coupling and boundary-layer turbulence to wildfire spread. Int J Wildland Fire 18(1):50–60CrossRefGoogle Scholar
  25. Taylor SW et al (2013) Wildfire prediction to inform fire management: statistical science challenges. Stat Sci 28(4):586–615CrossRefGoogle Scholar
  26. Wherry RJ (1984) Contributions to correlational analysis. Academic Press, New YorkGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Fábio Teodoro de Souza
    • 1
    Email author
  • Tarcyzio Cezar Koerner
    • 2
  • Rafael Chlad
    • 2
  1. 1.Postgraduate Program in Urban Management (PPGTU) - School of Architecture and Design, Course of Civil Engineering - Polytechnic SchoolPontifical Catholic University of Paraná (PUCPR)CuritibaBrazil
  2. 2.Civil Engineering DepartmentFederal University of Paraná (UFPR)CuritibaBrazil

Personalised recommendations