• DocumentCode
    2405163
  • Title

    An evolved fuzzy logic system for fire size prediction

  • Author

    Fowler, Alan ; Teredesai, Ankur M. ; De Cock, Martine

  • Author_Institution
    Inst. of Technol., Univ. of Washington, Tacoma, WA, USA
  • fYear
    2009
  • fDate
    14-17 June 2009
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    The accurate prediction of forest fire size is important in order to issue adequate and timely warnings and to allocate fire-fighting assets efficiently and effectively. A forest fire data set collected in Portugal has recently become available as a benchmark for experimental validation of data mining techniques to tackle this problem. In this paper, we explore the suitability of a fuzzy rule based system to solve the forest fire size prediction problem. Since we have no specific domain expertise, we evolve the fuzzy rules as well as the membership functions automatically using genetic algorithms. The results clearly demonstrate the utility of the evolved fuzzy rule based system.
  • Keywords
    data mining; fires; forestry; fuzzy logic; geography; knowledge based systems; data mining techniques; fire size prediction; fire-fighting assets; forest fire size; fuzzy logic system; fuzzy rule based system; genetic algorithms; membership functions; Data mining; Fires; Fuzzy logic; Fuzzy systems; Genetic algorithms; Humidity; Knowledge based systems; Meteorology; Temperature; Weather forecasting; co-evolution; forest fire; fuzzy rule base; genetic algorithm; meteorological data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Information Processing Society, 2009. NAFIPS 2009. Annual Meeting of the North American
  • Conference_Location
    Cincinnati, OH
  • Print_ISBN
    978-1-4244-4575-2
  • Electronic_ISBN
    978-1-4244-4577-6
  • Type

    conf

  • DOI
    10.1109/NAFIPS.2009.5156419
  • Filename
    5156419