• DocumentCode
    2248097
  • Title

    Artificial intelligence for forest fire prediction

  • Author

    Sakr, George E. ; Elhajj, Imad H. ; Mitri, George ; Wejinya, Uchechukwu C.

  • Author_Institution
    American Univ. of Beirut, Beirut, Lebanon
  • fYear
    2010
  • fDate
    6-9 July 2010
  • Firstpage
    1311
  • Lastpage
    1316
  • Abstract
    Forest fire prediction constitutes a significant component of forest fire management. It plays a major role in resource allocation, mitigation and recovery efforts. This paper presents a description and analysis of forest fire prediction methods based on artificial intelligence. A novel forest fire risk prediction algorithm, based on support vector machines, is presented. The algorithm depends on previous weather conditions in order to predict the fire hazard level of a day. The implementation of the algorithm using data from Lebanon demonstrated its ability to accurately predict the hazard of fire occurrence.
  • Keywords
    artificial intelligence; ecology; fires; forestry; support vector machines; artificial intelligence; fire hazard level; forest fire management; forest fire risk prediction algorithm; support vector machine; weather condition; Data mining; Equations; Fires; Prediction algorithms; Support vector machines; Weather forecasting; Forest Fire Prediction; Machine Learning; SVM;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Intelligent Mechatronics (AIM), 2010 IEEE/ASME International Conference on
  • Conference_Location
    Montreal, ON
  • Print_ISBN
    978-1-4244-8031-9
  • Type

    conf

  • DOI
    10.1109/AIM.2010.5695809
  • Filename
    5695809