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Publication Detail
Applications of spatio-temporal data mining and knowledge discovery (STDMKD) for forest fire prevention
  • Publication Type:
    Conference
  • Authors:
    Cheng T, Wang J
  • Publication date:
    01/01/2006
  • Published proceedings:
    International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
  • Volume:
    36
  • Status:
    Published
  • Print ISSN:
    1682-1750
Abstract
Forests play an important role for sustaining the natural environment of human living. Forest fires not only destroy natural environment and ecological equivalence, but also threaten security of life and wealth to people. This paper presents applications of Spatio-temporal Data Mining and Knowledge Discovering (STDMKD) for forest fire prevention. The special attention of the research is paid to the spatio-temporal forecasting of forest fires because of the importance of prediction for the fire prevention. It is also due to the fact that most existing spatio-temporal forecasting methods cannot handle the dynamic development of forest fires over space. An improved spatio-temporal integrated forecasting framework - ISTIFF is proposed. The method and algorithm of ISTIFF are presented, which are illustrated by a case study of forest fire area predication in Canada. Comparative analysis of ISTIFF with other methods is implemented, which shows its high accuracy in short-term prediction. Based upon the forecasting result, more intelligent strategies of fire prevention and extinguishments can be delivered to decision makers in fireproofing.
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