articleEnvironmental ReviewsJul 28, 2020GREEN OA

A review of machine learning applications in wildfire science and management

PJPiyush JainSCSean C.P. CooganSGSriram Ganapathi SubramanianMCMark CrowleySTSteve Taylor

Canadian Forest Service · Natural Resources Canada · +2 more institutions

Indexed inarxivcrossref

Abstract

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then, the field has rapidly progressed congruently with the wide adoption of machine learning (ML) methods in the environmental sciences. Here, we present a scoping review of ML applications in wildfire science and management. Our overall objective is to improve awareness of ML methods among wildfire researchers and managers, as well as illustrate the diverse and challenging range of problems in wildfire science available to ML data scientists. To that end, we first present an overview of popular ML approaches used in wildfire science to date…

Citation impact

681
total citations
FWCI
28.45
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100%
References
321
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Authors

6
  • PJ
    Piyush JainCorresponding

    Canadian Forest Service, Natural Resources Canada, University of Alberta

  • SC
    Sean C.P. Coogan

    University of Alberta

  • SG
    Sriram Ganapathi Subramanian

    University of Waterloo

  • MC
    Mark Crowley

    University of Waterloo

  • ST
    Steve Taylor

    Canadian Forest Service, Natural Resources Canada

Topics & keywords

Keywords
  • Artificial neural network
  • Field (mathematics)
  • Deep learning
  • Random forest
  • Climate change
  • Decision tree
  • Range (aeronautics)
  • Expert elicitation
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