Developing novel machine-learning-based fire weather indices

Assaf Shmuel, Eyal Heifetz

Research output: Contribution to journalArticlepeer-review


Accurate wildfire risk estimation is an essential yet challenging task. As the frequency of extreme fire weather and wildfires is on the rise, forest managers and firefighters require accurate wildfire risk estimations to successfully implement forest management and firefighting strategies. Wildfire risk depends on non-linear interactions between multiple factors; therefore, the performance of linear models in its estimation is limited. To date, several traditional fire weather indices (FWIs) have been commonly used by weather services, such as the Canadian FWI.@Traditional FWIs are primarily based on empirical and statistical analyses. In this paper, we propose a novel FWI that was developed using machine learning—the machine learning based fire weather index (MLFWI). We present the performance of the MLFWI and compare it with various traditional FWIs. We find that the MLFWI significantly outperforms traditional indices in predicting wildfire occurrence, achieving an area under the curve score of 0.99 compared to 0.62-0.80. We recommend applying the MLFWI in wildfire warning systems.

Original languageEnglish
Article number015029
JournalMachine Learning: Science and Technology
Issue number1
StatePublished - 1 Mar 2023


  • fire weather indices
  • forest management
  • machine learning
  • wildfire risk

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Human-Computer Interaction


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