  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) in the environmental sciences. Here, we
present a scoping review of ML in wildfire science and management. Our
objective is to improve awareness of ML among wildfire scientists and managers,
as well as illustrate the challenging range of problems in wildfire science
available to data scientists. We first present an overview of popular ML
approaches used in wildfire science to date, and then review their use in
wildfire science within six problem domains: 1) fuels characterization, fire
detection, and mapping; 2) fire weather and climate change; 3) fire occurrence,
susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6)
fire management. We also discuss the advantages and limitations of various ML
approaches and identify opportunities for future advances in wildfire science
and management within a data science context. We identified 298 relevant
publications, where the most frequently used ML methods included random
forests, MaxEnt, artificial neural networks, decision trees, support vector
machines, and genetic algorithms. There exists opportunities to apply more
current ML methods (e.g., deep learning and agent based learning) in wildfire
science. However, despite the ability of ML models to learn on their own,
expertise in wildfire science is necessary to ensure realistic modelling of
fire processes across multiple scales, while the complexity of some ML methods
requires sophisticated knowledge for their application. Finally, we stress that
the wildfire research and management community plays an active role in
providing relevant, high quality data for use by practitioners of ML methods.
