A review of machine learning applications in wildfire science and management
Canadian Forest Service · Natural Resources Canada · +2 more institutions
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
- FWCI
- 28.45
- Percentile
- 100%
- References
- 321
Authors
6- PJPiyush JainCorresponding
Canadian Forest Service, Natural Resources Canada, University of Alberta
- SCSean C.P. Coogan
University of Alberta
- SGSriram Ganapathi Subramanian
University of Waterloo
- MCMark Crowley
University of Waterloo
- STSteve Taylor
Canadian Forest Service, Natural Resources Canada
Topics & keywords
- Artificial neural network
- Field (mathematics)
- Deep learning
- Random forest
- Climate change
- Decision tree
- Range (aeronautics)
- Expert elicitation