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Type: Journal Article
Author(s): Christopher D. O'Connor; Jessica R. Haas; Benjamin M. Gannon; Christopher J. Dunn; Matthew P. Thompson; David E. Calkin
Publication Date: 2022

Wildfire management has long been driven by a cadre of experienced professionals that rely heavily on their personal experience and judgement to determine the best available holding features to contain actively growing wildfires. In the western United States, the number of large high-severity wildfires has increased dramatically over the past decade, pushing the limits of the fire management system, and highlighting the need for more strategic, data-driven approaches to incident response. Here, we present work that builds from an original methods paper published in 2017 that outlines a gradient boosting approach to predict potential fire control locations.

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Citation: O'Connor, Christopher D.; Haas, Jessica R.; Gannon, Benjamin M.; Dunn, Christopher J.; Thompson, Matthew P.; Calkin, David E. 2022. Modelling potential control locations: development and adoption of data-driven analytics to support strategic and tactical wildfire containment decisions. Environmental Sciences Proceedings 17(1):73.

Cataloging Information

  • analytics
  • decision support
  • fire management systems
  • probability of success
  • wildfire management
Record Last Modified:
Record Maintained By: FRAMES Staff (
FRAMES Record Number: 67492