Skip to Main Content
An empirical machine learning method for predicting potential fire control locations for pre-fire planning and operational fire managementAuthor(s): Christopher D. O'Connor; David E. Calkin; Matthew P. Thompson
Source: International Journal of Wildland Fire. 26: 587-597.
Publication Series: Scientific Journal (JRNL)
Station: Rocky Mountain Research Station
View PDF (1.0 MB)
DescriptionDuring active fire incidents, decisions regarding where and how to safely and effectively deploy resources to meet management objectives are often made under rapidly evolving conditions, with limited time to assess management strategies or for development of backup plans if initial efforts prove unsuccessful. Under all but the most extreme fire weather conditions, topography and fuels are significant factors affecting potential fire spread and burn severity. We leverage these relationships to quantify the effects of topography, fuel characteristics, road networks and fire suppression effort on the perimeter locations of 238 large fires, and develop a predictive model of potential fire control locations spanning a range of fuel types, topographic features and natural and anthropogenic barriers to fire spread, on a 34 000 km2 landscape in southern Idaho and northern Nevada. The boosted logistic regression model correctly classified final fire perimeter locations on an independent dataset with 69% accuracy without consideration of weather conditions on individual fires. The resulting fire control probability surface has potential for reducing unnecessary exposure for fire responders, coordinating pre-fire planning for operational fire response, and as a network of locations to incorporate into spatial fire planning to better align fire operations with land management objectives.
- You may send email to email@example.com to request a hard copy of this publication.
- (Please specify exactly which publication you are requesting and your mailing address.)
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationO'Connor, Christopher D.; Calkin, David E.; Thompson, Matthew P. 2017. An empirical machine learning method for predicting potential fire control locations for pre-fire planning and operational fire management. International Journal of Wildland Fire. 26: 587-597.
Keywordsboosted regression, fire responder safety, MaxEnt, operational decision support, pre-fire planning, risk analysis, spatial analysis
- Estimating wildland fire rate of spread in a spatially nonuniform environment
- Using a stochastic model and cross-scale analysis to evaluate controls on historical low-severity fire regimes
- Synthesis of knowledge of extreme fire behavior: volume I for fire managers
XML: View XML