Description: <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>FROM RESEARCH ABSTRACT:</SPAN></P><P><SPAN>During 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.</SPAN></P></DIV></DIV></DIV>
Service Item Id: eff01002067241048a593963e92926b0
Copyright Text: Provided to WA DNR by Chris Dunn (OSU), stewarded by Kirk Davis (WA DNR - Wildfire Division)
CITATION:
O'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.
Description: <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>FROM ORIGINAL DATASET:</SPAN></P><P><SPAN>In its original formulation for use in Spain, SDI included aerial resource use, however for development and application in the United States we removed the aerial resource component due to a lack of consistent data. We note this distinction of “terrestrial only” calculations with the inclusion of “t” in the acronym. SDIt factors in topography, fuels, expected fire behavior under severe fire weather conditions, firefighter line production rates in various fuel types, and accessibility (distance from roads/trails) to assess relative suppression effort. For this dataset severe fire behavior is modeled with 15 mph up-slope winds and fully cured fuels. This SDI dataset has a continuous value distribution from 0 - 1, anything above 1 is considered HIGH.</SPAN></P></DIV></DIV></DIV>
Service Item Id: eff01002067241048a593963e92926b0
Copyright Text: Provided to DNR by Chris Dunn (OSU), stewarded by Kirk Davis (WA DNR - Wildfire Division)
ORIGINAL WORK BY:
Rodríguez y Silva Francisco, O’Connor Christopher D., Thompson Matthew P., Molina Martínez Juan Ramón, Calkin David E. (2020) Modelling suppression difficulty: current and future applications. International Journal of Wildland Fire 29, 739-751.