Productivity of Ontario initial-attack fire crews: results of an expert-judgement elicitation study

Abstract

A structured expert-judgement elicitation technique was used to develop probability distributions for fireline production rates for Ontario's three- and four-person initial-attack crews for seven common fuel types and two distinct levels of fire intensity (i.e., low, 500 kW/m; moderate, 1500 kW/m). A total of 141 crew leaders provided 900 estimates of the minimum, maximum, and most likely (mode) time to construct 610 m (2000 ft) of fireline. This information was used to estimate parameters for beta probability distributions for each individual and scenario. Analysis of variance (ANOVA) of the beta-distribution parameters (α and β) and the three time estimates indicated that fuel type, intensity, crew size, and crew-leader experience all have a statistically significant (p < 0.05) influence on estimated crew productivity. The 28 scenario-specific and 7 aggregated distributions and expected values can be used in many operational fire-management activities (e.g., presuppression planning, initial-attack dispatching, initial-fire assessments) and incorporated into initial-attack containment models. These results also provide baseline data on crew productivity that can be used in larger strategic analyses to gauge the benefits of new fire-suppression equipment and techniques for the entire fire-management program.

Advancing effects analysis for integrated, large-scale wildfire risk assessment

“…In the meantime, however, in absence of improved information, decision-makers must look to appropriate decision support techniques to address this knowledge uncertainty. Most common is use of an expert system, based on the premise that the best judgment of experts is likely the most appropriate substitute for perfect information (e.g., Vadrevu et al 2009;González et al 2007; Hessburg et al 2007;Nadeau and Englefield 2006;Kaloudis et al 2005;Hirsch et al 1998Hirsch et al , 2004. Ongoing research within the USDA Forest Service is advancing the development of wildfire risk analysis tools that employ expert systems approaches to perform integrated effects analysis.…”

A review of challenges to determining and demonstrating efficiency of large fire management

“…That is, although the approach of Rodríguez y Silva and González-Cabán (2016) can determine the efficiency of past actions, it does not evaluate a range of other suppression strategies and tactics in terms of how they might be more or less efficient. Simply put, efforts to collect data on suppression productivity and effectiveness in operational large fire contexts and to develop research-quality reporting and information systems have not kept up with the capabilities of fire suppression modelling systems; hence, the limited ability to fully or even partially parameterise large fire optimisation models, and instead the continued reliance of many systems on assumptions, rulesets and expert judgment (Hirsch et al 2004;Petrovic and Carlson 2012;Plucinski et al 2012;Duff and Tolhurst 2015).…”

Integrated national-scale assessment of wildfire risk to human and ecological values

“…Effects analysis is made difficult by the scientific uncertainty and lack of data/information surrounding wildfire effects on non-market resources (Venn and Calkin 2009;Keane and Karau 2010). An expert systems approach was, therefore, adopted to deal with the scientific uncertainty (e.g., Vadrevu et al 2010;González et al 2007;Kaloudis et al 2005;Hirsch et al 1998Hirsch et al , 2004. Expert systems rely on the best judgment of experts as a proxy for empirical data.…”