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A Managerial Approach to Estimating Wildland Fire Use Workload
Author(s) -
Douglas B. Rideout,
Robin M. Reich,
Pamela S. Ziesler
Publication year - 2009
Publication title -
western journal of applied forestry
Language(s) - English
Resource type - Journals
eISSN - 1938-3770
pISSN - 0885-6095
DOI - 10.1093/wjaf/24.1.42
Subject(s) - workload , duration (music) , computer science , regression analysis , multilevel model , fire protection , environmental resource management , operations research , operations management , environmental science , engineering , civil engineering , machine learning , art , literature , operating system
Increasing recognition of the role of fire in natural ecosystems has increased the use of wildland fire as a management tool. Although wildland fire use (WFU) has been practiced for decades, it is emerging as an organized program. As such, the analytics of WFU, from a management sciences perspective, are largely undeveloped at a time when there is a growing need to inform program managers and support modeling efforts aimed at more cost-effective fire management programs. Conventional initial attack modeling relates workload to fire perimeter; but, currently, there is no analog for WFU events. This article takes the first step in providing a companion estimation of WFU workload. WFU workload is estimated as a function of basic information on fire size and duration by using a regression tree analysis. Workload scores for wildland fire use management and monitoring were estimated separately. These estimates explained about 68 and 60% of the variation in the management and monitoring scores, respectively. The estimated scores were sensitive to fire size, although duration played an important role, especially on larger events. For example, fires in the same size class often received higher workload scores with increasing duration. Workload estimates from the management regression tree were then associated with average resource usage. The form of the association indicated that as workload estimates increased, average resource usage increased exponentially. Estimating workload scores as a function of size and duration, which are readily available from simulation models, and then associating the scores with resource usage supports efforts to address WFU effort and cost management.

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