Predicting daily initial attack aircraft targets in British Columbia
S. W. Taylor A * and K. Nadeem B
+ Author Affiliations
- Author Affiliations
A Pacific Forestry Centre, Natural Resources Canada, Victoria, BC, Canada.
B Department of Mathematics and Statistics, University of Guelph, Guelph, ON, Canada.
* Correspondence to: steve.taylor@canada.ca
International Journal of Wildland Fire - https://doi.org/10.1071/WF21090
Submitted: 24 June 2021 Accepted: 19 January 2022 Published online: 4 April 2022
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. This is an open access article distributed under the Creative Commons Attribution- NonCommercial 4.0 International License (CC BY-NC)
Abstract
We developed spatially explicit models of the daily probability of aircraft use in initial attack (IA) on a fire (hereafter the conditional models), and estimates of the unconditional probability of daily aircraft IA targets to support preparedness planning in the province of British Columbia, Canada, using a grid cell × day lasso-logistic framework. Novel aspects of our work include: (1) inclusion of an historical aircraft baseline covariate to account for missing or poorly estimated factors in our models; and of 2 day lead weather and Forest Fire Weather Index (FWI) covariates as proxies for fire potential trend, and (2) linking the conditional models of aircraft use in IA to models of daily fire occurrence to estimate the daily number of aircraft IA targets. The baseline risk of using an aircraft, population and road density were highly influential spatial covariates in both aircraft conditional models. The probability of sustained flaming, temperature, and FWI lead, and Sheltered Duff Moisture Code, temperature, and the Showalter Index were the three most influential meteorological variables in the conditional airtanker and helicopter IA models, respectively. We demonstrate the application of the models to portray the distribution of the expected number of daily aircraft IA targets.
Keywords: aerial suppression, fire danger, fire management, forecasting, initial attack, preparedness planning, resource demand prediction, statistical learning.
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