University of Wollongong
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Derivation of a Bayesian fire spread model using large-scale wildfire observations

journal contribution
posted on 2024-11-17, 16:12 authored by Michael A Storey, Michael Bedward, Owen F Price, Ross A Bradstock, Jason J Sharples
Models that predict wildfire rate of spread (ROS) play an important role in decision-making during firefighting operations, including fire crew placement and timing of community evacuations. Here, we use a large set of remotely sensed wildfire observations, and explanatory data (focusing on weather), to demonstrate a Bayesian probabilistic ROS modelling approach. Our approach has two major advantages: (1) Using actual wildfire observations, instead of controlled fire observations, makes models developed well-suited to wildfire prediction; (2) Bayesian modelling accounts for the complex nature of wildfire spread by explicitly considering uncertainty in the data to produce probabilistic ROS predictions. We show that highly informative probabilistic predictions can be made from a simple Bayesian model containing wind speed, relative humidity and soil moisture. We provide current operational context to our work by calculating predictions from widely used deterministic ROS models in Australia.

History

Journal title

Environmental Modelling and Software

Volume

144

Language

English

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