Title

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

Publication Name

Environmental Modelling and Software

Abstract

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.

Open Access Status

This publication is not available as open access

Volume

144

Article Number

105127

Funding Sponsor

National Aeronautics and Space Administration

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Link to publisher version (DOI)

http://dx.doi.org/10.1016/j.envsoft.2021.105127