Ujjwal KC, J. E. Hilton, Saurabh Garg, Jagannath Aryal
Year Published:

Cataloging Information

Fire Behavior

NRFSN number: 24176
FRAMES RCS Number: 64985
Record updated: February 2, 2022

With the advancement in scientific understanding and computing technologies, fire practitioners have started relying on operational fire simulation tools to make better-informed decisions during wildfire emergencies. This increased use has created an opportunity to employ an emerging data-driven approach for wildfire risk estimation as an alternative to running computationally expensive simulations. In an investigative attempt, we propose a probability-based risk metric that gives a series of probability values for fires starting at any possible start location under any given weather condition falling into different categories. We investigate the validity of the proposed approach by applying it to use cases in Tasmania, Australia. Results show that the proposed risk metric can be an convenient and accurate method of assessing imminent risk during operational wildfire management. Additionally, the knowledge-base of our proposed risk metric based on a data-driven approach can be constantly updated to improve its accuracy.


KC, Ujjwal; Hilton, James E.; Garg, Saurabh; Aryal, Jagannath. 2022. A probability-based risk metric for operational wildfire risk management. Environmental Modelling & Software 148:105286.

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