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A probability-based risk metric for operational wildfire risk management

KC, U, Hilton, J, Garg, S ORCID: 0000-0003-3510-2464 and Aryal, J 2022 , 'A probability-based risk metric for operational wildfire risk management' , Environmental Modelling and Software, vol. 148 , doi: 10.1016/j.envsoft.2021.105286.

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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 a convenient and accurate method of estimating 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.

Item Type: Article
Authors/Creators:KC, U and Hilton, J and Garg, S and Aryal, J
Keywords: wildfire risk management, data-driven approach, risk metric, wildfire simulations, spark, risk reduction, bushfire, software system, algorithm, data
Journal or Publication Title: Environmental Modelling and Software
Publisher: Elsevier Sci Ltd
ISSN: 1364-8152
DOI / ID Number: 10.1016/j.envsoft.2021.105286
Copyright Information:

Copyright 2021 Elsevier Ltd.

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