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:	Refereed Article
Keywords:	wildfire risk management, data-driven approach, risk metric, wildfire simulations, spark, risk reduction, bushfire, software system, algorithm, data
Research Division:	Information and Computing Sciences
Research Group:	Distributed computing and systems software
Research Field:	Distributed computing and systems software not elsewhere classified
Objective Division:	Information and Communication Services
Objective Group:	Information systems, technologies and services
Objective Field:	Computer systems
UTAS Author:	Garg, S (Dr Saurabh Garg)
ID Code:	148548
Year Published:	2022
Web of Science® Times Cited:	2
Deposited By:	Information and Communication Technology
Deposited On:	2022-01-21
Last Modified:	2023-01-06
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