A probability-based risk metric for operational wildfire risk management. (February 2022) - KC, Ujjwal; Hilton, James; Garg, Saurabh; Aryal, Jagannath
Abstract: 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. Highlights: We investigated the applicability of a probability-based risk metric for rapid wildfire risk estimation. We developed a risk-metric based on wildfire simulations in operational wildfire management tool - Spark. We achieved a high accuracy with the proposed risk metric while assessing the wildfire risks in Tasmania, Australia.