Website
http://dataportal.arc.gov.au/NCGP/API/grants/DP220100795
Funding Status
Active
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Funded Activity Summary
Physics-aware machine learning for data-driven fire risk prediction. The 2019/20 Australian fire season was unprecedented in its extent, impact, and the response of fire agencies. In this project, we aim to answer the question: was the scale of these fires driven by known drivers of fire (drought, weather, fuels and ignitions), or were fundamentally new undescribed processes and phenomena involved? We will accomplish this by developing an innovative, physics-aware machine learning model of fire risk and spread, trained and validated on a two-decade satellite fire record. The predictive ability of the model will be tested on the 2019/20 fire season to determine if novel drivers of fire can be identified, and the model itself will be operationalised into a novel short-to-mid term fire risk prediction tool.Funded Activity Details
Start Date: 2022
End Date: 10-2025
Funding Scheme: Discovery Projects
Funding Amount: $485,000.00
Funder: Australian Research Council
Research Topics
ANZSRC Field of Research (FoR)
Simulation and Modelling | Physical Geography and Environmental Geoscience not elsewhere classified | Pattern Recognition and Data Mining | Physical Geography and Environmental Geoscience
ANZSRC Socio-Economic Objective (SEO)
Expanding Knowledge in the Environmental Sciences | Expanding Knowledge in the Information and Computing Sciences | Natural Hazards in Forest and Woodlands Environments |
