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Grant Award View - GA217282
Physics-aware machine learning for data-driven fire risk prediction
GA ID:
GA217282
Agency:
Australian Research Council
Approval Date:
27-Jan-2022
Publish Date:
8-Feb-2022
Category:
Science, Technology, Engineering and Mathematics (STEM) Research
Grant Term:
27-Jan-2022 to 31-Oct-2025
Original: 27-Jan-2022 to 31-Dec-2024
Value (AUD):
$485,000.00
(GST inclusive where applicable)
Variations:
- GA217282-V1 - Variation to Grant (4-Jul-2023 )
One-off/Ad hoc:
No
Aggregate Grant Award:
No
PBS Program Name:
ARC 21/22 Discovery
Grant Program:
Discovery Projects
Grant Activity:
Physics-aware machine learning for data-driven fire risk prediction
Purpose:
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.
GO ID:
GO Title:
Discovery Projects for funding commencing in 2022
Internal Reference ID:
DP22 Round 1
Selection Process:
Targeted or Restricted Competitive
Confidentiality - Contract:
No
Confidentiality - Outputs:
No
Grant Recipient Details
Recipient Name:
Western Sydney University
Recipient ABN:
53 014 069 881
Grant Recipient Location
Suburb:
RICHMOND
Town/City:
RICHMOND
Postcode:
2753
State/Territory:
NSW
Country:
AUSTRALIA
Grant Delivery Location
State/Territory:
NSW
Postcode:
2753
Country:
AUSTRALIA