Special Research Initiatives - Grant ID: SR0354575
Funder
Australian Research Council
Funding Amount
$30,000.00
Summary
Earth and Ocean Informatics and Technology Network (EON-ITnet). Sustainable resource exploration and mining onshore, as well as marine planning, exploration, and defence depend on effective cross-disciplinary investigation, sharing of expertise and technologies for integration and computational analysis of multidimensional data spaces. EON-ITNET will cross-fertilise the use of artificial intelligence, advanced computing and smart information sharing for management, analysis, visualisation and me ....Earth and Ocean Informatics and Technology Network (EON-ITnet). Sustainable resource exploration and mining onshore, as well as marine planning, exploration, and defence depend on effective cross-disciplinary investigation, sharing of expertise and technologies for integration and computational analysis of multidimensional data spaces. EON-ITNET will cross-fertilise the use of artificial intelligence, advanced computing and smart information sharing for management, analysis, visualisation and metadata modelling between these traditionally separate research groups, with the outcome of improving research efficiency and lowering costs. EON-ITNET will form an alliance with the Caltech-based GeoFramework, which is advancing a novel object-oriented data analysis environment, binding community software for Earth visualisation and simulation to 4D data bases.Read moreRead less
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 ....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. Read moreRead less