Smart Irrigation: integrating UAV soil moisture maps & variable rate sprays. This project will develop a state-of-the-art precision irrigation system for optimising water use and crop yield. Specifically, a novel UAV soil moisture mapping system based on passive microwave satellite remote sensing technology at L-band will be developed for near-surface soil moisture mapping at accuracies and spatial scales currently not attainable. These soil moisture maps will then be merged with irrigation wate ....Smart Irrigation: integrating UAV soil moisture maps & variable rate sprays. This project will develop a state-of-the-art precision irrigation system for optimising water use and crop yield. Specifically, a novel UAV soil moisture mapping system based on passive microwave satellite remote sensing technology at L-band will be developed for near-surface soil moisture mapping at accuracies and spatial scales currently not attainable. These soil moisture maps will then be merged with irrigation water delivery models to calibrate for spatial variation in soil properties and/or correct errors in spatial variation of rainfall and evapotranspiration inputs. Ultimately the water balance predictions will be used for implementation of variable rate irrigation control at scales hitherto unattainable.Read moreRead less
Three-dimensional Bayesian Modelling of Geological and Geophysical data. The project aims to develop technologies enabling rapid informed decision-making related to the management of natural resources, including critical metals, copper and water. This new technology will support a greener future, securing our energy future, our access to clean water and reduce the mining footprint. Expected outcomes include an enhanced capability in interoperable, integrated three-dimensional geological and geop ....Three-dimensional Bayesian Modelling of Geological and Geophysical data. The project aims to develop technologies enabling rapid informed decision-making related to the management of natural resources, including critical metals, copper and water. This new technology will support a greener future, securing our energy future, our access to clean water and reduce the mining footprint. Expected outcomes include an enhanced capability in interoperable, integrated three-dimensional geological and geophysical modelling in order to predictively characterise sub-surface geology. The outcome will be an open-source forecasting dashboard enabling decision making while considering underlying risk related to resource extractions and management with significant benefits to the Australian society (lower emissions, clean water).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