Improved management of marine habitats by learning from historical change. This project aims to greatly improve the cost-effectiveness of actions to protect and restore shallow subtidal marine habitats by quantifying the severity and distribution of recent human impacts. Environmental change will be quantified as the difference between contemporary and historical assemblages encompassing thousands of invertebrate species, and by reading historical chronicles coded by mollusc shells layered in se ....Improved management of marine habitats by learning from historical change. This project aims to greatly improve the cost-effectiveness of actions to protect and restore shallow subtidal marine habitats by quantifying the severity and distribution of recent human impacts. Environmental change will be quantified as the difference between contemporary and historical assemblages encompassing thousands of invertebrate species, and by reading historical chronicles coded by mollusc shells layered in sediments. The roles of different stressors (warming, dredging, eutrophication, introduced species, sediment runoff) will be distinguished. Expected outcomes include continental-scale understanding of factors that facilitate ecosystem decline and recovery, and of sites and species traits most affected by ongoing threats.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220100265
Funder
Australian Research Council
Funding Amount
$417,000.00
Summary
A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and ....A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and interpretability in decision making. Expected outcomes will benefit national cybersecurity by improving our understanding of vulnerabilities and threats involving decision actions, and by ensuring that human feedback and evaluations can help prevent catastrophic events in explorations of dynamic and complex environments.Read moreRead less
Portable and field-deployable analytical platforms for water monitoring. This project sets out to tackle one of the costliest and most challenging environmental problems, namely, nutrient pollution in water systems. At present, nutrient pollutant monitoring is predominantly carried out using an antiquated manual approach with numerous shortcomings, inadequate to achieve truly effective water quality management. The in-situ analyser developed and deployed within this project will provide continuo ....Portable and field-deployable analytical platforms for water monitoring. This project sets out to tackle one of the costliest and most challenging environmental problems, namely, nutrient pollution in water systems. At present, nutrient pollutant monitoring is predominantly carried out using an antiquated manual approach with numerous shortcomings, inadequate to achieve truly effective water quality management. The in-situ analyser developed and deployed within this project will provide continuous real-time observations and will allow users to remotely monitor water quality; alerting them to pollutant levels, enabling immediate action to be taken to prevent environmental damage. The system is low-cost, facilitating mass adoption, yet delivers an analytical performance comparable to leading laboratory analysers. Read moreRead less
High resolution health assessment of Antarctic plants as climate changes. Declines in terrestrial ecosystem health as a result of a drying climate have been observed in some areas of East Antarctica. This project aims to determine if such changes are widespread. Since mosses, the dominant plants of Antarctica, preserve a record of past climate down their shoots they can be used as surrogates to study how both ecosystems and climate are changing at remote polar sites. Outcomes will include improv ....High resolution health assessment of Antarctic plants as climate changes. Declines in terrestrial ecosystem health as a result of a drying climate have been observed in some areas of East Antarctica. This project aims to determine if such changes are widespread. Since mosses, the dominant plants of Antarctica, preserve a record of past climate down their shoots they can be used as surrogates to study how both ecosystems and climate are changing at remote polar sites. Outcomes will include improved climate data for Antarctica, enabling more robust analysis of regional climate change, and development of ultrahigh-resolution techniques capable of non-destructively monitoring Antarctic ecosystem health. This research will advance ecosystem science and inform best practice in management of Antarctic biodiversity.Read moreRead less
Industrial Transformation Research Hubs - Grant ID: IH180100002
Funder
Australian Research Council
Funding Amount
$5,000,000.00
Summary
ARC Research Hub for Driving Farming Productivity and Disease Prevention. The ARC Research Hub for Driving Farming Productivity and Disease Prevention aims to increase farm production and disease prevention through advancing and transferring new artificial intelligence technologies into industrial deployment. The Hub will combine machine vision, machine learning, software quality control, engineering, biology, and farming industries to develop technologies to build more intelligent systems. Thes ....ARC Research Hub for Driving Farming Productivity and Disease Prevention. The ARC Research Hub for Driving Farming Productivity and Disease Prevention aims to increase farm production and disease prevention through advancing and transferring new artificial intelligence technologies into industrial deployment. The Hub will combine machine vision, machine learning, software quality control, engineering, biology, and farming industries to develop technologies to build more intelligent systems. These dynamic systems will help determine what goal to achieve and the most efficient plan to achieve it. This Hub is expected to contribute to higher farming efficiency, lower production costs and fewer disease risks, giving the Australian industry new business opportunities and an international competitive advantage.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