Mid-Career Industry Fellowships - Grant ID: IM230100222
Funder
Australian Research Council
Funding Amount
$865,628.00
Summary
Large scale urban stormwater reuse: safe, clear and odourless water supply. This project aims to improve the resilience of Australian water supplies by building capacity in urban stormwater reuse. The project expects to address an industry-identified need to determine the suitability of urban lakes and wetlands for stormwater harvesting and develop chemical-sensory monitoring techniques to assess the quality of harvested water. Expected outcomes include the establishment of satellite-based remot ....Large scale urban stormwater reuse: safe, clear and odourless water supply. This project aims to improve the resilience of Australian water supplies by building capacity in urban stormwater reuse. The project expects to address an industry-identified need to determine the suitability of urban lakes and wetlands for stormwater harvesting and develop chemical-sensory monitoring techniques to assess the quality of harvested water. Expected outcomes include the establishment of satellite-based remote sensing as a key technology for stormwater applications and the widespread use of improved techniques for monitoring odorants by the water industry. This should provide significant benefits by informing adaptive planning and infrastructure readiness at water utilities and guiding Australian policy on stormwater reuse.Read moreRead less
Intelligent pattern recognition of water end uses enabling recommendations. This project aims to develop a hybrid machine learning method for autonomously disaggregating high- and low-resolution water flow data received from smart meters into discrete end-use events, and a customised recommender system for efficient resource demand management. Project novelty and significance relates to this coupling and autonomous disaggregation of datasets from advanced sensors, enabling more efficient utility ....Intelligent pattern recognition of water end uses enabling recommendations. This project aims to develop a hybrid machine learning method for autonomously disaggregating high- and low-resolution water flow data received from smart meters into discrete end-use events, and a customised recommender system for efficient resource demand management. Project novelty and significance relates to this coupling and autonomous disaggregation of datasets from advanced sensors, enabling more efficient utility services delivery and lower customer utility bills. Project benefits include enabling utilities to better manage and plan resources in the information age, while empowering customers with real-time water end-use data and behaviour changing consumption recommendations.Read moreRead less
Rare Event Simulation: Protecting vital infrastructure from flood extremes. This research aims to develop Rare Event Simulation to quantify the future risk of very rare to extreme floods. Expected outcomes include a framework for the design and maintenance of critical Civil Engineering infrastructure such as dams, extrapolation of extreme storm events beyond the observed record, and an assessment of change in rare flood risk across Australia. The significance of this world-first research lies in ....Rare Event Simulation: Protecting vital infrastructure from flood extremes. This research aims to develop Rare Event Simulation to quantify the future risk of very rare to extreme floods. Expected outcomes include a framework for the design and maintenance of critical Civil Engineering infrastructure such as dams, extrapolation of extreme storm events beyond the observed record, and an assessment of change in rare flood risk across Australia. The significance of this world-first research lies in adapting rare event simulation techniques that have only been applied to computer system failure before, to water engineering design. With Australian riverine flooding projected to cause $170 billion in losses by 2050, the benefit of this proposal in reducing future infrastructure damage costs and liability is overwhelming.Read moreRead less