Data-driven water quality treatment management decision support system. Data-driven water quality treatment management decision support system. This project aims to develop a robust decision support system to predict manganese and the character and concentration of dissolved organic matter in drinking water reservoirs, using intelligent algorithms and data collected through remote autonomous instrumentation. These predicted water quality parameters could be used as model input variables to provi ....Data-driven water quality treatment management decision support system. Data-driven water quality treatment management decision support system. This project aims to develop a robust decision support system to predict manganese and the character and concentration of dissolved organic matter in drinking water reservoirs, using intelligent algorithms and data collected through remote autonomous instrumentation. These predicted water quality parameters could be used as model input variables to provide real-time decisions for plant operators on the required treatment regime for incoming raw water, and advise them on the optimal reservoir offtake depth. This will potentially minimise treatment costs and health risks for consumers. The ultimate goal is to significantly enhance current water supply management practices.Read moreRead less
Delivering robust hydrological predictions for Australia’s water challenges. This project aims to build a virtual hydrological laboratory to identify the best hydrological models that maximise predictive performance in a range of catchments, accounting for their dominant hydrological processes and data availability. New process-informed hydrological model structures will be developed using this virtual laboratory to embody our best understanding of hydrological processes and data from real catch ....Delivering robust hydrological predictions for Australia’s water challenges. This project aims to build a virtual hydrological laboratory to identify the best hydrological models that maximise predictive performance in a range of catchments, accounting for their dominant hydrological processes and data availability. New process-informed hydrological model structures will be developed using this virtual laboratory to embody our best understanding of hydrological processes and data from real catchments. The expected outcomes include major improvements in hydrological predictions for Australian catchments. This project will provide major benefits to irrigators, water authorities and engineers, who rely on hydrological predictions for sustainable water management in the highly-variable, semi-arid Australian climate.Read moreRead less