Data-driven water quality treatment management decision support system

Using intelligent algorithms and data collected through remote autonomous instrumentation, this project aims to
develop a robust decision support system to enable the prediction of manganese and the character and
concentration of dissolved organic matter in drinking water reservoirs. These predicted water quality parameters
can then be used as model input variables to provide real-time decisions for plant operators on the required
treatment regime for incoming raw water, as well as advise them on the optimal reservoir offtake depth. This will
minimise treatment costs and health risks for consumers. The project will achieve its ultimate goal to significantly
enhance current water supply management practices.