An autonomous decision support system for manganese forecasting in subtropical water reservoirs
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Manganese monitoring and removal is essential for water utilities in order to avoid supplying discoloured water to consumers. Traditional manganese monitoring in water reservoirs consists of costly and time-consuming manual lake samplings and laboratory analysis. However, vertical profiling systems can automatically collect and remotely transfer a range of physical parameters that affect the manganese cycle. In this study, a manganese prediction model was developed, based on the profiler's historical data and weather forecasts. The model effectively forecasted seven-day ahead manganese concentrations in the epilimnion of Advancetown Lake (Queensland, Australia). The manganese forecasting model was then operationalised into an automatically updated decision support system with a user-friendly graphical interface that is easily accessible and interpretable by water treatment plant operators. The developed tool resulted in a reduction in traditional expensive monitoring while ensuring proactive water treatment management.
Environmental Modelling & Software
Water Resources Engineering