An autonomous decision support system for manganese forecasting in subtropical water reservoirs. (November 2015)

• Record Type: Journal Article
• Authors: Bertone, Edoardo; Stewart, Rodney A.; Zhang, Hong; Bartkow, Michael; Hacker, Charles
• Is Part Of: Environmental modelling & software. Volume 73(2015:Nov.)
• Page Start: 133
• Page End: 147
• Publication Date: 2015-11
• Subjects: Manganese -- Statistical modelling -- Water treatment -- Decision support system -- Prediction modelling -- Water management; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.08.008
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: DSS developed for Mn forecasting and proactive treatment in water reservoirs. DSS enabled significant reduction in Mn sampling and laboratory testing costs. Graphical user interface enabled proactive Mn water treatment by operators.