An autonomous decision support system for manganese forecasting in subtropical water reservoirs |
| |
| Affiliation: | 1. Griffith School of Engineering, Griffith University, Gold Coast Campus, QLD 4222, Australia;2. Seqwater, Ipswich, QLD 4305, Australia;1. School of Civil and Environmental Engineering, Cornell University, Ithaca, NY, USA;2. Department of Civil Engineering, Indian Institute of Technology Hyderabad, Yeddumailaram, India;3. Department of Geosciences, The Pennsylvania State University, University Park, PA, USA;4. Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA;5. Earth and Environmental Systems Institute, The Pennsylvania State University, University Park, PA, USA;1. Dept. of Computer Architecture, University of Granada, Spain;2. Water Research Institute, University of Granada, Spain;3. Dept. of Civil Engineering, University of Granada, Spain;4. Research Center for Information and Communications Technologies, University of Granada (CITIC), Spain;1. Ven Te Chow Hydrosystems Laboratory, Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA;2. Nebland Software, LLC, Green Bay, WI, USA;1. USDA Agricultural Research Service, Grazinglands Research Laboratory, El Reno, OK, 73036, USA;2. USDA Agricultural Research Service, Conservation and Production Research Laboratory, Bushland, TX, 79012, USA;3. USDA Agricultural Research Service, Grassland Soil and Water Research Laboratory, Temple, TX, 76502, USA;4. Texas A&M, Agriculture and Life Sciences, Spatial Science Laboratory, College Station, TX, 77843, USA;1. Irstea, UR LISC Laboratoire d''ingénierie des systèmes complexes, 9 avenue Blaise Pascal - CS 20085, 63178 Aubière, France;2. Université Laval, Département de génie civil et de génie des eaux, Pavillon Adrien-Pouliot, 1065 avenue de la Médecine, Québec G1V 0A6, QC, Canada |
| |
| 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. |
| |
| Keywords: | Manganese Statistical modelling Water treatment Decision support system Prediction modelling Water management |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
