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Results 1-10 of 15 (Search time: 0.003 seconds).
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PreviewIssue DateTitleAuthor(s)
2000Use of evolutionary artificial neural network models to forecast concentrations of cyanobacteria in the River MurrayBowden, G.; Dandy, G.; Maier, H.; International Hydrology and Water Resources Symposium (3rd : 2000 : Perth, W.A.)
2002An international comparative study of artificial neural network techniques for river stage forecastingAbrahart, R.; Anctil, F.; Bowden, G.; Dandy, G.; Maier, H.; Campolo, M.; Soldati, A.; Cannas, B.; Fanni, A.; Dawson, C.; Dulal, K.; Elshorbagy, A.; Hall, M.; Varoonchotikul, P.; Imrie, C.; Jain, S.; Jayawardena, A.; Fernando, T.; Liong, S.; Doan, C.; Panu, U.; Shamseldin, A.; Solomatine, D.; Sudheer, K.; Wilby, R.; National Hydrology Symposium (8th : 2002 : Birmingham, U.K.)
2001Understanding the range of applicability of artificial neural network forecasting modelsBowden, G.; Maier, H.; Dandy, G.; International Congress on Modelling and Simulation (14th : 2001 : Canberra, A.C.T.)
2003Forecasting chlorine residuals in a water distribution system using a general regression neural networkBowden, G.; Nixon, J.; Dandy, G.; Maier, H.; Holmes, M.; International Congress on Modelling and Simulation (15th : 2003 : Townsville, Queensland)
2003An evaluation of methods for the selection of inputs for an artificial neural network based river modelBowden, G.; Dandy, G.; Maier, H.
2005Input determination for neural network models in water resources applications. Part 1 - background and methodologyBowden, G.; Dandy, G.; Maier, H.
2006Forecasting chlorine residuals in a water distribution system using a general regression neural networkBowden, G.; Nixon, J.; Dandy, G.; Maier, H.; Holmes, M.
2005Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a riverBowden, G.; Maier, H.; Dandy, G.
2002Optimal division of data for neural network models in water resources applicationsBowden, G.; Maier, H.; Dandy, G.
2012Real-time deployment of artificial neural network forecasting models: Understanding the range of applicabilityBowden, G.; Maier, H.; Dandy, G.