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Preview	Issue Date	Title	Author(s)
	2006	Critical values of a kernel density-based mutual information estimator	May, R.; Dandy, G.; Maier, H.; Fernando, T.; Yen, G.; International Joint Conference on Neural Networks (2006 : Vancouver, Canada)
	2009	Making the most of modelling: A decision framework for the water industry	Blackmore, J.; Dandy, G.; Kuczera, G.; Rahman, J.; World IMACS and MODSIM09 International Congress (18th : 2009 : Cairns, Qld)
	2003	Data transformation for neural network models in water resources applications	Bowden, G.; Dandy, G.; Maier, H.
	2003	Use of artificial neural networks for modelling chlorine residuals in water distribution systems	Gibbs, M.; Morgan, N.; Maier, H.; Dandy, G.; Holmes, M.; Nixon, J.; Post, D.; International Congress on Modelling and Simulation (15th : 2003 : Townsville, Queensland)
	2004	Applying fitness landscape measures to water distribution optimization problems	Gibbs, M.; Maier, H.; Dandy, G.; Liong, S.; Phoon, K.; Babovic, V.; International Conference on Hydroinformatics (2004 : Singapore)
	2009	Sustainability trade-offs in the planning and design of cluster scale greywater reuse systems	Harnett, L.; Nicolson, J.; Tennant, R.; Dandy, G.; Maier, H.; Anderssen, R.S.; Braddock, R.D.; Newham, L.T.H.; World IMACS and MODSIM09 International Congress (18th : 2009 : Cairns, Qld)
	2009	Selection of input variables for data driven models: An average shifted histogram partial mutual information estimator approach	Fernando, T.; Maier, H.; Dandy, G.
	2007	Assessing prediction uncertainty in the BIGMOD model: A shuffled complex evolution metropolis algorithm approach	Fernando, T.; Maier, H.; Dandy, G.; Croke, B.; Lex Oxley; International Congress on Modelling and Simulation (17th : 2007 : Christchurch, New Zealand)
	2005	Efficient selection of inputs for artificial neural network models	Fernando, T.; Maier, H.; Dandy, G.; May, R.; Zerger, A.; Argent, R.; International Congress on Modelling and Simulation (16th : 2005 : Melbourne, Victoria)
	2005	Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a river	Bowden, G.; Maier, H.; Dandy, G.