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Preview	Issue Date	Title	Author(s)
	2005	Input determination for neural network models in water resources applications. Part 1 - background and methodology	Bowden, G.; Dandy, G.; Maier, H.
	2010	Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions	Maier, H.; Jain, A.; Dandy, G.; Sudheer, K.
	2009	Selection of input variables for data driven models: An average shifted histogram partial mutual information estimator approach	Fernando, T.; Maier, H.; Dandy, G.
	2010	Data splitting for artificial neural networks using SOM-based stratified sampling	May, R.; Maier, H.; Dandy, G.
	2009	Sensitivity analysis for decision-making using the MORE method-A Pareto approach	Ravalico, J.; Maier, H.; Dandy, G.
	2008	Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems	May, R.; Dandy, G.; Maier, H.; Nixon, J.
	2012	Evaluation of outputs from automated baseflow separation methods against simulated baseflow from a physically based, surface water-groundwater flow model	Partington, D.; Brunner, P.; Simmons, C.; Werner, A.; Therrien, R.; Maier, H.; Dandy, G.
	2008	A genetic algorithm calibration method based on convergence due to genetic drift	Gibbs, M.; Dandy, G.; Maier, H.
	2011	A hydraulic mixing-cell method to quantify the groundwater component of streamflow within spatially distributed fully integrated surface water-groundwater flow models	Partington, D.; Brunner, P.; Simmons, C.; Therrien, R.; Werner, A.; Dandy, G.; Maier, H.
	2012	Model comparisons for tracer experiments at a clear water storage tank	Crowther, J.; Dandy, G.