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Results 1-10 of 12 (Search time: 0.001 seconds).
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PreviewIssue DateTitleAuthor(s)
2005Efficient selection of inputs for artificial neural network modelsFernando, T.; Maier, H.; Dandy, G.; May, R.; Zerger, A.; Argent, R.; International Congress on Modelling and Simulation (16th : 2005 : Melbourne, Victoria)
2004Control-oriented water quality modelling using artificial neural networksMay, R.; Maier, H.; Dandy, G.; Nixon, J.; Howlett, P.; Enviro 04 (2004 : Sydney, Australia)
2012A method for comparing data splitting approaches for developing hydrological ANN modelsWu, W.; May, R.; Dandy, G.; Maier, H.; International Congress on Environmental Modelling and Software (6th : 2012 : Leipzig, Germany)
2012Exploring the impact of data splitting methods on artificial neural network modelsWu, W.; Maier, H.; Dandy, G.; May, R.; International Conference on Hydroinformatics (10th : 2012 : Hamburg, Germany)
2005DrCT®: Developing tools for improved disinfection control within water distribution systemsHolmes, M.; Chow, C.; May, R.; Badalyan, A.; Fitzgerald, F.; Nixon, J.; Dandy, G.; Maier, H.; Disinfection 2005 Conference (2005 : Mesa, Arizona)
2009Developing artificial neural networks for water quality modelling and analysisMay, R.; Maier, H.; Dandy, G.; Grady Hanrahan,
2004General regression neural networks for modeling disinfection residual in water distribution systemsMay, R.; Maier, H.; Dandy, G.; Nixon, J.; World Water and Environmental Resources Congress (2004 : Salt Lake City, Utah)
2006Critical values of a kernel density-based mutual information estimatorMay, R.; Dandy, G.; Maier, H.; Fernando, T.; Yen, G.; International Joint Conference on Neural Networks (2006 : Vancouver, Canada)
2013A benchmarking approach for comparing data splitting methods for modeling water resources parameters using artificial neural networksWu, W.; May, R.; Maier, H.; Dandy, G.
2008Non-linear variable selection for artificial neural networks using partial mutual informationMay, R.; Maier, H.; Dandy, G.; Fernando, T.