1. Adelaide Research & Scholarship

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Results 31-40 of 265 (Search time: 0.002 seconds).
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PreviewIssue DateTitleAuthor(s)
2004Dynamic role-play online - learning from experienceMaier, H.; Kavanagh, S.; Kindley, R.; Carrington, A.; Baron, J.
2007Assessing prediction uncertainty in the BIGMOD model: A shuffled complex evolution metropolis algorithm approachFernando, T.; Maier, H.; Dandy, G.; Croke, B.; Lex Oxley; International Congress on Modelling and Simulation (17th : 2007 : Christchurch, New Zealand)
2008Uncertainty in Environmental Decision Making: Issues, Challenges and Future directionsMaier, H.; Ascough, J.; Wattenbach, M.; Renschler, C.; Labiosa, W.; Ravalico, J.; Jakeman, A.; Voinov, A.; Rizzoli, A.; Chen, S.
2000Advection, growth and nutrient status of phytoplankton populations in the lower River Murray, South AustraliaBaker, P.; Brookes, J.; Burch, M.; Maier, H.; Ganf, G.
2014An adaptive ant colony optimization framework for scheduling environmental flow management alternatives under varied environmental water availability conditionsSzemis, J.; Maier, H.; Dandy, G.
2006Optimising the mutual information of ecological data clusters using evolutionary algorithmsMaier, H.; Zecchin, A.; Radbone, L.; Goonan, P.
2005A Bayesian method to improve the extrapolation ability of ANNsHumphrey, G.; Maier, H.; Lambert, M.; Hamza, M.; International Conference on Applied Simulation and Modelling (14th : 2005 : Benalmadena, Spain)
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)
2014Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directionsMaier, H.; Kapelan, Z.; Kasprzyk, J.; Kollat, J.; Matott, L.; Cunha, M.; Dandy, G.; Gibbs, M.; Keedwell, E.; Marchi, A.; Ostfeld, A.; Savic, D.; Solomatine, D.; Vrugt, J.; Zecchin, A.; Minsker, B.; Barbour, E.; Kuczera, G.; Pasha, F.; Castelletti, A.; et al.
2014Selection of smoothing parameter estimators for general regression neural networks - applications to hydrological and water resources modellingLi, X.; Zecchin, A.; Maier, H.

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