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Results 11-18 of 18 (Search time: 0.003 seconds).
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PreviewIssue DateTitleAuthor(s)
2002Effectiveness of flow management in reducing the risk of cyanobacterial blooms in riversClark, T.; Humphrey, G.; Frazer, A.; Sanderson, A.; Maier, H.; David Sheehan,; Hydrology and Water Resources Symposium (27th : 2002 : Melbourne, Vic.)
2014An evaluation framework for input variable selection algorithms for environmental data-driven modelsGalelli, S.; Humphrey, G.; Maier, H.; Castelletti, A.; Dandy, G.; Gibbs, M.
2008Bayesian model selection applied to artificial neural networks used for water resources modelingHumphrey, G.; Maier, H.; Lambert, M.
2006A probabilistic method for assisting knowledge extraction from artificial neural networks used for hydrological predictionHumphrey, G.; Maier, H.; Lambert, M.
2005Calibration and validation of neural networks to ensure physically plausible hydrological modelingHumphrey, G.; Maier, H.; Lambert, M.
2017Improved validation framework and R-package for artificial neural network modelsHumphrey, G.; Maier, H.; Wu, W.; Mount, N.; Dandy, G.; Abrahart, R.; Dawson, C.
2004Risk-based approach for assessing the effectiveness of flow management in controlling cyanobacterial blooms in riversMaier, H.; Humphrey, G.; Clark, T.; Frazer, A.; Sanderson, A.
2021Beyond validation: assessing the legitimacy of artificial neural network modelsHumphrey, G.; Maier, H.; Wu, W.; Mount, N.; Dandy, G.; Dawson, C.; International Congress on Modelling and Simulation (MODSIM) (5 Dec 2021 - 10 Dec 2021 : Sydney, NSW, Australia)

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