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PreviewIssue DateTitleAuthor(s)
2000Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applicationsMaier, H.; Dandy, G.
2005Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a riverBowden, G.; Maier, H.; Dandy, G.
2008Non-linear variable selection for artificial neural networks using partial mutual informationMay, R.; Maier, H.; Dandy, G.; Fernando, T.
2010Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directionsMaier, H.; Jain, A.; Dandy, G.; Sudheer, K.
2013Relative magnitudes of sources of uncertainty in assessing climate change impacts on water supply security for the southern Adelaide water supply systemPaton, F.; Maier, H.; Dandy, G.
2010The response of freshwater plants to salinity pulsesGoodman, A.; Ganf, G.; Dandy, G.; Maier, H.; Gibbs, M.
2011An integrated dynamic modeling framework for investigating the impact of climate change and variability on irrigated agricultureRowan, T.; Maier, H.; Connor, J.; Dandy, G.
2016A hybrid approach to monthly streamflow forecasting: integrating hydrological model outputs into a Bayesian artificial neural networkHumphrey, G.; Gibbs, M.; Dandy, G.; Maier, H.
1996The use of artificial neural networks for the prediction of water quality parametersMaier, H.; Dandy, G.
2012Real-time deployment of artificial neural network forecasting models: Understanding the range of applicabilityBowden, G.; Maier, H.; Dandy, G.