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PreviewIssue DateTitleAuthor(s)
2014An evaluation framework for input variable selection algorithms for environmental data-driven modelsGalelli, S.; Humphrey, G.; Maier, H.; Castelletti, A.; Dandy, G.; Gibbs, M.
2018Framework for developing hybrid process-driven, artificial neural network and regression models for salinity prediction in river systemsHunter, J.; Maier, H.; Gibbs, M.; Foale, E.; Grosvenor, N.; Harders, N.; Kikuchi-Miller, T.
2018Empirically derived method and software for semi-automatic calibration of Cellular Automata land-use modelsNewland, C.; Zecchin, A.; Maier, H.; Newman, J.; van Delden, H.
2003Settlement prediction of shallow foundations on granular soils using B-spline neurofuzzy modelsShahin, M.; Maier, H.; Jaksa, M.
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.
2001First-order reliability method for estimating reliability, vulnerability, and resilienceMaier, H.; Lence, B.; Tolson, B.; Foschi, R.
2004Data division for developing neural networks applied to geotechnical engineeringShahin, M.; Maier, H.; Jaksa, M.
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.
2001Flow management strategies to control blooms of the cyanobacterium, Anabaena circinalis, in the River Murray at Morgan, South AustraliaMaier, H.; Burch, M.; Bormans, M.