Higher Degree by Research Candidate
School of Civil, Environmental and Mining Engineering
Faculty of Engineering, Computer and Mathematical Sciences
The core interest of Jason's thesis is improving the quality and accessibility of probabilistic streamflow predictions in hydrology, to which end he has developed a generalised methodology for high-quality probabilistic predictions and embedded that in a user-friendly R-package. He has also worked with artificial neural networks, system optimisation, rainfall-runoff models, water quality and conditions in pipelines.
Year Citation 2018 Hunter, J. M., Maier, H. R., Gibbs, M. S., Foale, E. R., Grosvenor, N. A., Harders, N. P., & Kikuchi-Miller, T. C. (2018). Modelling salinity in river systems using hybrid process and data-driven models. Hydrology and Earth System Sciences. 2018 Hunter, J., Maier, H., Gibbs, M., Foale, E., Grosvenor, N., Harders, N., & Kikuchi-Miller, T. (2018). Framework for developing hybrid process-driven, artificial neural network and regression models for salinity prediction in river systems. Hydrology and Earth System Sciences, 22(5), 2987-3006.
DOI Scopus12 WoS7
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