Higher Degree by Research Candidate
School of Civil, Environmental and Mining Engineering (Inact)
Faculty of Sciences, Engineering and Technology
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 2021 Hunter, J., Thyer, M., McInerney, D., & Kavetski, D. (2021). Achieving high-quality probabilistic predictions from hydrological models calibrated with a wide range of objective functions. Journal of Hydrology, 603, 1-22.
DOI Scopus7 WoS4
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 Scopus32 WoS25
Year Citation 2022 Thyer, M., Hunter, J., McInerney, D., & Kavetski, D. (2022). High-quality probabilistic predictions for existing hydrological models with common objective functions. Poster session presented at the meeting of Abstracts of the EGU General Assembly 2022. Vienna, Austria & Online: Copernicus GmbH.
2021 Hunter, J., Thyer, M., Kavetski, D., & McInerney, D. (2021). An open-source R-package and web application for high-quality probabilistic predictions in hydrology. Poster session presented at the meeting of Unknown Conference.
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