Jason Hunter

Higher Degree by Research Candidate

School of Architecture and Civil Engineering

Faculty of Sciences, Engineering and Technology

My Research
Career
Publications
Contact

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.

Jason can be found on LinkedIn and on Twitter.

Education

Date Institution name Country Title
2014 - 2018 The University of Adelaide Australia Bachelor of Engineering (Civil & Environmental) with First-Class Honours
Research Interests

Open Software Optimisation Simulation and Modelling Stochastic Analysis and Modelling Surfacewater Hydrology

Date	Institution name	Country	Title
2014 - 2018	The University of Adelaide	Australia	Bachelor of Engineering (Civil & Environmental) with First-Class Honours

Journals

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 Scopus11 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 Scopus35 WoS25

Conference Papers

Year	Citation
2021	Kavetski, D., McInerney, D., Hunter, J., & Thyer, M. (2021). Recent learnings towards achieving high quality probabilistic predictions in practical applications of hydrological models. In R. W. Vervoort, A. A. Voinov, J. P. Evans, & L. Marshall (Eds.), MODSIM2021, 24th International Congress on Modelling and Simulation. (pp. 526-532). Canberra, ACT, Australia: Modelling and Simulation Society of Australia and New Zealand. DOI
2021	Thyer, M., McInerney, D., Kavetski, D., & Hunter, J. (2021). Practical approaches to produce high-quality probabilistic predictions and improve risk-based design making. In Proceedings of the Hydrology and Water Resources Symposium (HWRS 2021) (pp. 612-625). virtual online: Engineers Australia.
2021	Hunter, J., Thyer, M., McInerney, D., & Kavetski, D. (2021). High-quality probabilistic predictions for existing hydrological models with common objective functions. In Proceedings of the Hydrology and Water Resources Symposium (HWRS 2021) (pp. 626-639). Adelaide, Australia: Engineers Australia. DOI

Conference Items

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. DOI
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. DOI

Email: jason.hunter@adelaide.edu.au

Jason Hunter

Education

Research Interests

Journals

Conference Papers

Conference Items

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