Articles | Volume 22, issue 5
https://doi.org/10.5194/hess-22-2987-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-22-2987-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 May 2018
Research article |
| 22 May 2018
| 22 May 2018
Framework for developing hybrid process-driven, artificial neural network and regression models for salinity prediction in river systems
Jason M. Hunter
CORRESPONDING AUTHOR
School of Civil, Environmental and Mining Engineering, The University
of Adelaide, Adelaide, 5005 SA, Australia
Holger R. Maier
School of Civil, Environmental and Mining Engineering, The University
of Adelaide, Adelaide, 5005 SA, Australia
Matthew S. Gibbs
School of Civil, Environmental and Mining Engineering, The University
of Adelaide, Adelaide, 5005 SA, Australia
Department of Environment and Water, GPO Box 2384, Adelaide, 5001 SA, Australia
Eloise R. Foale
School of Civil, Environmental and Mining Engineering, The University
of Adelaide, Adelaide, 5005 SA, Australia
Naomi A. Grosvenor
School of Civil, Environmental and Mining Engineering, The University
of Adelaide, Adelaide, 5005 SA, Australia
Nathan P. Harders
School of Civil, Environmental and Mining Engineering, The University
of Adelaide, Adelaide, 5005 SA, Australia
Tahali C. Kikuchi-Miller
School of Civil, Environmental and Mining Engineering, The University
of Adelaide, Adelaide, 5005 SA, Australia
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Latest update: 01 Jul 2025
Short summary
This research proposes a generalised hybrid model development framework and applies it to a case study of salinity prediction in a reach of the Murray River. The hybrid model combines five sub-models which describe one process of salt entry each and are developed based on the amount of system knowledge and data that are available to support each individual process. The model demonstrates increased performance over two benchmark models and has implications for future model development processes.
This research proposes a generalised hybrid model development framework and applies it to a case...
