Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 22, issue 5
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.
https://doi.org/10.5194/hess-22-2987-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 22, issue 5
Research article
 | 
22 May 2018
Research article |  | 22 May 2018

Framework for developing hybrid process-driven, artificial neural network and regression models for salinity prediction in river systems

Jason M. Hunter, Holger R. Maier, Matthew S. Gibbs, Eloise R. Foale, Naomi A. Grosvenor, Nathan P. Harders, and Tahali C. Kikuchi-Miller

Viewed

Total article views: 5,057 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,565 1,375 117 5,057 146 126 147
  • HTML: 3,565
  • PDF: 1,375
  • XML: 117
  • Total: 5,057
  • Supplement: 146
  • BibTeX: 126
  • EndNote: 147
Views and downloads (calculated since 25 Sep 2017)
Cumulative views and downloads (calculated since 25 Sep 2017)

Viewed (geographical distribution)

Total article views: 5,057 (including HTML, PDF, and XML) Thereof 4,724 with geography defined and 333 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 22 Nov 2025
Download
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.
Read more
Share