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


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (01 Mar 2018) by Dimitri Solomatine
AR by Anna Wenzel on behalf of the Authors (08 Mar 2018)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (19 Mar 2018) by Dimitri Solomatine
RR by Anonymous Referee #2 (31 Mar 2018)
RR by Anonymous Referee #1 (15 Apr 2018)
ED: Publish as is (23 Apr 2018) by Dimitri Solomatine
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.