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.Framework for developing hybrid process-driven, artificial neural network and regression models for salinity prediction in river systems
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- Final revised paper (published on 22 May 2018)
- Supplement to the final revised paper
- Preprint (discussion started on 25 Sep 2017)
Interactive discussion
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment


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RC1: 'hess-2017-571 comment', Anonymous Referee #1, 25 Oct 2017
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AC1: 'Response to Anonymous Referee #1', Jason Hunter, 05 Nov 2017
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AC1: 'Response to Anonymous Referee #1', Jason Hunter, 05 Nov 2017
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RC2: 'Clear and well written paper', Anonymous Referee #2, 22 Feb 2018
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AC2: 'Response to Anonymous Referee #2', Jason Hunter, 23 Feb 2018
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AC2: 'Response to Anonymous Referee #2', Jason Hunter, 23 Feb 2018
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
