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Ocean Science An interactive open-access journal of the European Geosciences Union

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Ocean Sci., 13, 303-313, 2017
http://www.ocean-sci.net/13/303/2017/
doi:10.5194/os-13-303-2017
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Technical note
19 Apr 2017
Technical note: Evaluation of three machine learning models for surface ocean CO2 mapping
Jiye Zeng et al.
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Interactive discussionStatus: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version      Supplement - Supplement
 
RC1: 'Review report of “Technical note: Evaluation of three machine learning models for surface ocean CO2 mapping” by Jiye Zeng et al.', Anonymous Referee #1, 29 Nov 2016 Printer-friendly Version 
AC1: 'Response to referee #1 comments', J. Zeng, 02 Mar 2017 Printer-friendly Version Supplement 
 
RC2: 'Review of "Evaluation of three machine learning models for surface ocean CO2 mapping"', Anonymous Referee #2, 23 Jan 2017 Printer-friendly Version 
AC2: 'Response to referee #2 comments', J. Zeng, 02 Mar 2017 Printer-friendly Version 
 
RC3: 'Review of "Evaluation of three machine learning models for surface ocean CO2 mapping"', Anonymous Referee #3, 26 Feb 2017 Printer-friendly Version 
AC3: 'Response to referee #3 comments', J. Zeng, 02 Mar 2017 Printer-friendly Version 
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision
AR by Lorena Grabowski on behalf of the Authors (03 Mar 2017)  Author's response
ED: Publish subject to minor revisions (Editor review) (10 Mar 2017) by John M. Huthnance  
AR by J. Zeng on behalf of the Authors (16 Mar 2017)  Author's response
ED: Publish subject to technical corrections (24 Mar 2017) by John M. Huthnance  
Publications Copernicus
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Short summary
Three machine learning models were investigated for the reconstruction of global surface ocean CO2 concentration. They include self-organizing maps (SOMs), feedforward neural networks (FNNs), and support vector machines (SVMs). Our results show that the SVM performs the best, the FNN the second, and the SOM the worst. While the SOM does not have over-fitting problems, it is sensitive to data scaling and its discrete interpolation may not be good for some applications.
Three machine learning models were investigated for the reconstruction of global surface ocean...
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