Articles | Volume 13, issue 2
https://doi.org/10.5194/os-13-303-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/os-13-303-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Technical note: Evaluation of three machine learning models for surface ocean CO2 mapping
Jiye Zeng , Tsuneo Matsunaga, Nobuko Saigusa, Tomoko Shirai, Shin-ichiro Nakaoka, and Zheng-Hong Tan
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- Final revised paper (published on 19 Apr 2017)
- Preprint (discussion started on 25 Oct 2016)
Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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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
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- AC1: 'Response to referee #1 comments', J. Zeng, 02 Mar 2017 Printer-friendly Version Printer-friendly Version
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RC2: 'Review of "Evaluation of three machine learning models for surface ocean CO2 mapping"', Anonymous Referee #2, 23 Jan 2017
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- AC2: 'Response to referee #2 comments', J. Zeng, 02 Mar 2017 Printer-friendly Version
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RC3: 'Review of "Evaluation of three machine learning models for surface ocean CO2 mapping"', Anonymous Referee #3, 26 Feb 2017
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- 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 J. Zeng on behalf of the Authors (02 Mar 2017)
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
AR by J. Zeng on behalf of the Authors (27 Mar 2017)
Manuscript
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...