Articles | Volume 13, issue 2
Ocean Sci., 13, 303–313, 2017
https://doi.org/10.5194/os-13-303-2017
Ocean Sci., 13, 303–313, 2017
https://doi.org/10.5194/os-13-303-2017

Technical note 19 Apr 2017

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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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
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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.