Technical note: Evaluation of three machine learning models for surface ocean CO<sub>2</sub> mapping
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Published:2017-04-19
Issue:2
Volume:13
Page:303-313
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ISSN:1812-0792
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Container-title:Ocean Science
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language:en
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Short-container-title:Ocean Sci.
Author:
Zeng Jiye, Matsunaga TsuneoORCID, Saigusa Nobuko, Shirai Tomoko, Nakaoka Shin-ichiroORCID, Tan Zheng-Hong
Abstract
Abstract. Reconstructing surface ocean CO2 from scarce measurements plays an important role in estimating oceanic CO2 uptake. There are varying degrees of differences among the 14 models included in the Surface Ocean CO2 Mapping (SOCOM) inter-comparison initiative, in which five models used neural networks. This investigation evaluates two neural networks used in SOCOM, self-organizing maps and feedforward neural networks, and introduces a machine learning model called a support vector machine for ocean CO2 mapping. The technique note provides a practical guide to selecting the models.
Publisher
Copernicus GmbH
Subject
Cell Biology,Developmental Biology,Embryology,Anatomy
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