Technical note: Evaluation of three machine learning models for surface ocean CO2 mapping

Other literature type English OPEN
Zeng, Jiye ; Matsunaga, Tsuneo ; Saigusa, Nobuko ; Shirai, Tomoko ; Nakaoka, Shin-ichiro ; Tan, Zheng-Hong (2016)

Reconstructing surface ocean CO<sub>2</sub> from scarce measurements plays an important role in estimating oceanic CO<sub>2</sub> uptake. There are varying degrees of differences among the 14 models included in the Surface Ocean CO<sub>2</sub> Mapping (SOCOM) inter-comparison initiative, in which five models used neural networks. This investigation evaluates two neural networks used in SOCOM, self-organization map and feedforward neural network, and introduces a machine learning model called support vector machine for ocean CO<sub>2</sub> mapping. The technique note provides a practical guide to selecting the models.
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