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

• Main Authors: J. Zeng, T. Matsunaga, N. Saigusa, T. Shirai, S.-I. Nakaoka, Z.-H. Tan
• Format: Article
• Language: English
• Published: Copernicus Publications 2017-04-01
• Series: Ocean Science
• Online Access: http://www.ocean-sci.net/13/303/2017/os-13-303-2017.pdf
• ISSN: 1812-0784; 1812-0792

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.