Technical note: Evaluation of three machine learning models for surface ocean CO2 mapping
Jiye Zeng1, Tsuneo Matsunaga1, Nobuko Saigusa1, Tomoko Shirai1, Shin-ichiro Nakaoka1, and Zheng-Hong Tan21National Institute for Environmental Studies, Tsukuba, Japan 2Department of Environmental Science, Hainan University, China
Received: 06 Sep 2016 – Accepted for review: 19 Oct 2016 – Discussion started: 25 Oct 2016
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-organization map and feedforward neural network, and introduces a machine learning model called support vector machine for ocean CO2 mapping. The technique note provides a practical guide to selecting the models.
Zeng, J., Matsunaga, T., Saigusa, N., Shirai, T., Nakaoka, S.-I., and Tan, Z.-H.: Technical note: Evaluation of three machine learning models for surface ocean CO2 mapping, Ocean Sci. Discuss., doi:10.5194/os-2016-73, in review, 2016.