FN Archimer Export Format
PT J
TI Technical note: Evaluation of three machine learning models for surface ocean CO2 mapping
BT 
AF ZENG, Jiye
   MATSUNAGA, Tsuneo
   SAIGUSA, Nobuko
   SHIRAI, Tomoko
   NAKAOKA, Shin-ichiro
   TAN, Zheng-Hong
AS 1:1;2:1;3:1;4:1;5:1;6:2;
FF 1:;2:;3:;4:;5:;6:;
C1 Natl Inst Environm Studies, Ctr Global Environm Res, Tsukuba, Ibaraki, Japan.
   Hainan Univ, Inst Trop Agr & Forestry, Haikou, Hainan, Peoples R China.
C2 NIES, JAPAN
   UNIV HAINAN, CHINA
IN DOAJ
IF 2.289
TC 12
UR https://archimer.ifremer.fr/doc/00383/49464/49949.pdf
LA English
DT Article
CR OISO - OCÉAN INDIEN SERVICE D'OBSERVATION
AB 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. 
PY 2017
PD APR
SO Ocean Science
SN 1812-0784
PU Copernicus Gesellschaft Mbh
VL 13
IS 2
UT 000399756000001
BP 303
EP 313
DI 10.5194/os-13-303-2017
ID 49464
ER

EF