A machine-learning approach to modeling picophytoplankton abundances in the South China Sea
- Chen, Bingzhang
- Liu, Hongbin
- Xiao, Wupeng
- Wang, Lei
- Huang, Bangqin
DOIAbstract
Picophytoplankton, the smallest phytoplankton (<3 micron), contribute significantly to primary production in the oligotrophic South China Sea. To improve our ability to predict picophytoplankton abundances in the South China Sea and infer the underlying mechanisms, we compared four machine learning algorithms to estimate the horizontal and vertical distributions of picophytoplankton abundances. The inputs of the algorithms include spatiotemporal (longitude, latitude, sampling depth and date) and environmental variables (sea surface temperature, chlorophyll, and light). The algorithms were fit to a dataset of 2442 samples collected from 2006 to 2012. We find that the Boosted Regression Trees (BRT) gives the best prediction performance with R...
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