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Machine learning indicates the fluctuation patterns of chlorophyll a in the high-prevalence red tide area near the Shengsi Islands, East China Sea

ecology
ecosystem-science
environmental-sciences
data-mining-and-machine-learning
biological-oceanography

Abstract

We utilized routine monitoring data from April to October each year to conduct monthly surveys of chlorophyll a (Chl-a) and environmental factors in the high-prevalence red tide area near the Shengsi Islands, East China Sea. Due to the combined effects of upwelling and freshwater intrusion, the survey area experiences frequent red tide occurrences. Six machine learning methods were applied to analyze the data, combined with SHAP values. The results showed that July is the month with the highest Chl-a concentration in the region, with significant inter-annual variability. Phosphate was identified as the most critical factor driving Chl-a fluctuations, while other factors had much smaller effects compared to dissolved inorganic phosphorus (DIP). The study suggests DIP availability (regulated by river plume and upwelling) is the key limiting factor. Future research should adopt broader spatiotemporal datasets to distinguish the multifactorial impacts on coastal Chl-a.
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