p < 0.05), with peak performance in cyclonic eddy zones where nutrient upwelling dominates. By mining over 7 million data points, we demonstrate that eddy data incorporation enhances prediction accuracy through physics-informed feature engineering, outperforming conventional satellite-only approaches. This work establishes a paradigm for multivariate remote sensing data synthesis, bridging oceanographic process understanding with AI-driven forecasting in data-sparse deep-sea environments.">

Integrating Mesoscale Eddies Into NARX Neural Networks for Improved Chlorophyll-A Forecasting From Remote Sensing Data in the South China Sea (original) (raw)

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