IA na N.A.S.A.
summary
Uses machine learning on satellite data plus model-derived weather inputs to detect and predict algal blooms, turning chlorophyll concentration into a quantitative proxy for algae presence. Combines precipitation and runoff estimates with atmospheric parameters to build short-term forecasts, and demonstrates end-to-end processing from raw remote-sensing pixels to bloom timing predictions. Claims operational monitoring and actionable forecasts rather than just visualizations.
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