“ETRI’s AI for making a better tomorrow” Episode 1 – Spatiotemporal algal bloom prediction using deep learning
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In this episode of AI for Good Perspectives, key experts from ETRI discuss their goal to build a real-time algal bloom control system, using ML-based prediction to help with decision making on early algae suppression.
IN THIS EPISODE, WE WILL DISCUSS…
(1) Extreme data imbalance: Input datasets are inherently very skewed since algal blooms are rarely observed. How can we train ML models from extremely imbalanced water quality data?
(2) Broad target data: The target area for this project is too broad to collect all datasets. How can we estimate chlorophyll-a concentration for all target areas?
This AI for Good Perspectives episode is part our “ETRI’s AI for making a better tomorrow” series.
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AI for Good Perspectives are interviews, viewpoints and presentations from the AI for Good community, moderated by professional journalists and available on demand.