Near-shore water quality is influenced by complex terrestrial and oceanic interactions, making accurate prediction challenging. This presentation explores how big data and machine learning enhance water quality predictions in human-impacted bays. Key cases include pollutant flux estimation from unmonitored watersheds, nowcasting with in-situ monitoring, and spatiotemporal reconstruction of multi-source data. Future research directions will also be discussed.
 

big data, AI, machine learning, water quality, bay, near-shore, model