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This document summarizes research on using turbidity data to monitor water quality. It discusses deploying multi-modal sensor platforms to collect turbidity data from the Poolbeg and Marina monitoring sites. Machine learning methods are used to automatically detect turbidity events and vessel trajectories from the turbidity data. Regression trees predict salinity levels and microbial contamination by separating natural from anthropogenic turbidity events. The research aims to provide decision support for water management through surrogate water quality monitoring and informed sampling.Read less