UF researcher develops machine-learning tool to better predict Florida red tide
Recent University of Florida graduate Nicholas Chin has created a framework using machine learning to identify the environmental factors associated with the initiation, growth, and termination of red tide blooms along Florida’s southwest coast. The study, accepted for publication in the journal Harmful Algae, analyzed more than 20 years of data and identified seven distinct bloom states in the Charlotte Harbor region. Researchers say the tool could sharpen bloom forecasting and guide water-management decisions, particularly around controlling nutrient runoff from land.
Sources: UF News
