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  • Presentation | H32F: Monitoring, Prediction, and Mitigation of Harmful Algal Blooms (HABs) I Oral
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  • H32F-06: Operational Forecasting of Harmful Algal Blooms in Florida Lakes Using a Two-Stage Bayesian Model
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Author(s):
Gi-Hyeon Park, Research Triangle Institute (First Author, Presenting Author)
Natalie Reynolds, RTI International
Jonathan Holt, RTI International
Kimberly Matthews, RTI International


Harmful algal blooms, which are overgrowths of algae, can produce toxins that are dangerous to people, pets, and wildlife, and can harm Florida's economy. Currently, officials often respond to these blooms only after they appear. To help them get ahead of the problem, we built a new computer model that acts like a weather forecast for algal blooms in Florida's largest lakes. Using data from satellites and weather forecasts, our model predicts where a bloom is likely to occur and how intense it might be up to a week in advance. The model has proven to be highly accurate, correctly predicting the presence or absence of blooms about 95% of the time. This tool gives state agencies an early warning system, allowing them to be more proactive in their monitoring and response efforts, which can help protect both public health and Florida's vital water resources.



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