Journal
HARMFUL ALGAE
Volume 111, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.hal.2021.102149
Keywords
Red tide; Forecast; Respiratory irritation; Modeling; Hawkes process; Aerosol; HAB; Cyanobacteria; New tools; Public health
Categories
Funding
- Global Change Center
- Fralin Life Sciences Institute
- Institute for Society, Culture, and Environment at Virginia Tech
- USDA/NIFA [VA-136344]
- National Science Foundation (NSF) [1922516]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1922516] Funding Source: National Science Foundation
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Accurately forecasting the respiratory irritation level caused by red tide can greatly improve the lives of people living in affected areas. This study trained two different models, one wind-based and one probabilistic self-exciting Hawkes model, using a database of beach conditions. The models were applied to red tide blooms and showed varying levels of accuracy depending on the beach, with the wind-based model performing the best on half of the beaches.
An accurate forecast of the red tide respiratory irritation level would improve the lives of many people living in areas affected by algal blooms. Using a decades-long database of daily beach conditions, two conceptually different models to forecast the respiratory irritation risk level one day ahead of time are trained. One model is wind-based, using the current days' respiratory level and the predicted wind direction of the following day. The other model is a probabilistic self-exciting Hawkes process model. Both models are trained on beaches in Florida during 2011-2017 and applied to the red tide bloom during 2018-2019. For beaches where there is enough historical data to develop a model, the model which performs best depends on the beach. The wind-based model is the most accurate at half the beaches, correctly predicting the respiratory risk level on average about 84% of the time. The Hawkes model is the most accurate (81% accuracy) at nearly all of the remaining beaches.
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