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Ross SD, Fish J, Moeltner K, et al. Beach-level 24-hour forecasts of Florida red tide-induced respiratory irritation. Harmful Algae. 2021;111:102149doi: 10.1016/j.hal.2021.102149.
Ross, S. D., Fish, J., Moeltner, K., Bollt, E. M., Bilyeu, L., & Fanara, T. (2022). Beach-level 24-hour forecasts of Florida red tide-induced respiratory irritation. Harmful algae, 111102149. https://doi.org/10.1016/j.hal.2021.102149
Ross, Shane D, et al. "Beach-level 24-hour forecasts of Florida red tide-induced respiratory irritation." Harmful algae vol. 111 (2022): 102149. doi: https://doi.org/10.1016/j.hal.2021.102149
Ross SD, Fish J, Moeltner K, Bollt EM, Bilyeu L, Fanara T. Beach-level 24-hour forecasts of Florida red tide-induced respiratory irritation. Harmful Algae. 2022 Jan;111:102149. doi: 10.1016/j.hal.2021.102149. Epub 2021 Dec 12. PMID: 35016762.
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Harmful Algae. 2022 Jan;111:102149. doi: 10.1016/j.hal.2021.102149. Epub 2021 Dec 12.

Beach-level 24-hour forecasts of Florida red tide-induced respiratory irritation.

Harmful algae

Shane D Ross, Jeremie Fish, Klaus Moeltner, Erik M Bollt, Landon Bilyeu, Tracy Fanara

Affiliations

  1. Aerospace and Ocean Engineering, Virginia Tech, Blacksburg, Virginia. Electronic address: [email protected].
  2. Electrical and Computer Engineering and CS the Clarkson Center for Complex Systems Science, Clarkson University, Clarkson, New York USA.
  3. Agricultural and Applied Economics, Virginia Tech, Blacksburg, Virginia USA.
  4. School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, Virginia USA.
  5. Mote Marine Laboratory and Aquarium, Sarasota, Florida USA; National Oceanic and Atmospheric Administration, Washington, DC USA.

PMID: 35016762 DOI: 10.1016/j.hal.2021.102149

Abstract

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.

Copyright © 2021 Elsevier B.V. All rights reserved.

Keywords: Aerosol; Cyanobacteria; Forecast; HAB; Hawkes process; Modeling; New tools; Public health; Red tide; Respiratory irritation

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