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Review

A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish Contamination

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Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa (FCT NOVA), 2829-516 Caparica, Portugal
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IPMA—Instituto Português do Mar e da Atmosfera, 1495-165 Lisboa, Portugal
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CCMAR—Centro de Ciências do Mar, Universidade do Algarve, 8005-139 Faro, Portugal
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INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, 1000-029 Lisboa, Portugal
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NOVA Laboratory for Computer Science and Informatics (NOVA LINCS), FCT NOVA, 2829-516 Caparica, Portugal
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Center for Mathematics and Applications (CMA), FCT NOVA, 2829-516 Caparica, Portugal
*
Author to whom correspondence should be addressed.
Academic Editor: Carmela Caroppo
J. Mar. Sci. Eng. 2021, 9(3), 283; https://doi.org/10.3390/jmse9030283
Received: 30 January 2021 / Revised: 1 March 2021 / Accepted: 2 March 2021 / Published: 5 March 2021
(This article belongs to the Special Issue Applications of Machine Learning in Marine Ecology Studies)
Harmful algal blooms (HABs) are among the most severe ecological marine problems worldwide. Under favorable climate and oceanographic conditions, toxin-producing microalgae species may proliferate, reach increasingly high cell concentrations in seawater, accumulate in shellfish, and threaten the health of seafood consumers. There is an urgent need for the development of effective tools to help shellfish farmers to cope and anticipate HAB events and shellfish contamination, which frequently leads to significant negative economic impacts. Statistical and machine learning forecasting tools have been developed in an attempt to better inform the shellfish industry to limit damages, improve mitigation measures and reduce production losses. This study presents a synoptic review covering the trends in machine learning methods for predicting HABs and shellfish biotoxin contamination, with a particular focus on autoregressive models, support vector machines, random forest, probabilistic graphical models, and artificial neural networks (ANN). Most efforts have been attempted to forecast HABs based on models of increased complexity over the years, coupled with increased multi-source data availability, with ANN architectures in the forefront to model these events. The purpose of this review is to help defining machine learning-based strategies to support shellfish industry to manage their harvesting/production, and decision making by governmental agencies with environmental responsibilities. View Full-Text
Keywords: marine biotoxins; shellfish production; harmful algal blooms; toxic phytoplankton; multivariate time series; time-series forecasting; artificial neural networks; machine learning marine biotoxins; shellfish production; harmful algal blooms; toxic phytoplankton; multivariate time series; time-series forecasting; artificial neural networks; machine learning
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MDPI and ACS Style

Cruz, R.C.; Reis Costa, P.; Vinga, S.; Krippahl, L.; Lopes, M.B. A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish Contamination. J. Mar. Sci. Eng. 2021, 9, 283. https://doi.org/10.3390/jmse9030283

AMA Style

Cruz RC, Reis Costa P, Vinga S, Krippahl L, Lopes MB. A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish Contamination. Journal of Marine Science and Engineering. 2021; 9(3):283. https://doi.org/10.3390/jmse9030283

Chicago/Turabian Style

Cruz, Rafaela C., Pedro Reis Costa, Susana Vinga, Ludwig Krippahl, and Marta B. Lopes 2021. "A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish Contamination" Journal of Marine Science and Engineering 9, no. 3: 283. https://doi.org/10.3390/jmse9030283

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