TY - JOUR
T1 - A review of recent machine learning advances for forecasting harmful algal blooms and shellfish contamination
AU - Cruz, Rafaela C.
AU - Costa, Pedro Reis
AU - Vinga, Susana
AU - Krippahl, Ludwig
AU - Lopes, Marta B.
N1 - Funding Information:
info:eu-repo/grantAgreement/EC/H2020/951970/EU#
Funding: This work was funded by the project “MATISSE: A machine learning-based forecasting system for shellfish safety” (DSAIPA/DS/0026/2019).
The work was also supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with references CEECINST/00102/2018, UIDB/04516/2020 (NOVA LINCS), UIDB/00297/2020 (CMA), UIDB/50021/2020 (INESC-ID), and UID/Multi/04326/2020 (CCMAR).
PY - 2021/3/5
Y1 - 2021/3/5
N2 - 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.
AB - 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.
KW - Artificial neural networks
KW - Harmful algal blooms
KW - Machine learning
KW - Marine biotoxins
KW - Multivariate time series
KW - Shellfish production
KW - Time-series forecasting
KW - Toxic phytoplankton
UR - http://www.scopus.com/inward/record.url?scp=85103021115&partnerID=8YFLogxK
U2 - 10.3390/jmse9030283
DO - 10.3390/jmse9030283
M3 - Review article
AN - SCOPUS:85103021115
SN - 2077-1312
VL - 9
JO - Journal of Marine Science and Engineering
JF - Journal of Marine Science and Engineering
IS - 3
M1 - 283
ER -