Autor(es):
Cruz, Rafaela C. ; Costa, Pedro Reis ; Vinga, Susana ; Krippahl, Ludwig ; Lopes, Marta B.
Data: 2021
Identificador Persistente: http://hdl.handle.net/10362/125029
Origem: Repositório Institucional da UNL
Projeto/bolsa:
info:eu-repo/grantAgreement/EC/H2020/951970/EU;
Assunto(s): Artificial neural networks; Harmful algal blooms; Machine learning; Marine biotoxins; Multivariate time series; Shellfish production; Time-series forecasting; Toxic phytoplankton; Civil and Structural Engineering; Water Science and Technology; Ocean Engineering; SDG 13 - Climate Action; SDG 14 - Life Below Water
Descrição
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).
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.
