A review of recent machine learning advances for forecasting harmful algal blooms and shellfish contamination

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 (NOV...

ver descrição completa

Detalhes bibliográficos
Autor principal: Cruz, Rafaela C. (author)
Outros Autores: Costa, Pedro Reis (author), Vinga, Susana (author), Krippahl, Ludwig (author), Lopes, Marta B. (author)
Formato: review
Idioma:eng
Publicado em: 2021
Assuntos:
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
Texto completo:http://hdl.handle.net/10362/125029
País:Portugal
Oai:oai:run.unl.pt:10362/125029
Descrição
Resumo: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).

Registos relacionados