A real time data driven algal bloom risk forecast system for mariculture management.
Mar Pollut Bull
; 161(Pt B): 111731, 2020 Dec.
Article
em En
| MEDLINE
| ID: mdl-33130398
ABSTRACT
In eutrophic coastal waters, harmful algal blooms (HAB) often occur and present challenges to environmental and fisheries management. Despite decades of research on HAB early warning systems, the field validation of algal bloom forecast models have received scant attention. We propose a daily algal bloom risk forecast system based on (i) a vertical stability theory verified against 191 past algal bloom events; and (ii) a data-driven artificial neural network (ANN) model that assimilates high frequency data to predict sea surface temperature (SST), vertical temperature and salinity differential with an accuracy of 0.35oC, 0.51oC, and 0.58 psu respectively. The model does not rely on past chlorophyll measurements and has been validated against extensive field data. Operational forecasts are illustrated for representative algal bloom events at a marine fish farm in Tolo Harbour, Hong Kong. The robust model can assist with traditional onsite monitoring as well as artificial-intelligence (AI) based methods.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Contexto em Saúde:
2_ODS3
Problema de saúde:
2_quimicos_contaminacion
Assunto principal:
Clorofila
/
Proliferação Nociva de Algas
Tipo de estudo:
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
País/Região como assunto:
Asia
Idioma:
En
Revista:
Mar Pollut Bull
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
China