A real time data driven algal bloom risk forecast system for mariculture management.

Autor: Guo J; Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China., Dong Y; School of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao, China., Lee JHW; Department of Civil and Environmental Engineering and Institute for Advanced Study, The Hong Kong University of Science and Technology, Hong Kong, China. Electronic address: jhwlee@ust.hk.
Jazyk: angličtina
Zdroj: Marine pollution bulletin [Mar Pollut Bull] 2020 Dec; Vol. 161 (Pt B), pp. 111731. Date of Electronic Publication: 2020 Oct 30.
DOI: 10.1016/j.marpolbul.2020.111731
Abstrakt: 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 0.35 o C, 0.51 o C, 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.
(Copyright © 2020 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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