气候变化领域集成服务门户(CCPortal): A real time data driven algal bloom risk forecast system for mariculture management

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DOI	10.1016/j.marpolbul.2020.111731
	A real time data driven algal bloom risk forecast system for mariculture management
	Guo J.; Dong Y.; Lee J.H.W.
发表日期	2020
ISSN	0025326X
卷号	161
英文摘要	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. © 2020 Elsevier Ltd
英文关键词	Artificial neural network; Chlorophyll; Data assimilation; Dissolved oxygen; Eutrophication; Fisheries management; Harmful algal blooms; Real-time forecast; Red tide; Risk management; Stratification; Water quality prediction
语种	英语
scopus关键词	Forecasting; Marine biology; Neural networks; Oceanography; Surface waters; Artificial neural network models; Chlorophyll measurements; Early Warning System; Fisheries management; Harmful algal blooms; Operational forecasts; Sea surface temperature (SST); Vertical temperature; Information management; algal bloom; artificial neural network; chlorophyll; dissolved oxygen; eutrophication; frequency analysis; numerical model; real time; red tide; sea surface temperature; stratification; China; Hong Kong; New Territories; Tolo Harbour; algae
来源期刊	Marine Pollution Bulletin
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/148419
作者单位	Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong; School of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao, China; Department of Civil and Environmental Engineering, Institute for Advanced Study, The Hong Kong University of Science and Technology, Hong Kong
推荐引用方式 GB/T 7714	Guo J.,Dong Y.,Lee J.H.W.. A real time data driven algal bloom risk forecast system for mariculture management[J],2020,161.
APA	Guo J.,Dong Y.,&Lee J.H.W..(2020).A real time data driven algal bloom risk forecast system for mariculture management.Marine Pollution Bulletin,161.
MLA	Guo J.,et al."A real time data driven algal bloom risk forecast system for mariculture management".Marine Pollution Bulletin 161(2020).