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题名: Real-time observation, early warning and forecasting phytoplankton blooms by integrating in situ automated online sondes and hybrid evolutionary algorithms
作者: Ye, Lin1; Cai, Qinghua1; Zhang, Min1; Tan, Lu1
关键词: Early warning ; Eutrophication ; Hybrid evolutionary algorithm ; Phytoplankton blooms ; Real-time observation ; Three Gorges Reservoir
刊名: ECOLOGICAL INFORMATICS
发表日期: 2014-07-01
DOI: 10.1016/j.ecoinf.2014.04.001
卷: 22, 期:-, 页:44-51
收录类别: SCI
文章类型: Article
WOS标题词: Science & Technology ; Life Sciences & Biomedicine
类目[WOS]: Ecology
研究领域[WOS]: Environmental Sciences & Ecology
英文摘要: Phytoplankton bloom is one of the most serious threats to water resource, and remains a global challenge in environmental management Real-time monitoring and forecasting the dynamics of phytoplankton and early warning the risks are critical steps in an effective environmental management. Automated online sondes have been widely used for in situ real-time monitoring of water quality due to their high reliability and low cost. However, the knowledge of using real-time data from those sondes to forecast phytoplankton blooms has been seldom addressed. Here we present an integrated system for real-time observation, early warning and forecasting of phytoplankton blooms by integrating automated online sondes and the ecological model. Specifically, based on the high-frequency data from automated online sondes in Xiangxi Bay of Three Gorges Reservoir, we successfully developed 1-4 days ahead forecasting models for chlorophyll a (chl a) concentration with hybrid evolutionary algorithms (HEM). With the predicted concentration of chl a, we achieved a high precision in 1-7 days ahead early warning of good (chl a < 25 mu g/L) and eutrophic (chl a 8-25 mu g/L) conditions; however only achieved an acceptable precision in 1-2 days ahead early warning of hypertrophic condition (chl a >= 25 mu g/L). Our study shows that the optimized HEM achieved an acceptable performance in real-time short-term forecasting and early warning of phytoplankton blooms with the data from the automated in situ sondes. This system provides an efficient way in real-time monitoring and early warning of phytoplankton blooms, and may have a wide application in eutrophication monitoring and management. (C) 2014 Elsevier B.V. All rights reserved.
关键词[WOS]: HARMFUL ALGAL BLOOMS ; ARTIFICIAL NEURAL-NETWORK ; 3 GORGES RESERVOIR ; NAKDONG RIVER KOREA ; CHLOROPHYLL-A ; XIANGXI BAY ; 3-GORGES RESERVOIR ; SPATIAL-ANALYSIS ; DYNAMICS ; EUTROPHICATION
语种: 英语
WOS记录号: WOS:000339036200005
ISSN号: 1574-9541
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.ihb.ac.cn/handle/342005/20317
Appears in Collections:淡水生态学研究中心_期刊论文

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作者单位: 1.Chinese Acad Sci, Inst Hydrobiol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China
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