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Real-time observation, early warning and forecasting phytoplankton blooms by integrating in situ automated online sondes and hybrid evolutionary algorithms
Ye, Lin; Cai, Qinghua; Zhang, Min; Tan, Lu
2014-07-01
Source PublicationECOLOGICAL INFORMATICS
ISSN1574-9541
Volume22Issue:-Pages:44-51
AbstractPhytoplankton 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.
SubtypeArticle
KeywordEarly Warning Eutrophication Hybrid Evolutionary Algorithm Phytoplankton Blooms Real-time Observation Three Gorges Reservoir
DOI10.1016/j.ecoinf.2014.04.001
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
Indexed BySCI
Language英语
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEcology
WOS IDWOS:000339036200005
WOS KeywordHARMFUL ALGAL BLOOMS ; ARTIFICIAL NEURAL-NETWORK ; 3 GORGES RESERVOIR ; NAKDONG RIVER KOREA ; CHLOROPHYLL-A ; XIANGXI BAY ; 3-GORGES RESERVOIR ; SPATIAL-ANALYSIS ; DYNAMICS ; EUTROPHICATION
Citation statistics
Cited Times:9[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ihb.ac.cn/handle/342005/20317
Collection淡水生态学研究中心_期刊论文
AffiliationChinese Acad Sci, Inst Hydrobiol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China
Recommended Citation
GB/T 7714
Ye, Lin,Cai, Qinghua,Zhang, Min,et al. Real-time observation, early warning and forecasting phytoplankton blooms by integrating in situ automated online sondes and hybrid evolutionary algorithms[J]. ECOLOGICAL INFORMATICS,2014,22(-):44-51.
APA Ye, Lin,Cai, Qinghua,Zhang, Min,&Tan, Lu.(2014).Real-time observation, early warning and forecasting phytoplankton blooms by integrating in situ automated online sondes and hybrid evolutionary algorithms.ECOLOGICAL INFORMATICS,22(-),44-51.
MLA Ye, Lin,et al."Real-time observation, early warning and forecasting phytoplankton blooms by integrating in situ automated online sondes and hybrid evolutionary algorithms".ECOLOGICAL INFORMATICS 22.-(2014):44-51.
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