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Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/105055
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dc.contributor.authorChen, Q.-
dc.contributor.authorGuan, T.-
dc.contributor.authorYun, L.-
dc.contributor.authorLi, R.-
dc.contributor.authorRecknagel, F.-
dc.date.issued2015-
dc.identifier.citationHarmful Algae, 2015; 43:58-65-
dc.identifier.issn1568-9883-
dc.identifier.issn1878-1470-
dc.identifier.urihttp://hdl.handle.net/2440/105055-
dc.description.abstractAbstract not available-
dc.description.statementofresponsibilityQiuwen Chen, Tiesheng Guan, Liu Yun, Ruonan Li, Friedrich Recknagel-
dc.language.isoen-
dc.publisherElsevier-
dc.rights© 2015 Elsevier B.V. All rights reserved.-
dc.source.urihttp://dx.doi.org/10.1016/j.hal.2015.01.002-
dc.subjectAlgal bloom; ARIMA model; MVLR model; online early warning-
dc.titleOnline forecasting chlorophyll a concentrations by an auto-regressive integrated moving average model: feasibilities and potentials-
dc.typeJournal article-
dc.identifier.doi10.1016/j.hal.2015.01.002-
pubs.publication-statusPublished-
dc.identifier.orcidRecknagel, F. [0000-0002-1028-9413]-
Appears in Collections:Aurora harvest 8
Earth and Environmental Sciences publications

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