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Article: Modelling algal blooms using vector autoregressive model with exogenous variables and long memory filter

TitleModelling algal blooms using vector autoregressive model with exogenous variables and long memory filter
Authors
KeywordsAlgal blooms
Early warning system
Long range dependence
Red-tide
Time series forecasting
VARX modelling
Issue Date2007
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/ecolmodel
Citation
Ecological Modelling, 2007, v. 200 n. 1-2, p. 130-138 How to Cite?
AbstractAlgal blooms (ABs), which commonly occur in urbanised coastal marine environments worldwide, often result in hypoxia and even fish kills. Understanding the mechanism and providing accurate prediction of ABs' formation and occurrence is of foremost importance in relation to the protection of sensitive marine resources. In this paper, a multivariate time series model, namely the vector autoregressive model with exogenous variables (VARX) and the long memory filter is proposed to model and predict ABs. To evaluate the effectiveness of this VARX model, both daily and 2-h field monitoring data of chlorophyll fluorescence (CHL), dissolved oxygen (DO), total inorganic nitrogen (TIN), water temperature (TEMP), solar radiation (SR) and wind speed (WS) obtained at Kat O, Hong Kong, between February 2000 and March 2003 were employed. Unlike the other data driven approaches, this VARX model not only provides more interpretable effects of specific lags of environmental factors, but also sheds light on the feedback effects of AB on these variables. In general, daily CHL measurements up to 4 days can provide crucial information for predicting algal dynamics, while the VARX model is able to explicitly reveal ecological relationships between CHL and other environmental factors. In addition, the application of long-memory filter can further extract patterns of seasonal variations which is thought to be correspondent to the variation of algal species in Hong Kong water. With a view to providing an early warning signal of AB to fishermen and regulatory authorities, an alarming system was developed based on the VARX model; it could achieve 83% correct prediction of AB occurrences with a lead time of 2.5 days. Concerning the forecast performance of the VARX model, daily forecasting performance is comparatively better than that of artificial neural network models. © 2006 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/172442
ISSN
2015 Impact Factor: 2.275
2015 SCImago Journal Rankings: 1.098
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLui, GCSen_HK
dc.contributor.authorLi, WKen_HK
dc.contributor.authorLeung, KMYen_HK
dc.contributor.authorLee, JHWen_HK
dc.contributor.authorJayawardena, AWen_HK
dc.date.accessioned2012-10-30T06:22:33Z-
dc.date.available2012-10-30T06:22:33Z-
dc.date.issued2007en_HK
dc.identifier.citationEcological Modelling, 2007, v. 200 n. 1-2, p. 130-138en_HK
dc.identifier.issn0304-3800en_HK
dc.identifier.urihttp://hdl.handle.net/10722/172442-
dc.description.abstractAlgal blooms (ABs), which commonly occur in urbanised coastal marine environments worldwide, often result in hypoxia and even fish kills. Understanding the mechanism and providing accurate prediction of ABs' formation and occurrence is of foremost importance in relation to the protection of sensitive marine resources. In this paper, a multivariate time series model, namely the vector autoregressive model with exogenous variables (VARX) and the long memory filter is proposed to model and predict ABs. To evaluate the effectiveness of this VARX model, both daily and 2-h field monitoring data of chlorophyll fluorescence (CHL), dissolved oxygen (DO), total inorganic nitrogen (TIN), water temperature (TEMP), solar radiation (SR) and wind speed (WS) obtained at Kat O, Hong Kong, between February 2000 and March 2003 were employed. Unlike the other data driven approaches, this VARX model not only provides more interpretable effects of specific lags of environmental factors, but also sheds light on the feedback effects of AB on these variables. In general, daily CHL measurements up to 4 days can provide crucial information for predicting algal dynamics, while the VARX model is able to explicitly reveal ecological relationships between CHL and other environmental factors. In addition, the application of long-memory filter can further extract patterns of seasonal variations which is thought to be correspondent to the variation of algal species in Hong Kong water. With a view to providing an early warning signal of AB to fishermen and regulatory authorities, an alarming system was developed based on the VARX model; it could achieve 83% correct prediction of AB occurrences with a lead time of 2.5 days. Concerning the forecast performance of the VARX model, daily forecasting performance is comparatively better than that of artificial neural network models. © 2006 Elsevier B.V. All rights reserved.en_HK
dc.languageengen_US
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/ecolmodelen_HK
dc.relation.ispartofEcological Modellingen_HK
dc.rightsEcological Modelling. Copyright © Elsevier BV.-
dc.subjectAlgal bloomsen_HK
dc.subjectEarly warning systemen_HK
dc.subjectLong range dependenceen_HK
dc.subjectRed-tideen_HK
dc.subjectTime series forecastingen_HK
dc.subjectVARX modellingen_HK
dc.titleModelling algal blooms using vector autoregressive model with exogenous variables and long memory filteren_HK
dc.typeArticleen_HK
dc.identifier.emailLui, GCS: csglui@hku.hken_HK
dc.identifier.emailLi, WK: hrntlwk@hkucc.hku.hken_HK
dc.identifier.emailLeung, KMY: kmyleung@hku.hken_HK
dc.identifier.emailLee, JHW: hreclhw@hku.hken_HK
dc.identifier.authorityLui, GCS=rp00755en_HK
dc.identifier.authorityLi, WK=rp00741en_HK
dc.identifier.authorityLeung, KMY=rp00733en_HK
dc.identifier.authorityLee, JHW=rp00061en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/j.ecolmodel.2006.06.017en_HK
dc.identifier.scopuseid_2-s2.0-37849189766en_HK
dc.identifier.hkuros125140-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-37849189766&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume200en_HK
dc.identifier.issue1-2en_HK
dc.identifier.spage130en_HK
dc.identifier.epage138en_HK
dc.identifier.isiWOS:000243600800011-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridLui, GCS=8613288600en_HK
dc.identifier.scopusauthoridLi, WK=14015971200en_HK
dc.identifier.scopusauthoridLeung, KMY=7401860738en_HK
dc.identifier.scopusauthoridLee, JHW=36078318900en_HK
dc.identifier.scopusauthoridJayawardena, AW=7005049253en_HK

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