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Article: Neural network modelling of coastal algal blooms

TitleNeural network modelling of coastal algal blooms
Authors
KeywordsAlgal blooms
Artificial neural networks
Coastal eutrophication
Data driven methods
Environmental engineering
Hong Kong
Knowledge-based models
Real time prediction
Red tides
Tolo Harbour
Water quality modelling
Issue Date2003
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/ecolmodel
Citation
Ecological Modelling, 2003, v. 159 n. 2-3, p. 179-201 How to Cite?
AbstractAn artificial neural network (ANN), a data driven modelling approach, is proposed to predict the algal bloom dynamics of the coastal waters of Hong Kong. The commonly used back-propagation learning algorithm is employed for training the ANN. The modeling is based on (a) comprehensive biweekly water quality data at Tolo Harbour (1982-2000); and (b) 4-year set of weekly phytoplankton abundance data at Lamma Island (1996-2000). Algal biomass is represented as chlorophyll-a and cell concentration of Skeletonema at the two locations, respectively. Analysis of a large number of scenarios shows that the best agreement with observations is obtained by using merely the time-lagged algal dynamics as the network input. In contrast to previous findings with more complicated neural networks of algal blooms in freshwater systems, the present work suggests the algal concentration in the eutrophic sub-tropical coastal water is mainly dependent on the antecedent algal concentrations in the previous 1-2 weeks. This finding is also supported by an interpretation of the neural networks' weights. Through a systematic analysis of network performance, it is shown that previous reports of predictability of algal dynamics by ANN are erroneous in that 'future data' have been used to drive the network prediction. In addition, a novel real time forecast of coastal algal blooms based on weekly data at Lamma is presented. Our study shows that an ANN model with a small number of input variables is able to capture trends of algal dynamics, but data with a minimum sampling interval of 1 week is necessary. However, the sufficiency of the weekly sampling for real time predictions using ANN models needs to be further evaluated against longer weekly data sets as they become available. © 2002 Elsevier Science B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/71675
ISSN
2015 Impact Factor: 2.275
2015 SCImago Journal Rankings: 1.098
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLee, JHWen_HK
dc.contributor.authorHuang, Yen_HK
dc.contributor.authorDickman, Men_HK
dc.contributor.authorJayawardena, AWen_HK
dc.date.accessioned2010-09-06T06:34:08Z-
dc.date.available2010-09-06T06:34:08Z-
dc.date.issued2003en_HK
dc.identifier.citationEcological Modelling, 2003, v. 159 n. 2-3, p. 179-201en_HK
dc.identifier.issn0304-3800en_HK
dc.identifier.urihttp://hdl.handle.net/10722/71675-
dc.description.abstractAn artificial neural network (ANN), a data driven modelling approach, is proposed to predict the algal bloom dynamics of the coastal waters of Hong Kong. The commonly used back-propagation learning algorithm is employed for training the ANN. The modeling is based on (a) comprehensive biweekly water quality data at Tolo Harbour (1982-2000); and (b) 4-year set of weekly phytoplankton abundance data at Lamma Island (1996-2000). Algal biomass is represented as chlorophyll-a and cell concentration of Skeletonema at the two locations, respectively. Analysis of a large number of scenarios shows that the best agreement with observations is obtained by using merely the time-lagged algal dynamics as the network input. In contrast to previous findings with more complicated neural networks of algal blooms in freshwater systems, the present work suggests the algal concentration in the eutrophic sub-tropical coastal water is mainly dependent on the antecedent algal concentrations in the previous 1-2 weeks. This finding is also supported by an interpretation of the neural networks' weights. Through a systematic analysis of network performance, it is shown that previous reports of predictability of algal dynamics by ANN are erroneous in that 'future data' have been used to drive the network prediction. In addition, a novel real time forecast of coastal algal blooms based on weekly data at Lamma is presented. Our study shows that an ANN model with a small number of input variables is able to capture trends of algal dynamics, but data with a minimum sampling interval of 1 week is necessary. However, the sufficiency of the weekly sampling for real time predictions using ANN models needs to be further evaluated against longer weekly data sets as they become available. © 2002 Elsevier Science B.V. All rights reserved.en_HK
dc.languageengen_HK
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.en_HK
dc.subjectAlgal bloomsen_HK
dc.subjectArtificial neural networksen_HK
dc.subjectCoastal eutrophicationen_HK
dc.subjectData driven methodsen_HK
dc.subjectEnvironmental engineeringen_HK
dc.subjectHong Kongen_HK
dc.subjectKnowledge-based modelsen_HK
dc.subjectReal time predictionen_HK
dc.subjectRed tidesen_HK
dc.subjectTolo Harbouren_HK
dc.subjectWater quality modellingen_HK
dc.titleNeural network modelling of coastal algal bloomsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0304-3800&volume=159&issue=2-3&spage=179&epage=201&date=2003&atitle=Neural+network+modelling+of+coastal+algal+bloomsen_HK
dc.identifier.emailLee, JHW: hreclhw@hku.hken_HK
dc.identifier.authorityLee, JHW=rp00061en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/S0304-3800(02)00281-8en_HK
dc.identifier.scopuseid_2-s2.0-0037438909en_HK
dc.identifier.hkuros76313en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0037438909&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume159en_HK
dc.identifier.issue2-3en_HK
dc.identifier.spage179en_HK
dc.identifier.epage201en_HK
dc.identifier.isiWOS:000180599300007-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridLee, JHW=36078318900en_HK
dc.identifier.scopusauthoridHuang, Y=7501577482en_HK
dc.identifier.scopusauthoridDickman, M=7005833020en_HK
dc.identifier.scopusauthoridJayawardena, AW=7005049253en_HK
dc.identifier.citeulike3851827-

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