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Article: Genetic programming for analysis and real-time prediction of coastal algal blooms

TitleGenetic programming for analysis and real-time prediction of coastal algal blooms
Authors
KeywordsData-driven models
Genetic programming
Harmful algal blooms
Hong Kong
Real-time prediction
Red tides
Water quality modelling
Issue Date2005
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/ecolmodel
Citation
Ecological Modelling, 2005, v. 189 n. 3-4, p. 363-376 How to Cite?
AbstractHarmful algal blooms (HAB) have been widely reported and have become a serious environmental problem world wide due to its negative impacts to aquatic ecosystems, fisheries, and human health. A capability to predict the occurrence of algal blooms with an acceptable accuracy and lead-time would clearly be very beneficial to fisheries and environmental management. In this study, we present the first real-time modelling and prediction of algal blooms using a data driven evolutionary algorithm, Genetic Programming (GP). The daily prediction of the algal blooms is carried out at Kat O station in Hong Kong using 3 years of high frequency (two-hourly) chlorophyll fluorescence and related hydro-meteorological and water quality data. The results for the prediction of chlorophyll fluorescence, a measure of algal biomass, are within reasonable accuracy for a lead-time of up to 1 day. The results generally concur with those obtained with artificial neural network. As compared to traditional data-driven models, GP has the advantage of evolving an equation relating input and output variables. A detailed analysis of the results of the GP models shows that GP not only correctly identifies the key input variables in accordance with ecological reasoning, but also demonstrates the relationship between the auto-regressive nature of bloom dynamics and flushing time. This study shows GP to be a viable alternative for algal bloom modelling and prediction; the interpretation of the results is greatly facilitated by the analytical form of the evolved equations. © 2005 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/70886
ISSN
2015 Impact Factor: 2.275
2015 SCImago Journal Rankings: 1.098
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorMuttil, Nen_HK
dc.contributor.authorLee, JHWen_HK
dc.date.accessioned2010-09-06T06:26:59Z-
dc.date.available2010-09-06T06:26:59Z-
dc.date.issued2005en_HK
dc.identifier.citationEcological Modelling, 2005, v. 189 n. 3-4, p. 363-376en_HK
dc.identifier.issn0304-3800en_HK
dc.identifier.urihttp://hdl.handle.net/10722/70886-
dc.description.abstractHarmful algal blooms (HAB) have been widely reported and have become a serious environmental problem world wide due to its negative impacts to aquatic ecosystems, fisheries, and human health. A capability to predict the occurrence of algal blooms with an acceptable accuracy and lead-time would clearly be very beneficial to fisheries and environmental management. In this study, we present the first real-time modelling and prediction of algal blooms using a data driven evolutionary algorithm, Genetic Programming (GP). The daily prediction of the algal blooms is carried out at Kat O station in Hong Kong using 3 years of high frequency (two-hourly) chlorophyll fluorescence and related hydro-meteorological and water quality data. The results for the prediction of chlorophyll fluorescence, a measure of algal biomass, are within reasonable accuracy for a lead-time of up to 1 day. The results generally concur with those obtained with artificial neural network. As compared to traditional data-driven models, GP has the advantage of evolving an equation relating input and output variables. A detailed analysis of the results of the GP models shows that GP not only correctly identifies the key input variables in accordance with ecological reasoning, but also demonstrates the relationship between the auto-regressive nature of bloom dynamics and flushing time. This study shows GP to be a viable alternative for algal bloom modelling and prediction; the interpretation of the results is greatly facilitated by the analytical form of the evolved equations. © 2005 Elsevier 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.subjectData-driven modelsen_HK
dc.subjectGenetic programmingen_HK
dc.subjectHarmful algal bloomsen_HK
dc.subjectHong Kongen_HK
dc.subjectReal-time predictionen_HK
dc.subjectRed tidesen_HK
dc.subjectWater quality modellingen_HK
dc.titleGenetic programming for analysis and real-time prediction of coastal algal bloomsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0304-3800&volume=189&issue=3-4&spage=363&epage=376&date=2005&atitle=Genetic+programming+for+analysis+and+real-time+prediction+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/j.ecolmodel.2005.03.018en_HK
dc.identifier.scopuseid_2-s2.0-27744586803en_HK
dc.identifier.hkuros118039en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-27744586803&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume189en_HK
dc.identifier.issue3-4en_HK
dc.identifier.spage363en_HK
dc.identifier.epage376en_HK
dc.identifier.isiWOS:000233798200009-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridMuttil, N=6508237745en_HK
dc.identifier.scopusauthoridLee, JHW=36078318900en_HK
dc.identifier.citeulike3851821-

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