Evaluation of environmental factors on cyanobacterial bloom in eutrophic reservoir using artificial neural networks

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dc.contributor.authorChi-Yong Ahn-
dc.contributor.authorHee-Mock Oh-
dc.contributor.authorY S Park-
dc.date.accessioned2017-04-19T09:23:33Z-
dc.date.available2017-04-19T09:23:33Z-
dc.date.issued2011-
dc.identifier.issn0022-3646-
dc.identifier.uri10.1111/j.1529-8817.2011.00990.xko
dc.identifier.urihttps://oak.kribb.re.kr/handle/201005/10168-
dc.description.abstractCyanobacterial blooms are a common issue in eutrophic freshwaters, and some cyanobacteria produce toxins, threatening the health of humans and livestock. Microcystin, a representative cyanobacterial hepatotoxin, is frequently detected in most Korean lakes and reservoirs. This study developed predictive models for cyanobacterial bloom using artificial neural networks (ANNs; self-organizing map [SOM] and multilayer perceptron [MLP]), including an evaluation of related environmental factors. Fourteen environmental factors, as independent variables for predicting the cyanobacteria density, were measured weekly in the Daechung Reservoir from spring to autumn over 5years (2001, 2003-2006). Cyanobacterial density was highly associated with environmental factors measured 3weeks earlier. The SOM model was efficient in visualizing the relationships between cyanobacteria and environmental factors, and also for tracing temporal change patterns in the environmental condition of the reservoir. And the MLP model exhibited a good predictive power for the cyanobacterial density, based on the environmental factors of 3weeks earlier. The water temperature and total dissolved nitrogen were the major determinants for cyanobacteria. The water temperature had a stronger influence on cyanobacterial growth than the nutrient concentrations in eutrophic waters. Contrary to general expectations, the nitrogen compounds played a more important role in bloom formation than the phosphorus compounds.-
dc.publisherWiley-
dc.titleEvaluation of environmental factors on cyanobacterial bloom in eutrophic reservoir using artificial neural networks-
dc.title.alternativeEvaluation of environmental factors on cyanobacterial bloom in eutrophic reservoir using artificial neural networks-
dc.typeArticle-
dc.citation.titleJournal of Phycology-
dc.citation.number3-
dc.citation.endPage504-
dc.citation.startPage495-
dc.citation.volume47-
dc.contributor.affiliatedAuthorChi-Yong Ahn-
dc.contributor.affiliatedAuthorHee-Mock Oh-
dc.contributor.alternativeName안치용-
dc.contributor.alternativeName오희목-
dc.contributor.alternativeName박영석-
dc.identifier.bibliographicCitationJournal of Phycology, vol. 47, no. 3, pp. 495-504-
dc.identifier.doi10.1111/j.1529-8817.2011.00990.x-
dc.subject.keywordArtificial neural network-
dc.subject.keywordBloom-
dc.subject.keywordCyanobacteria-
dc.subject.keywordMultilayer perceptron-
dc.subject.keywordPrediction model-
dc.subject.keywordSelf-organizing map-
dc.subject.localArtificial neural network-
dc.subject.localbloom-
dc.subject.localBloom-
dc.subject.localCyanobacteria-
dc.subject.localMultilayer perceptron-
dc.subject.localprediction model-
dc.subject.localPrediction model-
dc.subject.localSelf-organizing map-
dc.description.journalClassY-
Appears in Collections:
Synthetic Biology and Bioengineering Research Institute > Cell Factory Research Center > 1. Journal Articles
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