Global parameter estimation of the Cochlodinium polykrikoides model using bioassay data SCIE SCOPUS

DC Field Value Language
dc.contributor.author Hong-Yeon, Cho -
dc.contributor.author Kwang-Soon, Park -
dc.contributor.author Sung, Kim -
dc.date.accessioned 2020-04-20T02:40:42Z -
dc.date.available 2020-04-20T02:40:42Z -
dc.date.created 2020-01-28 -
dc.date.issued 2016-02 -
dc.identifier.issn 0253-505X -
dc.identifier.uri https://sciwatch.kiost.ac.kr/handle/2020.kiost/2252 -
dc.description.abstract Cochlodinium polykrikoides is a notoriously harmful algal species that inflicts severe damage on the aquacultures of the coastal seas of Korea and Japan. Information on their expected movement tracks and boundaries of influence is very useful and important for the effective establishment of a reduction plan. In general, the information is supported by a red-tide (a.k.a algal bloom) model. The performance of the model is highly dependent on the accuracy of parameters, which are the coefficients of functions approximating the biological growth and loss patterns of the C. polykrikoides. These parameters have been estimated using the bioassay data composed of growth-limiting factor and net growth rate value pairs. In the case of the C. polykrikoides, the parameters are different from each other in accordance with the used data because the bioassay data are sufficient compared to the other algal species. The parameters estimated by one specific dataset can be viewed as locally-optimized because they are adjusted only by that dataset. In cases where the other one data set is used, the estimation error might be considerable. In this study, the parameters are estimated by all available data sets without the use of only one specific data set and thus can be considered globally optimized. The cost function for the optimization is defined as the integrated mean squared estimation error, i.e., the difference between the values of the experimental and estimated rates. Based on quantitative error analysis, the root-mean squared errors of the global parameters show smaller values, approximately 25%-50%, than the values of the local parameters. In addition, bias is removed completely in the case of the globally estimated parameters. The parameter sets can be used as the reference default values of a red-tide model because they are optimal and representative. However, additional tuning of the parameters using the in-situ monitoring data is highly required. As opposed to the bioassay data, it is necessary because the bioassay data have limitations in terms of the in-situ coastal conditions. -
dc.description.uri 1 -
dc.language English -
dc.publisher SPRINGER -
dc.subject HARMFUL ALGAL BLOOMS -
dc.subject MARGALEF DINOPHYCEAE -
dc.subject GROWTH -
dc.subject DINOFLAGELLATE -
dc.title Global parameter estimation of the Cochlodinium polykrikoides model using bioassay data -
dc.type Article -
dc.citation.endPage 45 -
dc.citation.startPage 39 -
dc.citation.title ACTA OCEANOLOGICA SINICA -
dc.citation.volume 35 -
dc.citation.number 2 -
dc.contributor.alternativeName 조홍연 -
dc.contributor.alternativeName 박광순 -
dc.contributor.alternativeName 김성 -
dc.identifier.bibliographicCitation ACTA OCEANOLOGICA SINICA, v.35, no.2, pp.39 - 45 -
dc.identifier.doi 10.1007/s13131-016-0806-0 -
dc.identifier.scopusid 2-s2.0-84959346219 -
dc.identifier.wosid 000370614700005 -
dc.type.docType Article -
dc.description.journalClass 1 -
dc.subject.keywordPlus HARMFUL ALGAL BLOOMS -
dc.subject.keywordPlus MARGALEF DINOPHYCEAE -
dc.subject.keywordPlus GROWTH -
dc.subject.keywordPlus DINOFLAGELLATE -
dc.subject.keywordAuthor global and local estimation -
dc.subject.keywordAuthor gain and loss parameters -
dc.subject.keywordAuthor Cochlodinium polykrikoides -
dc.subject.keywordAuthor bioassay data -
dc.subject.keywordAuthor model performance -
dc.relation.journalWebOfScienceCategory Oceanography -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.relation.journalResearchArea Oceanography -
Appears in Collections:
Marine Digital Resources Department > Marine Bigdata & A.I. Center > 1. Journal Articles
Ocean Climate Solutions Research Division > Ocean Climate Response & Ecosystem Research Department > 1. Journal Articles
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