Monitoring Coastal Chlorophyll-a Concentrations in Coastal Areas Using Machine Learning Models SCIE SCOPUS

DC Field Value Language
dc.contributor.author Kwon, Yong Sung -
dc.contributor.author Baek, Seung Ho -
dc.contributor.author Lim, Young Kyun -
dc.contributor.author Pyo, JongCheol -
dc.contributor.author Ligaray, Mayzonee -
dc.contributor.author Park, Yongeun -
dc.contributor.author Cho, Kyung Hwa -
dc.date.accessioned 2020-04-16T08:40:22Z -
dc.date.available 2020-04-16T08:40:22Z -
dc.date.created 2020-01-28 -
dc.date.issued 2018-08 -
dc.identifier.issn 2073-4441 -
dc.identifier.uri https://sciwatch.kiost.ac.kr/handle/2020.kiost/861 -
dc.description.abstract Harmful algal blooms have negatively affected the aquaculture industry and aquatic ecosystems globally. Remote sensing using satellite sensor systems has been applied on large spatial scales with high temporal resolutions for effective monitoring of harmful algal blooms in coastal waters. However, oceanic color satellites have limitations, such as low spatial resolution of sensor systems and the optical complexity of coastal waters. In this study, bands 1 to 4, obtained from Landsat-8 Operational Land Imager satellite images, were used to evaluate the performance of empirical ocean chlorophyll algorithms using machine learning techniques. Artificial neural network and support vector machine techniques were used to develop an optimal chlorophyll-a model. Four-band, four-band-ratio, and mixed reflectance datasets were tested to select the appropriate input dataset for estimating chlorophyll-a concentration using the two machine learning models. While the ocean chlorophyll algorithm application on Landsat-8 Operational Land Imager showed relatively low performance, the machine learning methods showed improved performance during both the training and validation steps. The artificial neural network and support vector machine demonstrated a similar level of prediction accuracy. Overall, the support vector machine showed slightly superior performance to that of the artificial neural network during the validation step. This study provides practical information about effective monitoring systems for coastal algal blooms. -
dc.description.uri 1 -
dc.language English -
dc.publisher MDPI -
dc.subject HARMFUL ALGAL BLOOMS -
dc.subject ARTIFICIAL NEURAL-NETWORK -
dc.subject WATER-QUALITY PARAMETERS -
dc.subject RED TIDES -
dc.subject COCHLODINIUM-POLYKRIKOIDES -
dc.subject ECOLOGICAL ROLES -
dc.subject SATELLITE DATA -
dc.subject INLAND WATERS -
dc.subject KOREA -
dc.subject PREDICTION -
dc.title Monitoring Coastal Chlorophyll-a Concentrations in Coastal Areas Using Machine Learning Models -
dc.type Article -
dc.citation.title WATER -
dc.citation.volume 10 -
dc.citation.number 8 -
dc.contributor.alternativeName 백승호 -
dc.contributor.alternativeName 임영균 -
dc.identifier.bibliographicCitation WATER, v.10, no.8 -
dc.identifier.doi 10.3390/w10081020 -
dc.identifier.scopusid 2-s2.0-85051121454 -
dc.identifier.wosid 000448462700054 -
dc.type.docType Article -
dc.description.journalClass 1 -
dc.subject.keywordPlus HARMFUL ALGAL BLOOMS -
dc.subject.keywordPlus ARTIFICIAL NEURAL-NETWORK -
dc.subject.keywordPlus WATER-QUALITY PARAMETERS -
dc.subject.keywordPlus RED TIDES -
dc.subject.keywordPlus COCHLODINIUM-POLYKRIKOIDES -
dc.subject.keywordPlus ECOLOGICAL ROLES -
dc.subject.keywordPlus SATELLITE DATA -
dc.subject.keywordPlus INLAND WATERS -
dc.subject.keywordPlus KOREA -
dc.subject.keywordPlus PREDICTION -
dc.subject.keywordAuthor harmful algal blooms -
dc.subject.keywordAuthor remote sensing -
dc.subject.keywordAuthor Landsat-8 Operational Land Imager -
dc.subject.keywordAuthor machine learning -
dc.relation.journalWebOfScienceCategory Water Resources -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.relation.journalResearchArea Water Resources -
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