Publication:
Evaluation of phytoplankton composition of the pelagic region of do? Anci dam reservoir (Bursa, Turkey) by artificial neural network (ANN) and clustering technique

dc.contributor.authorÖzengin, Nihan
dc.contributor.buuauthorÖZENGİN, NİHAN
dc.contributor.departmentUludağ Üniversitesi/Mühendislik Fakültesi/Çevre Mühendisliği Bölümü
dc.contributor.researcheridAAH-1475-2021
dc.date.accessioned2024-10-04T11:54:16Z
dc.date.available2024-10-04T11:54:16Z
dc.date.issued2022-12-07
dc.description.abstractThis study was carried out in four stations in the Doganci Dam Reservoir in the northwestern part of the Anatolian Region of Turkey. Within this study, phytoplankton composition were evaluated by using artificial neural network and clustering technique. For this purpose, phytoplankton algal flora and some physico-chemical parameters were investigated in water samples taken from four different stations in Doganci Dam Reservoir. A total of 75 taxa belonging to the divisions of Bacillariophyceae (45), Chlorophyceae (12), Cyanophyceae (12), Dinophyceae (2), Chrysophyceae (2) and Euglenophyceae (2) were detected in the algal flora of the pelagic region. In terms of species diversity in the phytoplankton, Bacillariophyceae members were dominant, followed by Chlorophyceae and Cyanophyceae members. As a result of the research, the type list determined is the first report on the phytoplankton composition of the dam reservoir and it is thought to be beneficial in terms of future water quality and water pollution research. Cluster analysis is a classification method that is used to arrange a set of form into clusters. The aim of this method is to classify a set of clusters such that cases within a cluster are more similar to each other and to submit summary information of the data to researchers. For predicting phytoplankton biomass, in this study, ANN was combined with a clustering technique. This case study demonstrated the good performance of ANN models in describing phytoplankton dynamics, and the potential of coupling ANN with a clustering technique to describe the spatial heterogeneity of natural ecosystems.
dc.identifier.doi10.15666/aeer/2101_823833
dc.identifier.eissn1785-0037
dc.identifier.endpage833
dc.identifier.issn1589-1623
dc.identifier.startpage823
dc.identifier.urihttps://doi.org/10.15666/aeer/2101_823833
dc.identifier.urihttps://aloki.hu/pdf/2101_823833.pdf
dc.identifier.urihttps://hdl.handle.net/11452/45889
dc.identifier.wos000925837200001
dc.indexed.wosWOS.SCI
dc.language.isoen
dc.publisherAloki Applied Ecological Research and Forensic Inst Ltd
dc.relation.journalApplied Ecology and Environmental Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectLake
dc.subjectImpact
dc.subjectWater
dc.subjectBacillariophyta
dc.subjectClustering technique
dc.subjectPhytoplankton
dc.subjectTurkey
dc.subjectScience & technology
dc.subjectLife sciences & biomedicine
dc.subjectEcology
dc.subjectEnvironmental sciences
dc.titleEvaluation of phytoplankton composition of the pelagic region of do? Anci dam reservoir (Bursa, Turkey) by artificial neural network (ANN) and clustering technique
dc.typeArticle
dc.typeEarly Access
dspace.entity.typePublication
relation.isAuthorOfPublicationdbddf8c5-4d13-49e1-93a0-967e66ae3169
relation.isAuthorOfPublication.latestForDiscoverydbddf8c5-4d13-49e1-93a0-967e66ae3169

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