Predicting diel, diurnal and nocturnal dynamics of dissolved oxygen and chlorophyll-a using regression models and neural networks

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dc.authorid0000-0003-1099-4363en_US
dc.authorid0000-0002-0156-1657en_US
dc.authorid0000-0002-9604-4539en_US
dc.contributor.authorKarakaya, Nusret
dc.contributor.authorEvrendilek, Fatih
dc.contributor.authorGüngör, Kerem
dc.contributor.authorÖnal, Deniz
dc.date.accessioned2021-06-23T19:34:16Z
dc.date.available2021-06-23T19:34:16Z
dc.date.issued2013
dc.departmentBAİBÜ, Mühendislik Fakültesi, Çevre Mühendisliği Bölümüen_US
dc.description.abstractHuman-induced and natural interruptions with continuous streams of observational data necessitate the development of gap-filling and prediction strategies towards better understanding, monitoring and management of aquatic systems. This study quantified the efficacy of multiple non-linear regression (MNLR) versus artificial neural network (ANN) models as well as the temporal partitioning of diurnal versus nocturnal data for the predictions of chlorophyll-a (chl-a) and dissolved oxygen (DO) dynamics. The temporal partitioning increased the predictive performances of the best MNLR models of diurnal DO by 45% and nocturnal DO by 4%, relative to the best diel MNLR model of diel DO (r(adj)(2) = 68.8%). The ANN-based predictions had a higher predictive power than the MNLR-based predictions for both chl-a and DO except for diurnal DO dynamics. The best ANNs based on independent validations were multilayer perceptron (MLP) for diel chl-a, generalized feedforward (GFF) for diurnal and nocturnal chl-a, MLP for diel DO, GFF for diurnal DO, and MLP for nocturnal DO.en_US
dc.identifier.doi10.1002/clen.201200683
dc.identifier.endpage877en_US
dc.identifier.issn1863-0650
dc.identifier.issn1863-0669
dc.identifier.issue9en_US
dc.identifier.scopus2-s2.0-84883445093en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage872en_US
dc.identifier.urihttps://doi.org/10.1002/clen.201200683
dc.identifier.urihttps://hdl.handle.net/20.500.12491/7449
dc.identifier.volume41en_US
dc.identifier.wosWOS:000327816200006en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorKarakaya, Nusret
dc.institutionauthorEvrendilek, Fatih
dc.institutionauthorGüngör, Kerem
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofClean-Soil Air Wateren_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectData-driven Modelingen_US
dc.subjectLake Water Qualityen_US
dc.subjectNon-linear Dynamicsen_US
dc.subjectTime Series Dataen_US
dc.titlePredicting diel, diurnal and nocturnal dynamics of dissolved oxygen and chlorophyll-a using regression models and neural networksen_US
dc.typeArticleen_US

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