Dergi makalesi Açık Erişim
Karakaya, Nusret; Evrendilek, Fatih; Gungor, Kerem; Onal, Deniz
<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> <dc:creator>Karakaya, Nusret</dc:creator> <dc:creator>Evrendilek, Fatih</dc:creator> <dc:creator>Gungor, Kerem</dc:creator> <dc:creator>Onal, Deniz</dc:creator> <dc:date>2013-01-01</dc:date> <dc:description>Human-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.</dc:description> <dc:identifier>https://aperta.ulakbim.gov.trrecord/13747</dc:identifier> <dc:identifier>oai:zenodo.org:13747</dc:identifier> <dc:rights>info:eu-repo/semantics/openAccess</dc:rights> <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights> <dc:source>CLEAN-SOIL AIR WATER 41(9) 872-877</dc:source> <dc:title>Predicting Diel, Diurnal and Nocturnal Dynamics of Dissolved Oxygen and Chlorophyll-a Using Regression Models and Neural Networks</dc:title> <dc:type>info:eu-repo/semantics/article</dc:type> <dc:type>publication-article</dc:type> </oai_dc:dc>
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