1 Ocak 2013 Dergi makalesi Açık Erişim

Karakaya, Nusret; Evrendilek, Fatih; Gungor, Kerem; Onal, Deniz

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/13747</identifier>
  <creators>
    <creator>
      <creatorName>Karakaya, Nusret</creatorName>
      <givenName>Nusret</givenName>
      <familyName>Karakaya</familyName>
      <affiliation>Abant Izzet Baysal Univ, Dept Environm Engn, TR-14280 Bolu, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Evrendilek, Fatih</creatorName>
      <givenName>Fatih</givenName>
      <familyName>Evrendilek</familyName>
      <affiliation>Abant Izzet Baysal Univ, Dept Environm Engn, TR-14280 Bolu, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Gungor, Kerem</creatorName>
      <givenName>Kerem</givenName>
      <familyName>Gungor</familyName>
      <affiliation>Abant Izzet Baysal Univ, Dept Environm Engn, TR-14280 Bolu, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Onal, Deniz</creatorName>
      <givenName>Deniz</givenName>
      <familyName>Onal</familyName>
      <affiliation>Middle E Tech Univ, Dept Biol, TR-06531 Ankara, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Predicting Diel, Diurnal And Nocturnal Dynamics Of Dissolved Oxygen And Chlorophyll-A Using Regression Models And Neural Networks</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2013</publicationYear>
  <dates>
    <date dateType="Issued">2013-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/13747</alternateIdentifier>
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  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1002/clen.201200683</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">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.</description>
  </descriptions>
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