1 Ocak 2013 Dergi makalesi Açık Erişim

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

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{
  "@context": "https://schema.org/", 
  "@id": 13747, 
  "@type": "ScholarlyArticle", 
  "creator": [
    {
      "@type": "Person", 
      "affiliation": "Abant Izzet Baysal Univ, Dept Environm Engn, TR-14280 Bolu, Turkey", 
      "name": "Karakaya, Nusret"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Abant Izzet Baysal Univ, Dept Environm Engn, TR-14280 Bolu, Turkey", 
      "name": "Evrendilek, Fatih"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Abant Izzet Baysal Univ, Dept Environm Engn, TR-14280 Bolu, Turkey", 
      "name": "Gungor, Kerem"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Middle E Tech Univ, Dept Biol, TR-06531 Ankara, Turkey", 
      "name": "Onal, Deniz"
    }
  ], 
  "datePublished": "2013-01-01", 
  "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.", 
  "headline": "Predicting Diel, Diurnal and Nocturnal Dynamics of Dissolved Oxygen and Chlorophyll-a Using Regression Models and Neural Networks", 
  "identifier": 13747, 
  "image": "https://aperta.ulakbim.gov.tr/static/img/logo/aperta_logo_with_icon.svg", 
  "license": "http://www.opendefinition.org/licenses/cc-by", 
  "name": "Predicting Diel, Diurnal and Nocturnal Dynamics of Dissolved Oxygen and Chlorophyll-a Using Regression Models and Neural Networks", 
  "url": "https://aperta.ulakbim.gov.tr/record/13747"
}