1 Ocak 2013 Dergi makalesi Açık Erişim

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

Citation Style Language JSON

{
  "DOI": "10.1002/clen.201200683", 
  "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.", 
  "author": [
    {
      "family": "Karakaya", 
      "given": " Nusret"
    }, 
    {
      "family": "Evrendilek", 
      "given": " Fatih"
    }, 
    {
      "family": "Gungor", 
      "given": " Kerem"
    }, 
    {
      "family": "Onal", 
      "given": " Deniz"
    }
  ], 
  "container_title": "CLEAN-SOIL AIR WATER", 
  "id": "13747", 
  "issue": "9", 
  "issued": {
    "date-parts": [
      [
        2013, 
        1, 
        1
      ]
    ]
  }, 
  "page": "872-877", 
  "title": "Predicting Diel, Diurnal and Nocturnal Dynamics of Dissolved Oxygen and Chlorophyll-a Using Regression Models and Neural Networks", 
  "type": "article-journal", 
  "volume": "41"
}