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Predicting Diel, Diurnal and Nocturnal Dynamics of Dissolved Oxygen and Chlorophyll‐ a Using Regression Models and Neural Networks
Author(s) -
Karakaya Nusret,
Evrendilek Fatih,
Gungor Kerem,
Onal Deniz
Publication year - 2013
Publication title -
clean – soil, air, water
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.444
H-Index - 66
eISSN - 1863-0669
pISSN - 1863-0650
DOI - 10.1002/clen.201200683
Subject(s) - diel vertical migration , nocturnal , environmental science , chlorophyll a , artificial neural network , multilayer perceptron , atmospheric sciences , ecology , biology , machine learning , computer science , botany , geology
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 ( $r_{{\rm adj}}^{2} = 68.8\%$ ). 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.