Journal
ENVIRONMENTAL MONITORING AND ASSESSMENT
Volume 186, Issue 11, Pages 7837-7848Publisher
SPRINGER
DOI: 10.1007/s10661-014-3971-7
Keywords
Generalized regression neural networks; GRNN; Multiple linear regression; MLR; Colored dissolved organic matter; CDOM; Modeling
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The prediction of colored dissolved organic matter (CDOM) using artificial neural network approaches has received little attention in the past few decades. In this study, colored dissolved organic matter (CDOM) was modeled using generalized regression neural network (GRNN) and multiple linear regression (MLR) models as a function of Water temperature (TE), pH, specific conductance (SC), and turbidity (TU). Evaluation of the prediction accuracy of the models is based on the root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (CC), and Willmott's index of agreement (d). The results indicated that GRNN can be applied successfully for prediction of colored dissolved organic matter (CDOM).
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