Abstract:
: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).
journal_name
Environ Monit Assessjournal_title
Environmental monitoring and assessmentauthors
Heddam Sdoi
10.1007/s10661-014-3971-7subject
Has Abstractpub_date
2014-11-01 00:00:00pages
7837-48issue
11eissn
0167-6369issn
1573-2959journal_volume
186pub_type
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