Modelling Colored Dissolved Organic Matter (CDOM) using Neuro Fuzzy Technique: a Comparative Study

Abstract

Colored dissolved organic matter (CDOM) is part of the dissolved organic matter (DOM), which can be mainly divided into two groups-natural organic matter (NOM) and anthropogenic organic matter. With two other components, chlorophyll and non-algal particles (NAP), CDOM plays an important role in determining photochemical characteristics of water in nature. The prediction of colored dissolved organic matter (CDOM) using artificial intelligence techniques (AI) has received little attention in the past few decades. In this study, colored dissolved organic matter (CDOM) was modelled using Adaptive Neuro-Fuzzy Inference Systems (ANFIS) 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 ANFIS can be applied successfully for prediction of colored dissolved organic matter (CDOM). In both models, 60 % of the data set was randomly assigned to the training set, 20 % to the validation set, and 20 % to the test set. The system proposed in this paper shows a novelty approach with regard to the usage of ANFIS models for colored dissolved organic matter (CDOM) concentration modelling.