Artificial neural networks predictive models. A case study: carbon and bromine concentrations prediction based on chlorination time

Artificial neural networks (ANNs) are being used increasingly to predict water variables. This study
offers an alternative approach to quantify the relationship between time of chlorination in potable
water (due to convectional treatment procedure) and chlorination by-products concentration
(expressed as carbon and bromine) with an ANN model, i.e., capturing non-linear relationships
among the water quality variables.
Thus, carbon and bromine concentrations in potable water (the second chosen due to the toxicity of
brominated trihalomethanes, THMs) were predicted using artificial neural networks (ANNs) based
mainly on multi-layer perceptrons (MLPs) architecture. The chlorination (detention) time as much as
58 hours in Athens distributed network, comprised the input variables to the ANNs models.
Moreover, to develop an ANN model for estimating carbon and bromine, the available data set was
partitioned into training, validation and test set. In order to reach an optimum amount of hidden
layers or nodes, different architectures were tested. The quality of the ANN simulations was
evaluated in terms of the error in the validation sample set for the proper interpretation of the results.
The calculated sum-squared errors for training, validation and test set were 0.056, 0.039 and 0.060
respectively for the best model selected.
Comparison of the results showed that a two-layer feed-forward back propagation ANN model could
be used as an acceptable model for predicting carbon and bromine contained in potable water
THMs.