Artificial Neural Networks (ANNs) and Multiple Linear Regression (MLR) Analysis Modelling for Predicting Chemical Dosages of a Water Treatment Plant (WTP) of Drinking Water

As the quantity and quality of water resources decreases, the need for timely and valid prediction of the WTP of drinking water-used chemicals to produce quality drinking water for the final consumer increases. The question that arises is which prediction model performs better in predicting the chemical dosages used in a WTP of drinking water. ANNs or the MLR analysis models? The present study is a comparative study between the two aforementioned prediction models. The evaluation criteria chosen are: the Root Mean Square Error (RMSE), the Coefficient of Determination (R2), and the Pearson Correlation Coefficient (R). A previously optimised ensemble ANN model was chosen, which consisted of 100 neural networks, with 42 hidden nodes each, 10 inputs, and 4 outputs. On the other hand, four different scenarios in MLR analysis with dependent variables were examined: the ozone (O3) concentration, the Anionic Polyelectrolyte (ANPE) dosage, the Poly-Aluminium Chloride hydroxide sulphate (PACl) dosage, and the chlorine (Cl2(g)) dosage. As independent variables, 10 WTP operational and quality water variables were considered. According to RMSE results, the MLR model had better performance for the three (RMSE ANPE = 0.05 mg/L, RMSE PACl = 0.08 mg/L, and RMSE Cl2(g) = 0.10 kg/h) of the four used WTP of drinking water chemicals, than the ANN model, which performed better for only one (RMSE O3 = 0.02 mg/L). According to R2 and R results, the ANN model had better performance compared to the MLR analysis model for all four variables. Based on the criterion of R2 > 0.5, the ANN performance was satisfactory in predicting three variables: ANPE (R2 = 0.772), PACl (R2 = 0.742), and Cl2(g) dosage (R2 = 0.838, +23% and R = 0.91553, +11%). Respectively, the prediction of the MLR analysis model was evaluated as satisfactory only for the Cl2(g) dosage (R2 = 0.681, R = 0.82500). If someone wants to use the above described (ANN or MLR) scenarios to predict Cl2(g) dosages, it is better to use the one with the smallest RMSE. If they are interested in fitting purposes, the one with the largest R2, is preferable. Also, the ozone concentration variable showed low values of the R2, in all cases, possibly due to the large variation in its values. This study further strengthens the opinion that ANNs are useful decision support tools for a WTP of a drinking water operator and can accurately and sufficiently mimic the decisions regarding the used chemical dosages, which is the main daily concern of the plant operator.