Metro-environmental data approach for the prediction of chemical oxygen demand in new nicosia wastewater treatment plant

This study aimed at employing three data-driven models, namely the Hammerstein–Weiner (HW) model, support vector machine (SVM), and feedforward back propagation neural network (FFBPNN) and traditional multi-linear regression, as well as two non-linear ensemble techniques viz: HW-ensemble and FFBPNN-ensemble, were employed to predict chemical oxygen demand (COD_eff ). For the prediction of the COD_eff, two types of data were used, the first one being environmental data from the new Nicosia waste water treatment plant conductivity (Cond_inf ), including total nitrogen (TN_inf ), total phosphorus (TP_inf ) and one-effluent parameter COD_eff as M1, where the second was meteorology data from the National Aeronautics and Space Administration (NASA) (at 2 m above the Earth’s surface), such as relative humidity (R2H), maximum temperature (T2M_M) and mean temperature (T2M) as M2, in a hybrid model M3, which was a combination of both the meteorology and environmental data M1 and M2. According to the performance criteria RMSE and DC of the single models, values of HW-M1 (0.0308 and 0.9686), HW-M2 (0.0322 and 0.9093) and SVM-M3 (0.025 and 0.9486) were recorded. The ensemble technique improved the performance of the single models in the verification phase by 12% and 19% for HW-E and FFBPNN-E, respectively.