Multi-parametric modeling of water treatment plant using AI-based non-linear ensemble

In this research, general regression neural network (GRNN), Hammerstein-wiener (HW) and nonlinear autoregressive with exogenous (NARX) neural network, least square support vector machine (LSSVM) models were employed for multi-parametric (Hardness (mg/L), turbidity (Turb) (μs/cm), pH and suspended solid (SS) (mg/L)) modeling of Tamburawa water treatment plant (TWTP) at Kano, Nigeria. The weekly data from the TWTP were used and the predictive models were evaluated based on several numerical indicators. Four different non-linear ensemble techniques (GRNN-E, HW-E, NARX-E, and LSSVM-E) were subsequently employed. The comparison of the results of modeling showed that HW served as the best model for the simulation of Hardness, Turb, and SS while the NARX model demonstrated high capability in predicting pH. Yet, the HW and NARX as system identification techniques attained best overall predictive performance among the four modeling approaches. The HW model offers, therefore, a reliable and intelligent approach for predicting the treated Hardness, Turb, and SS and should be explored as a predictive tool in TWTP. Among the nonlinear ensemble models, GRNN-E proved of high merit and increased the accuracy of the best single models significantly up to 30% for Hardness and Turb, 34% for pH, and 37% for SS with regards to the single models.