Optimization of Extreme Learning Machine with Metaheuristic Algorithms for Modelling Water Quality Parameters of Tamburawa Water Treatment Plant in Nigeria

Sustainable management of available water resources needs robust and reliable intelligent tools to address emerging water challenges. These days, artificial intelligence (AI) based tools are more efficient and prominent in addressing issues related to water treatment plants. Therefore, in the current study, the extreme learning machine (ELM) was optimized with four different metaheuristic algorithms, namely particle swarm optimization (PSO-ELM), genetic algorithm (GA-ELM), biogeography-based optimization (BBO-ELM), and BBO-PSO-ELM for modelling treated water quality parameters, i.e., pHT, Turbidity (TurbT), total dissolved solids (TDST), and HardnessT of Tamburawa water treatment plant (TWTP) located in Nigeria. The performance of the hybrid ELM models was evaluated using mean absolute error (MAE), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC), and Willmott index (WI) as well as graphically. The obtained numerical and visualized results indicate that the BBO-PSO-ELM model performed superior in modeling pHT (MAE = 0.403, RMSE = 0.514, NSE = 0.863, PCC = 0.935, WI = 0.964), TDST (MAE = 11.818 mg/L, RMSE = 16.058 mg/L, NSE = 0.711, PCC = 0.853, WI = 0.923), and HardnessT (MAE = 2.624 mg/L, RMSE = 3.497 mg/L, NSE = 0.818, PCC = 0.909, WI = 0.947), while BBO-ELM demonstrated superior performance in TurbT (MAE = 0.385 mg/L, RMSE = 0.694 mg/L, NSE = 0.996, PCC = 0.999, WI = 0.999) modelling. Generally, the findings suggested that the proposed hybrid ELM model has the potential to predict the water quality parameters of TWTP in Nigeria effectively.