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

Sani I. Abba (), Quoc Bao Pham (), Anurag Malik (), Romulus Costache (), Muhammad Sani Gaya (), Jazuli Abdullahi (), Sagiru Mati (), A. G. Usman () and Gaurav Saini ()
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Sani I. Abba: Prince Mohammad Bin Fahd University
Quoc Bao Pham: University of Silesia in Katowice
Anurag Malik: Punjab Agricultural University, Regional Research Station
Romulus Costache: Department of Civil Engineering, Transilvania University of Brasov
Muhammad Sani Gaya: Kano University of Science and Technology
Jazuli Abdullahi: Baze University
Sagiru Mati: Near East University
A. G. Usman: Near East University
Gaurav Saini: Netaji Subhas University Technology

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 3, No 20, 1377-1401

Abstract: Abstract 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.

Keywords: Water quality parameters; ELM; Metaheuristic algorithms; TWTP; Nigeria (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11269-024-04027-z

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