A new insight for real-time wastewater quality prediction using hybridized kernel-based extreme learning machines with advanced optimization algorithms

• Main Authors: Alavi, Javad, Ewees, Ahmed A., Ansari, Sepideh, Shahid, Shamsuddin, Yaseen, Zaher Mundher
• Format: Article
• Published: Springer Science and Business Media Deutschland GmbH 2022
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://eprints.utm.my/103762/; http://dx.doi.org/10.1007/s11356-021-17190-2

Accurate prediction of inlet chemical oxygen demand (COD) is vital for better planning and management of wastewater treatment plants. The COD values at the inlet follow a complex nonstationary pattern, making its prediction challenging. This study compared the performance of several novel machine learning models developed through hybridizing kernel-based extreme learning machines (KELMs) with intelligent optimization algorithms for the reliable prediction of real-time COD values. The combined time-series learning method and consumer behaviours, estimated from water-use data (hour/day), were used as the supplementary inputs of the hybrid KELM models. Comparison of model performances for different input combinations revealed the best performance using up to 2-day lag values of COD with the other wastewater properties. The results also showed the best performance of the KELM-salp swarm algorithm (SSA) model among all the hybrid models with a minimum root mean square error of 0.058 and mean absolute error of 0.044.