A new insight for real-time wastewater quality prediction using hybridized kernel-based extreme learning machines with advanced optimization algorithms
2022
Alavi, Javad | Ewees, Ahmed A. | Ansari, Sepideh | Shahid, Shamsuddin | Yaseen, Zaher Mundher
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.
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