Accurately predicting the characteristics of effluent, discharged from wastewater treatment
plants (WWTPs) is crucial for reducing sampling requirements, labor, costs, and …

Wastewater characteristics prediction in wastewater treatment plants (WWTPs) is valuable
and can reduce the number of sampling, energy, and cost. Feature Selection (FS) methods …

Total suspended solids (TSS) are a major pollutant that affects waterways all over the world.
Predicting the values of TSS is of interest to quality control of wastewater processing. Due to …

In the domain of water treatment, improving the efficiency of wastewater treatment plants
(WWTPs) has highlighted the need to model certain concentrations and variables …

Although the management of sewage sludge is an important and challenging task of
wastewater treatment, there is a scarcity of studies on the prediction of waste sludge. To …

This paper proposes a novel targeted blend of machine learning (ML) based approaches for
controlling wastewater treatment plant (WWTP) operation by predicting distributions of key …

Water pollution generated from intensive anthropogenic activities has emerged as a critical
issue concerning ecosystem balance and livelihoods worldwide. Although optimizing …

This paper presents the use of four machine learning algorithms including Gaussian process
regression (GPR), random forest (FR), extreme gradient boosting (XGB) and light gradient …

With the advent of the big data era, data-driven analysis to realize the mining of internal laws
of data has gradually become a developmental trend in sewage management and decision …

The main objective of this study was to develop, validate, and comprehend machine
learning (ML) models capable of predicting chemical oxygen demand concentration in the …