A comparative analysis of machine learning techniques for chlorine remaining treatment using algorithms for predictive analytics
Abstract
The effectiveness of numerous algorithms for machine learning in forecasting residual chlorine levels in drinking water distribution systems is assessed in this study. The goal of the research is to determine the best precise and dependable technique for maintaining ideal chlorine levels, thereby guaranteeing water safety and purity, by comparing models like Random Forest, Support Vector Machine, and Artificial Neural Networks.
How to Cite This Article
EE Nnuka (2024). A comparative analysis of machine learning techniques for chlorine remaining treatment using algorithms for predictive analytics . International Journal of Future Engineering Innovations (IJFEI), 1(6), 09-11.