2024, Volume 18, Issue 2
Frontiers of Environmental Science & Engineering >> 2024, Volume 18, Issue 2 doi: 10.1007/s11783-024-1777-6
Development of gradient boosting-assisted machine learning data-driven model for free chlorine residual prediction
1. School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA;1. School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA;2. School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China;1. School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA;1. School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA;1. School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
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Abstract
● A machine learning approach was applied to predict free chlorine residuals.
Keywords
Machine learning ; Data-driven modeling ; Drinking water treatment ; Disinfection ; Chlorination
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