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VOL. 8, ISSUE 1 (2024)
Predictive analytics for chlorine residual management: A comparative study of machine learning algorithms
Authors
Pierpaolo Di Nosotti
Abstract
This study evaluates the efficacy of various
machine learning algorithms in predicting residual chlorine levels in drinking
water distribution systems. By comparing models such as Random Forest, Support
Vector Machine, and Artificial Neural Networks, the research aims to identify
the most accurate and reliable method for maintaining optimal chlorine levels,
thereby ensuring water safety and quality.
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Pages:16-18
How to cite this article:
Pierpaolo Di Nosotti "Predictive analytics for chlorine residual management: A comparative study of machine learning algorithms". International Journal of Advanced Engineering and Technology, Vol 8, Issue 1, 2024, Pages 16-18
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