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International Research Journal of Advanced Engineering and Technology

Open Access Peer Review International
Open Access

Hybrid Machine Learning Framework for Real-Time Prediction and Optimization of Chlorine Residual Levels in Water Distribution Systems

DOI https://doi.org/10.55640/irjaet-v03i07-01
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Rohan Mehta 1 , Ananya Sharma 1 ,
4 Department of Mechanical Engineering, Indian Institute of Technology Delhi, New Delhi, India
4 Department of Electrical Engineering, National Institute of Technology Karnataka, Surathkal, India

Abstract

Effective management of chlorine residual levels in drinking water distribution systems is a critical component of ensuring microbiological safety and maintaining regulatory compliance. Variability in hydraulic conditions, decay kinetics, and environmental interactions introduces significant uncertainty in residual chlorine prediction, making traditional deterministic models insufficient for real-time operational control. This study proposes a hybrid machine learning framework that integrates data-driven predictive modeling with optimization mechanisms to enhance real-time chlorine residual management in complex water distribution networks. The framework synthesizes multi-source water quality data, hydraulic parameters, and historical disinfection behavior to develop adaptive predictive models capable of capturing nonlinear decay patterns.

Drawing upon prior studies on data-driven water quality modeling and chlorine decay dynamics, including Gibbs et al. (2006), Kang et al. (2017), and Zounemat-Kermani et al. (2018), the proposed approach leverages ensemble learning and deep learning architectures to improve predictive robustness. Additionally, optimization layers are incorporated to adjust operational dosing strategies dynamically under varying demand conditions. The study also considers ecological and probabilistic perspectives on residual chlorine variability as highlighted by Rajabova and Mambetullaeva (2020). Epidemiological insights from waterborne disease risk assessments further emphasize the public health significance of maintaining optimal chlorine residuals (Benacer D, Thong K, Min N, Verasahib KB, Galloway R, Hartskeerl R, et al., 2016).

The results demonstrate that hybrid models outperform conventional regression and standalone machine learning techniques in both prediction accuracy and stability under dynamic system fluctuations. The proposed framework provides a scalable and intelligent decision-support system for water utilities aiming to improve disinfection efficiency, reduce chemical overuse, and ensure safe drinking water delivery.

Keywords

Chlorine residual prediction, hybrid machine learning, water distribution systems

References

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