Foundations of Machine Learning and Intelligent Systems
Articles

Water Quality Prediction Using Machine Learning: A Smart Approach for Real-Time Monitoring

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  • Abhijit Paul,
  • Rishabh Pipalwa
Abhijit Paul
Department of Computer Science, Swami Vivekananda University, Barrackpore, India
Rishabh Pipalwa
Senior Consultant, Aaseya IT services pvt ltd, Hyderabad, Telangana, India

Published 2026-05-27

Keywords

  • Water quality,
  • machine learning,
  • IoT sensors,
  • prediction,
  • water contamination,
  • public health,
  • Random Forest,
  • Support Vector Machines
    • ...More
    Less

How to Cite

Paul, Abhijit, and Rishabh Pipalwa. “Water Quality Prediction Using Machine Learning: A Smart Approach for Real-Time Monitoring”. Swami Vivekananda University Press, May 2026, pp. 92-98, https://swamivivekanandauniversitypress.com/index.php/svup/article/view/581.

Copyright (c) 2026 Swami Vivekananda University Press

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

Water quality is crucial for public health, agriculture, and industrial applications. With increasing pollution and contamination threats, traditional water quality monitoring methods are insufficient to provide real-time insights and early warnings. This paper proposes a machine learning-based framework for predicting water quality parameters, such as pH, dissolved oxygen (DO), turbidity, and chemical composition. The system leverages Internet of Things (IoT) sensors for real-time data collection and uses supervised learning algorithms to predict water quality levels based on historical data. Experimental results demonstrate that machine learning models, particularly Random Forest and Support Vector Machines (SVM), achieve high prediction accuracy and can provide early warnings for water contamination, making the system suitable for both industrial and residential applications.

DOI - https://doi.org/10.65525/SVUP.9788199565418.2026.92-98

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