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Intelligent Water Quality Assessment and Prediction System for Public Networks: A Comparative Analysis of ML
Camelia Paliuc1, Paul Banu-Taran1, Sebastian-Ioan Petruc1
1Department of Automation and Computing, Politehnica University Timisoara, 300006 Timisoara, Romania.
Sensors (Basel, Switzerland)
|February 27, 2026
Summary
A new smart water monitoring system predicts drinking water quality using machine learning algorithms. The system identifies areas with poor water quality and recommends treatments, enhancing public health and water management.
Area of Science:
- Environmental Science
- Computer Science
- Public Health
Background:
- Public water networks require robust quality assessment and prediction systems.
- Existing monitoring methods may lack real-time capabilities and sophisticated data analysis.
Purpose of the Study:
- To develop and evaluate a smart water monitoring system for public water networks.
- To assess drinking water quality and predict future conditions using machine learning.
Main Methods:
- Implemented a system on Google Firebase cloud storage.
- Applied twelve machine learning algorithms to 804 water samples, comparing 17 parameters.
- Utilized decision tree, random forest, gradient boosting, and logistic regression models.
Main Results:
- The decision tree algorithm demonstrated the highest accuracy and calibration.
- Identified regions with the worst and best water quality.
- Provided real-time data, drinkability predictions, and treatment recommendations.
Conclusions:
- The developed system offers a stable solution for smart water monitoring.
- Correlates practical deployment with data-driven insights for improved water management.
- Contributes to enhanced public health and scalable water quality solutions.
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