# Intelligent Water Quality Assessment and Prediction System for Public Networks: A Comparative Analysis of ML Algorithms and Rule-Based Recommender Techniques

## TL;DR

This paper presents a smart water quality system using machine learning and rule-based methods to assess and predict water quality in public networks, tested in Timișoara, Romania.

## Contribution

The system introduces a stable, scalable solution for smart water monitoring with practical deployment and data-driven recommendations.

## Key findings

- The decision tree algorithm outperformed random forest, gradient boosting, and logistic regression in accuracy and calibration.
- The system identified regions with the best and worst water quality and proposed corresponding treatment measures.
- The solution enhances public health and water management through real-time data and smart predictions.

## Abstract

An assessment and prediction system for the quality of public water networks was developed, using Timișoara, Romania, as a case study. This was implemented on a Google Firebase cloud storage system and comprised twelve ML algorithms applied to test samples for drinkability and used in predictions of upcoming samples. The system compares 17 water quality parameters to the World Health Organization and public reports of Timișoara drinking water standards for 804 samples. The system provides real-time data storage, drinkability prediction for the reservoir water system, and rule-based critical water recommendations for elementary treatment in samples. The most accurate and best-calibrated against random forest, gradient boosting, and Logistic Regression algorithms was the decision tree algorithm of the ML models. The experimental findings also determine the regions of the worst and best water quality and propose respective treatment. In contrast to previous research and structures, the paper demonstrates an approved stable solution for smart water monitoring, correlating practical deployment with sophisticated data-based conclusions. The results contribute to enhancing public health, enhancing water management measures, and upscaling the system for larger-scale applications.