Advanced Machine Learning Techniques for Assessing Water Quality - A Comparative Study Using Ensemble, Neural Networks, and Instance-Based Models - Comparative evaluation of model performance

Aug 4, 2026

Upload by Sage

Access to safe water remains a major global challenge, since billions of people lack reliable access to potable water. Accurate water-safety identification is critical for reducing waterborne diseases, yet conventional laboratory testing is slow and costly. This study applies machine learning to physicochemical properties and addresses a common research gap: most work evaluates a single model type on small datasets. Three paradigms—ensemble, neural networks, and instance-based models—are compared on a public dataset of 7,999 samples.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Abstract

Introduction

Motivation and problem context

Limits of conventional testing

Rationale for comparative ML analysis

Study scope: RF, ANN, KNN

Advanced Machine Learning Techniques for Assessing Water Quality: A Comparative Study Using Ensemble, Neural Networks, and Instance-Based Models Muhammad Hafiz1 , Johan Iswara2 , Bari Fakhrudin3 , WiditraNararya Rama4 , Avellino Vincent Juwono5 , Gilang Raka Rayuda Dewa6􀀍 1,2,3,4,5,6 Department of Computer Science, Sampoerna University, Indonesia 􀀍 Corresponding Author: Gilang Raka Rayuda Dewa (e-mail: [gilang.dewa@sampoernauniversity.ac.id](gilang.dewa@sampoernauniversity.ac.id)) | Article Information | | ABSTRACT | | --- | --- | --- | |
Article History | | Access to safe water remains a significant issue, with around 5.8 billion people lacking access to potable water globally. Rapid and | |
Received: May 27, 2025
Revised: June 1, 2025
Published: July 02, 2025 | | accurate identification of water safety is thereby essential to reduce public waterborne diseases. However, conventional laboratorybased testing is typically time-consuming and expensive. On the other hand, machine learning provides time- and cost-effective assessments based on physicochemical properties. Unfortunately, most studies only evaluate a single model type in a small dataset, resulting in limited insight that makes it hard to determine the actual effectiveness of these models. To address this limitation, the present study conducts a comparative analysis of three machine learning | | | | | |
Keywords: | | paradigms: ensemble-based, neural network-based, and instancebased models. Using a publicly available dataset of 7,999 samples, | |
ensemble;
instance;
machine learning; neural networks; water quality. | | each model is evaluated using key performance metrics, including accuracy, precision, and confusion matrix analysis. The evaluation results show that the ensemble-based model achieves the highest accuracy of 96.62% and precision of 96. 53%, outperforming the neural network-based model, which achieves an accuracy of 94 . 75% and precision of 70.47% . Additionally, the instance-based model achieves an accuracy of 91. 12% and a precision of 83.04% . These results indicate the effectiveness of the ensemble-based model for real-time water quality monitoring. | INTRODUCTION Ensuring access to clean water has become a considerable concern for researchers regarding human health and well-being. Over 5.8 billion people worldwide lack access to safely managed drinking water, exposing them to significant health risks from chemical contaminants , e.g. , heavy metals and pesticides, and biological contaminants, e.g. , bacteria and viruses (State of the World’s Drinking Water, 2022) . Waterborne diseases cause approximately 505,000 deaths each year, including over 395,000 children under five, primarily due to unsafe water, poor sanitation, and inadequate hygiene (World Health Organization, 2023) . Moreover, significant chemical and biological substances can easily contaminate water, compromising its safety (Ghoochani et al. , 2023; Mohialden et al. , 2024) . On the other hand, manually monitoring water quality using conventional tools is challenging due to the potential for human error and inefficient use of resources (Jha, 2020) . Relying on laboratory testing methods is also time-consuming and delays the implementation of proper actions in anticipating public health risks (Nuanmeesri et al. , 2024) . Accordingly, to address these challenges, researchers have intensively explored machine learning (ML) techniques (Zhu et al. , 2022) . By analyzing the historical data patterns of water quality, ML models can immediately predict the water status based on various physicochemical parameters (Nuanmeesri et al. , 2024; Zhu et al. , 2022) . Moreover, ML techniques can identify complex interactions among contaminants that are impossible to detect using conventional statistical methods. However, due to the diversity of machine learning paradigms, selecting the most suitable model for water quality classification is a challengi

FAQs

Why is identifying water quality safety important?

Unsafe water contributes to widespread health risks, including waterborne diseases caused by chemical and biological contaminants. Accurate identification supports timely actions to reduce these risks.

What machine learning approaches are compared in the study?

The study compares three paradigms: Random Forest (ensemble-based), Artificial Neural Network (neural-network-based), and K-Nearest Neighbors (instance-based).

How is model effectiveness evaluated and what is the main finding?

Models are assessed using performance metrics such as accuracy, precision, and confusion matrix analysis. The ensemble-based approach achieves the highest accuracy and precision, indicating strong suitability for real-time water quality monitoring.

Aug 4, 2026

Upload by Sage