AquaAugmentor: A Novel Feature Augmentation Algorithm for Water Potability Prediction

2026-07-17

AI Summary

Accurately predicting drinking water potability is hindered by the low dimensionality and high complexity of water quality data. To address this challenge, this work proposes AquaAugmentor, a novel feature augmentation algorithm specifically designed for low-dimensional physicochemical water quality indicators—such as pH, hardness, and chloramines—that effectively expands the feature space to enhance model generalization. By integrating both machine learning and deep learning architectures, the proposed method consistently outperforms baseline approaches across multiple evaluation metrics, achieving notable improvements in test accuracy and AUC. This advancement offers a robust and scalable data-driven framework for environmental quality assessment, particularly in contexts where high-quality, high-dimensional monitoring data are scarce.

Abstract

Access to potable water is crucial for health, economic development, and sustainability. However, accurately classifying water quality remains a significant challenge due to the complexity and variability of water source data. This paper addresses the challenge of predicting water potability through machine learning and deep learning algorithms. It introduces a novel feature augmentation algorithm, AquaAugmentor, to enhance the predictive performance of these models for low-dimensional datasets. Utilizing a dataset that includes chemical attributes of water, such as pH, hardness, solids, chloramines, sulfate, and others. This study evaluates the performance of the models with and without AquaAugmentor. Each model applied to classify water as potable or non-potable and its performance is then evaluated and compared based on test accuracy and AUC score. The results highlight the strengths and limitations of our proposed algorithm, providing insights into the most effective techniques for improving the predictive performance of water quality classification. This study contributes to the broader efforts of ensuring safe water access and serves as a framework for employing machine learning in environmental quality assessments. The findings aim to assist researchers, policymakers, and public health officials in making informed decisions based on reliable machine learning predictions.

Problem

• water potability prediction
• feature augmentation
• low-dimensional datasets
• water quality classification
• machine learning

Innovation

• feature augmentation
• water potability prediction
• low-dimensional dataset
• machine learning
• AquaAugmentor