AquaAugmentor: A Novel Feature Augmentation Algorithm for Water Potability Prediction
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Computer Science > Machine Learning
arXiv:2607.15775 (cs)
[Submitted on 17 Jul 2026]
Title:AquaAugmentor: A Novel Feature Augmentation Algorithm for Water Potability Prediction
Authors:Muntasir Tabasum, Al Zadid Sultan Bin Habib, Tanpia Tasnim, Md. Ekramul Islam, Md Younus Ahamed, Md Asif Bin Syed
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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.
| Comments: | Published in 2024 6th International Conference on Sustainable Technologies for Industry 5.0 (STI), 14-15 December, Dhaka, Bangladesh |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE) |
| Cite as: | arXiv:2607.15775 [cs.LG] |
| (or arXiv:2607.15775v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.15775
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | 2024 6th International Conference on Sustainable Technologies for Industry 5.0 (STI) |
| Related DOI: | https://doi.org/10.1109/STI64222.2024.10951101
DOI(s) linking to related resources
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Submission history
From: Al Zadid Sultan Bin Habib [view email][v1] Fri, 17 Jul 2026 09:11:24 UTC (1,340 KB)
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