AquaAugmentor: A Novel Feature Augmentation Algorithm for Water Potability Prediction
Machine LearningArtificial IntelligenceComputational Engineering, Finance, and Science
Summary
The authors focus on predicting whether water is safe to drink by using machine learning and deep learning techniques. They created a new method called AquaAugmentor to improve how well these models work, especially when there isn’t much data available. By testing different models with and without this method on water data featuring things like pH and chemicals, they show how AquaAugmentor can help classify water as potable or not. Their work aims to support better decisions about water safety using reliable computer predictions.
Authors
Muntasir Tabasum, Al Zadid Sultan Bin Habib, Tanpia Tasnim, Md. Ekramul Islam, Md Younus Ahamed, Md Asif Bin Syed
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.