AI 中文总结
针对水源数据复杂多变致水质分类难的问题，提出特征增强算法AquaAugmentor，利用含多种化学属性的数据集，评估有无该算法时模型对水可饮用性的预测性能，为相关决策提供依据，助力环境质量评估。
AI 中文摘要
获得安全饮用水对健康、经济发展和可持续性至关重要。然而，由于水源数据的复杂性和多变性，准确分类水质仍然是一项重大挑战。本文通过机器学习和深度学习算法应对预测饮用水可预测性的挑战。它引入了一种新型特征增强算法AquaAugmentor，以提高这些模型对低维数据集的预测性能。利用包含水的化学属性（如pH值、硬度、固体、氯胺、硫酸盐等）的数据集。本研究评估了有无AquaAugmentor时模型的性能。每个模型用于将水分类为可饮用或不可饮用，然后根据测试准确性和AUC分数评估和比较其性能。结果突出了我们提出的算法的优势和局限性，为提高水质分类预测性能的最有效技术提供了见解。本研究有助于更广泛地确保安全用水，并为在环境质量评估中应用机器学习提供了框架。研究结果旨在帮助研究人员、政策制定者和公共卫生官员基于可靠的机器学习预测做出明智决策。
英文摘要
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.
CommentsPublished in 2024 6th International Conference on Sustainable Technologies for Industry 5.0 (STI), 14-15 December, Dhaka, Bangladesh
Journal ref2024 6th International Conference on Sustainable Technologies for Industry 5.0 (STI)
DOI：10.1109/STI64222.2024.10951101
