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Title	Air Quality Prediction Using Machine Learning: An Comprehensive Review
ID_Doc	7175
Authors	Potey S.; Borkar P.; Badhiye S.; Farooqui S.; Rangari S.
Year	2025
Published	2025 International Conference on Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics, IC3ECSBHI 2025
DOI	http://dx.doi.org/10.1109/IC3ECSBHI63591.2025.10991003
Abstract	Using machine learning (ML)-based prediction models could significantly improve the precision and effectiveness of traditional air quality models. This article provides a comprehensive evaluation of the state-of-the-art in machine learning-based air quality prediction. It looks at the body of research on data sources and machine learning (ML)-based techniques for predicting air quality, with a focus on the several ML techniques and data sources used. Along with discussing the limitations and shortcomings of the current machine learning-based air quality prediction models, the paper also identifies areas that require additional research. This study is divided into four components. The first section provides an overview of air quality assessment and the need for air quality prediction. The second segment reviews the literature on machine learning techniques and studies on air quality assessment. The last section looks at limitations and future research paths as well as the shortcomings of the current machine learning-based air quality prediction models. The fourth component of the study concludes with a summary of the findings and recommendations for additional research. © 2025 IEEE.
Author Keywords	Accuracy metrics; Air pollutants; algorithm; Dataset; Deep Learning; Linear regression; Operating Systems; Prediction Model; Random Forest; Sensor devices; Smart cities

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