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Home > Archives > Volume 20, No 11 (2022) > Article

DOI: 10.14704/nq.2022.20.11.NQ66193

Air Quality Index Prediction Using Machine Learning Techniques

M.S.Bhuvaneswari, N.Balaganesh


Human welfare is fundamentally impacted by air quality. Poor air quality causes a wide range of medical issues, especially in children. The ability to predict air quality gives the government and other concerned organisations the capability to take all necessary precautions to protect the most vulnerable people from being exposed to air that is of an unhealthy quality. Because conventional approaches to this task lack access to sufficient longitudinal data, their effectiveness has been severely constrained. Monitoring and preserving air quality has emerged as one of the most significant movements in many modern and metropolitan places. Air quality is negatively impacted by several types of pollution brought on by transportation, electricity, fuel use, and other reasons. The quality of life in wealthy urban areas is seriously threatened by the presence of hazardous gases. With the rise in air pollution, we really want to put in place efficient air quality monitoring systems that gather data on the level of air pollutants and offer evaluations of air contamination everywhere.


Index Prediction, Techniques,Models, Air Quality, Artificial Neural Network, Machine Learning

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