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Title	A Machine Learning Approach For Predicting Air Quality Index In Smart Cities
ID_Doc	2458
Authors	Swamynathan S.; Sneha N.; Ramesh S.P.; Niranjana R.; Ponkumar D.D.N.; Saravanakumar R.
Year	2024
Published	Proceedings of 5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024
DOI	http://dx.doi.org/10.1109/ICICNIS64247.2024.10823106
Abstract	One essential natural resource, the air quality index has been deteriorated by economic activities. Many studies have been conducted about the forecasting of periods of unsafe atmospheric conditions; however, the majority of these studies are limited in their capacity to include seasonal and other factors due to insufficient longitudinal data. India noise pollution has compiled a 6-year dataset that has been used to build several prediction models. There are encouraging outcomes when AQI levels are predicted using machine learning methodologies such as support vector machines (SVMs) artificial neural networks (ANNs), and Naive Bayes (NB). However, in several tests involving datasets from three different locations, NB, ANN, and SVM performed well to maximize prediction performance. When it comes to mean absolute error (MAE), R-squared (R2), and Root-mean-square deviation (RMSE), the combined performance of stacks is unsurpassed. In the related work section, summarize the limitations of existing models, such as inadequate data preprocessing, low spatialtemporal resolution, limited feature selection, or overfitting in complex models. Highlight gaps in scalability, real-time adaptability, and generalization across regions, emphasizing the need for robust, efficient models tailored to air quality prediction. © 2024 IEEE.
Author Keywords	Air Quality Index; Artificial Neural Networks; Mean Absolute Error; Naive Bayes; Root-mean-square deviation; Rsquared; Support Vector Machines

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