• Aryan Mahajan
• Mayank Chaudhary
• Sheenam Naaz

Air pollution has emerged as a significant environmental issue, leading to numerous casualties each year and posing serious risks to both human health and the environment. It contributes to the greenhouse effect, exacerbates global warming, and heightens the likelihood of lung cancer and other respiratory diseases, such as allergies. To effectively combat air pollution, it is crucial to establish and enforce stringent air quality standards. The Air Quality Index (AQI) serves as a critical measure of the concentration of pollutants in the atmosphere. With advancements in machine learning, accurately forecasting fine-grained AQI levels has become achievable. Various algorithms, such as logistic regression, decision tree regression, K-Nearest Neighbors (KNN), Support Vector Regression (SVR), and linear regression, have been employed to predict AQI. The primary goal of this research is to develop machine learning models and evaluate their performance in predicting AQI, ultimately determining which algorithm is most effective for AQI prediction.

Air quality, Prediction, Algorithms, Random Forest, Linear Regression

"Smart City Air Quality Forecasting: Combining Hybrid Models for Fine-Grained Predictions", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.e365-e380, October-2024, Available :http://www.jetir.org/papers/JETIR2410439.pdf

"Smart City Air Quality Forecasting: Combining Hybrid Models for Fine-Grained Predictions", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. ppe365-e380, October-2024, Available at : http://www.jetir.org/papers/JETIR2410439.pdf