PREDICTING AIR QUALITY IN REAL TIME WITH ADVANCED MACHINE LEARNING MODELS

Abstract

The growing concern over air pollution has prompted the need for effective, real-time air quality prediction systems. Predicting air quality in real time enables timely decision-making to mitigate the adverse effects of pollution on public health and the environment. This study presents an advanced machine learning-based approach for real-time air quality prediction, incorporating a variety of 
environmental factors, such as particulate matter (PM2.5), nitrogen dioxide (NO2), carbon monoxide (CO), and weather conditions. Using cutting-edge machine learning algorithms, including deep learning, ensemble models, and support vector machines 
(SVM), this approach aims to improve the accuracy and reliability of air quality forecasts.