AIR QUALITY PREDICTION USING IOT AND NEURAL NETWORKS-AN EMPIRICAL ANALYSIS

Air pollution is one of the most critical environmental issues affecting human health, ecosystems, and climate change. The rapid urbanization and industrialization have exacerbated air quality issues, necessitating real-time monitoring and prediction systems. This paper presents a comprehensive study on using the Internet of Things (IoT) and Neural Networks for air quality prediction. By leveraging IoT for data acquisition and Neural Networks for predictive analysis, this research aims to provide a scalable and accurate solution to monitor and forecast air quality. Experimental results demonstrate the potential of this integrated approach in providing actionable insights for policymakers and the public.