Exploring Air Quality Forecasting: Methods,Insights, and Discoveries

Abstract This research is dedicated to advancing air quality forecasting through state-of-the-art neural network models. Our focus lies in Tamil Nadu's environmental data, with a specific emphasis on predicting Respirable Suspended Particulate Matter (RSPM) concentrations, a crucial air quality indicator in the region. Our approach utilizes historical data for SO2 and NO2 as input variables. The core of our methodology comprises a four-layered neural network trained using the back-propagation algorithm. This model effectively predicts RSPM content based on SO2 and NO2 levels. The results from our models highlight the effectiveness of Artificial Neural Networks in analyzing and predicting air quality ,offering valuable contributions to both research and practical applications in the field.