Air Quality Analysis and Prediction in Tamil Nadu: AN ANN and CNN Approach

Air quality poses a significant environmental and health challenge, particularly in developing nations like India. Tamil Nadu, situated in southern India, grapples with severe air pollution issues stemming from diverse sources like discharges from automobiles, production tasks, biomass burning, and dust storms. Traditional monitoring and forecasting methods like ground-based stations and satellite observations have limitations concerning spatial and temporal coverage, cost, and upkeep. Consequently, there arises a demand for alternative approaches harnessing advancements in machine learning and big data analytics to furnish high-resolution, real-time air quality insights. In this study, we introduce an innovative methodology for analyzing and predicting air quality in Tamil Nadu employing artificial neural networks (ANNs) and convolutional neural networks (CNNs). These sophisticated machine learning techniques adeptly capture intricate nonlinear relationships from extensive and diverse datasets while adeptly handling missing and noisy data. Leveraging historical datasets encompassing air pollutant concentrations, meteorological variables, and land use features sourced from multiple channels, we train and validate our models. We conduct comparative assessments with established methodologies, assessing our models' robustness and generalizability. Furthermore, we offer an understanding of the spatiotemporal dynamics and trends of air quality in Tamil Nadu, identifying pivotal factors and drivers of air pollution. Our proposed approach holds promise for policymakers, researchers, and the general populace to comprehend and ameliorate the air quality scenario not only in Tamil Nadu but also in analogous regions worldwide. Additionally, our model yields the following performance metrics: Mean Absolute Error (MAE): 0.037, R-squared (R2): 0.9998, Root Mean Squared Error (RMSE): 0.4522.