Air Pollution Forecasting Using a Deep Learning Model Based on 1D ConvNets and Bidirectional GRU

This study presents a methodology for air pollution forecasting, aiming to improve accuracy through incorporating models for deep learning. Our goal is to create a reliable technique for forecasting air pollution concentrations. Central Pollution Control Board (CPCB) data undergoes exploratory data analysis. Analysis (EDA) and pre-processing before being split into training and testing sets. Two sequential models, Sequential-1 and Sequential-2, are compared, with Sequential-2 incorporating Conv1D layers alongside GRU for enhanced spatial-temporal modeling. Findings reveal that Sequential-2 consistently outperforms Sequential-1, exhibiting lower loss, mean squared error (MSE), validation loss, and validation MSE metrics. This indicates Sequential-2's superior predictive performance and generalization capability, attributed because of how well it can grasp spatial dependencies. In sum, the methodology proves that deep learning methods work well for predicting air pollution levels, offering promising avenues for accurately predicting pollutant concentrations and informing mitigation strategies for a healthier environment