Spatio-Temporal Forecasting using a Hybrid BiGRU-1DCNN Model for PM\(_{2.5}\) Concentrations in Delhi, India (2018-2023) Across Multiple Monitoring Stations

Abstract

Air quality deterioration, particularly the suspension of particulate matter over large urban areas, has emerged as a significant environmental concern. This issue, exacerbated by urbanization, industrialization, human activities, and climate change, poses serious health risks to populations. The present study proposes a hybrid BiGRU-1DCNN model to predict PM\(_{2.5}\) levels in Delhi, India, by leveraging data from multiple monitoring stations. The proposed model incorporates Bidirectional Gated Recurrent Units (BiGRU) and a one-dimensional Convolutional Neural Network (1DCNN) to capture both temporal dependencies and spatial correlations in PM\(_{2.5}\) data. The model’s performance is evaluated through both single-station (SS) and spatio-temporal correlation (STC) approaches. Results demonstrate that the hybrid BiGRU-1DCNN model outperforms traditional deep learning models in both SS and STC scenarios. Specifically, it achieved a minimal Root Mean Square Error (RMSE) of 15.75, Mean Square Error (MSE) of 248.04, Mean Absolute Error (MAE) of 9.04, and Mean Absolute Percentage Error (MAPE) of 13.31 at the Jawaharlal Nehru Stadium (JNS) station. For comparison, the univariate SS model for the Major Dhyan Chandra National Stadium (MDCNS) station produced an RMSE of 17.31, MAE of 10.03, MAPE of 14.50, and MSE of 299.59. The non-parametric Friedman ranking further corroborated the superior performance of the hybrid BiGRU-1DCNN model, with it achieving the highest ranking across all performance metrics compared to other models. These results highlight the potential of the ST BiGRU-1DCNN model as a robust tool for air quality forecasting and public health risk mitigation in highly polluted urban environments like Delhi.