Air Pollution Prediction Using Model of Deep Learning

Air pollution has grown to be a severe and rising environmental issue in recent decades as a result of human activities, industrialization, and urbanization. Health risks arise from prolonged exposure to high levels of airborne contaminants. Forecasting pollutants is therefore essential for early detection and air pollution management. One of the major pollutants that has a substantial impact on air quality is PM2.5, which respirable particles having a diameter of 2.5 micrometers or less. An increase in PM2.5 concentrations makes the hazard to human health worse. This study looks into the accurate prediction of long-term PM2.5 concentrations, which can aid in reducing air pollution and avoiding its negative consequences. The proposed method is unique in its ability to handle massive multivariate nonlinear datasets by combining a Gated Recurrent Unit (GRU) algorithm and an Encoder- Decoder (E-D) model to predict PM2.5 concentrations within 24 hours using data from Beijing, China's capital. The GRU encoder is employed to extract feature correlations between meteorological and pollutant data, aiding in the identification of the PM2.5 concentrations' spatiotemporal distribution features. The GRU decoder, as an output layer, obtains PM2.5 concentration correlations from spatiotemporal data, overcoming the problem of long-term dependence and realizing the task of accurately predicting PM2.5 concentrations in multiple sites.