Spatial and Temporal Variations on Air Quality Prediction Using Deep Learning Techniques

Abstract

Air Pollution is constantly causing a severe effect on the environment and public health. Prediction of air quality is widespread and has become a challenging issue owing to the enormous environmental data with time-space nonlinearity and multi-dimensional feature interaction. There is a need to bring out the spatial and temporal factors that are influencing the prediction. The present study concentrates on the correlation prediction of spatial and temporal relations. A Deep learning technique has been proposed for forecasting the accurate prediction. The proposed Bi_ST model is evaluated for 17 cities in India and China. The predicted results are evaluated with the performance metrics of RMSE, MAE, and MAPE. Experimental results demonstrate that our method Bi_ST accredits more accurate forecasts than all baseline RNN and LSTM models by reducing the error rate. The accuracy of the model obtained is 94%.