Air Pollution Prediction by Deep Learning Model

The impact of harmful pollutants in the air on human health is a vast area of research, preventing or controlling, and also monitoring the pollutant is the huge responsibility of any governing body. Several computing models starting from statistical and machine learning to deep learning have compared and contrasted to prove the accuracy of forecasting air quality standards until date. The level of pollutants is still not in control in several parts of the world due to various sources and reasons. This paper attempts to forecast PM2.5 pollutant which is one of the detrimental diseases triggering pollutants throughout the globe by using bidirectional long short term memory model. The proposed model accuracy is comparatively greater than the existing model by evaluating the following error estimation metrics Root mean square error = 9.86, mean absolute error = 7.53, and symmetric mean absolute percentage error = 0.1664.