Air Quality Prediction Using Hybrid Deep Learning-A Review

Abstract

In recent years, many countries are facing the problem of air pollution, which effect on the health of the young and old people for breathing problem. For securing people lives by following government’s policy, it is important to predict the air quality. It is important to invest more time on forecasting of air quality to provide accurate and relevant solution to achieve acceptable result which helps us to overcome faults. With the help of meteorological data and also knowledge about air pollutants we are building a deep learning-based model which forecast concentrations of ambient pollutants. By applying 1D CNN, Bidirectional LSTM we are actually forecasting this proposed model results which is finally present in the form of different graphs, predicted values and by comparing the actual and predicted data we define he accuracy. Based on the Beijing city dataset, our experimental analysis demonstrate he result and advantages of model.