Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5

Day by day pollution in cities is increasing due to urbanization. One of the biggest challenges posed rapid migration inhabitants into increased air pollution. Sustainable Development Goal 11 indicates that 99 percent world’s urban population breathes polluted air. In such a trend urbanization, predicting concentrations pollutants advance very important. Predictions would help city administrations take timely measures for ensuring 11. data engineering, imputation and removal outliers are important steps prior forecasting concentration pollutants. For meteorological data, missing values critical problems need be addressed. This paper proposes novel method called multiple iterative using autoencoder-based long short-term memory (MIA-LSTM) which uses an extra tree regressor as estimator multivariate followed LSTM autoencoder detection present dataset. The preprocessed were given PM2.5 concentration. also presents effect removing from dataset well imputing process proposed provides better results with root mean square error (RMSE) value 9.8883. obtained compared traditional gated recurrent unit (GRU), 1D convolutional neural network (CNN), (LSTM) approaches Aotizhonhxin area Beijing China. Similar observed another two locations China one location India. show outlier/anomaly improve accuracy forecasting.