Air-pollution prediction in smart city, deep learning approach

Abstract

Over the past few decades, due to human activities, industrialization, and urbanization, air pollution has become a life-threatening factor in many countries around the world. Among air pollutants, Particulate Matter with a diameter of less than $2.5\mu m$ ( $P{M}_{2.5}$ ) is a serious health problem. It causes various illnesses such as respiratory tract and cardiovascular diseases. Hence, it is necessary to accurately predict the $P{M}_{2.5}$ concentrations in order to prevent the citizens from the dangerous impact of air pollution beforehand. The variation of $P{M}_{2.5}$ depends on a variety of factors, such as meteorology and the concentration of other pollutants in urban areas. In this paper, we implemented a deep learning solution to predict the hourly forecast of $P{M}_{2.5}$ concentration in Beijing, China, based on CNN-LSTM, with a spatial-temporal feature by combining historical data of pollutants, meteorological data, and $P{M}_{2.5}$ concentration in the adjacent stations. We examined the difference in performances among Deep learning algorithms such as LSTM, Bi-LSTM, GRU, Bi-GRU, CNN, and a hybrid CNN-LSTM model. Experimental results indicate that our method "hybrid CNN-LSTM multivariate" enables more accurate predictions than all the listed traditional models and performs better in predictive performance.

Keywords: Air-pollution; CNN-LSTM; Deep learning; Forecasting; GRU; LSTM; PM2.5.