A Novel Hybrid Model Based on Spatiotemporal Correlation for Air Quality Prediction

Abstract

With the rapid development of urbanization, environmental pollution has drawn worldwide attention. Accurate air quality prediction is very significant for alleviating severe pollution conditions and human healthy life. A novel hybrid model that combines a spatiotemporal correlation analysis method and an effectively simple network is proposed to forecast the concentrations of air pollutants. Firstly, a new calculation method considering distance and PM2.5 concentrations among stations is proposed to select stations strongly correlated with the target station on the basis of grey relation analysis. Secondly, a fully convolutional network is proposed to extract spatiotemporal features efficiently based on a temporal convolutional network. In addition, meteorological factors and other pollutants are selected as auxiliary factors to further improve the prediction accuracy. To verify the validity of the proposed model, the air quality and meteorological data collected from 40 monitoring stations in Fushun, which is an important industrial city in China, are applied for air quality prediction. The performance of models is evaluated by a series of metrics. The RMSE, MAE, and R2 values of the proposed model are 12.505, 8.214, and 0.884 for forecasting the next hour PM2.5 concentration. Compared with other conventional models and hybrid models, the proposed model verified that it outperforms the other models with air quality prediction.