Autoencoder-based deep belief regression network for air particulate matter concentration forecasting

Particulate matter (PM) is one of the most significant air pollutants in recent decades that has tremendous negative effects on the ambient air quality and the public health. Accurate PM forecasting provides a possibility for establishing an early warning system. In this paper, a deep feature learning architecture, i.e., autoencoder-based deep belief regression network (AE-based DBRN), is introduced and utilized to forecast the daily PM concentrations (PM2.5 and PM10). Prior to establishing this model, Pearson correlation analysis is applied to look for the possible input-output mapping, where the input candidate variables contain seven meteorological parameters and PM concentrations within one-day ahead, and the output variables are the local PM forecasts. The addressed model was evaluated by the dataset in the period of 28/10/2013 to 31/8/2016 in Chongqing municipality of China. Moreover, two shallow models, feed forward neural network and least squares support vector regression, were employed for the comparison. The results indicate that the AE-based DBRN model has remarkable better performances among the comparison models in terms of mean absolute percentage error (PM2.5 21.092%, PM10 19.474%), root mean square error (PM2.5 8.600 mu g/m(3), PM10 11.239 mu g/m(3)) and correlation coefficient criteria (PM2.5 0.840, PM10 0.826).