Application of cellular neural network (CNN) to the prediction of missing air pollutant data

For air-quality assessments in most major urban centers, air pollutants are monitored using continuous samplers. Sometimes data are not collected due to equipment failure or during equipment calibration. In this paper, we predict daily air pollutant concentrations (PM10 and SO2) from the Yenibosna and Umraniye air pollution measurement stations in Istanbul for times at which pollution data was not recorded. We predicted these pollutant concentrations using the CNN model with meteorological parameters, estimating missing daily pollutant concentrations for two data sets from 2002 to 2003. These data sets had 50 and 20% of data missing. The results of the CNN model predictions are compared with the results of a multivariate linear regression (LR). Results show that the correlation between predicted and observed data was higher for all pollutants using the CNN model (0.54–0.87). The CNN model predicted SO2 concentrations better than PM10 concentrations. Another interesting result is that winter concentrations of all pollutants were predicted better than summer concentrations. Experiments showed that accurate predictions of missing air pollutant concentrations are possible using the new approach contained in the CNN model. We therefore proposed a new approach to model air-pollution monitoring problem using CNN.

Research Highlights
► We have applied a Cellular Neural Network (CNN) approach to the air pollution.
► The missing concentrations of PM10 and SO2 pollutants modeled using this method.
► This paper is first example in practically about using CNN to air pollution.
► These result shows that the CNN modeling technique can be considered a promising approach for air pollutant prediction.