Prediction of hourly ozone concentrations with multiple regression and multilayer perceptron models

Abstract

In this work ozone observations of an urban area of the east coast of the Iberian Peninsula, are analyzed. The data set contains measurements from five automatic air pollution monitoring stations (background suburban or traffic urban). The application of multiple linear regression and neural networks models is considered. These models forecast hourly ozone levels for short-term prediction intervals (1, 8, and 24 h in advance). The study period is 2010–2012. The input variables are meteorological observations, ozone and nitrogen oxides concentrations, and daily and weekly seasonal cycles. The performance criteria to evaluate the computations accuracy are the residual mean square error, the mean absolute error, and the correlation coefficient between observations and predictions. These criteria have better results for the 1-h and 24-h predictions in all the locations. The comparison of multiple linear regressions and multilayer perceptron networks indicates that the second approach allows to obtain more accurate forecast for the three prediction intervals.