Short-term prediction of NO sub(2) and NOx concentrations using multilayer perceptron neural network: a case study of Tabriz, Iran

Due to the health effects caused by airborne pollutants in urban areas, forecasting of air quality parameters is one of the most important topics of air quality research. During recent years, statistical models based on artificial neural networks (ANNs) have been increasingly applied and evaluated for forecasting of air quality. The development of ANN and multiple linear regressions (MLRs) has been applied to short-term prediction of the NO sub(2) and NOx concentrations as a function of meteorological conditions. The optimum structure of ANN was determined by a trial and error method. We used hourly NOx and NO sub(2) concentrations and metrological parameters, automatic monitoring network during October and November 2012 for two monitoring sites (Abrasan and Farmandari sites) in Tabriz, Iran. Designing of the network architecture is based on the approximation theory of Kolmogorov, and the structure of ANN with 30 neurons had the best performance. ANN trained by scaled-conjugate-gradient (trainscg) training algorithm has implemented to model. It also demonstrates that MLP neural networks offer several advantages over linear MLR models. The results show that the correlation coefficient (R super(2)) values are 0.92 and 0/94 for NO sub(2) and NOx concentrations, respectively. But in MLR model, R super(2) values were 0.41 and 0.44 for NO sub(2) and NOx concentrations, respectively. This work shows that MLP neural networks can accurately model the relationship between local meteorological data and NO sub(2) and NOx concentrations in an urban environment compared to linear models.