Prediction of chemical composition concentration in an urban area by Artificial Neural Networks

Abstract

An artificial neural network (ANN) is one of the computational methods that, through the learning process and using simple processors called neurons, tries to give a mapping between the input variables and the output variables. In this paper, six different recurrent dynamic ANN models are proposed to predict the air pollutant concentrations in city of Tehran, Iran. There is no need to know the details of the governing phenomena to develop the ANN models.The chemical composition as the air pollutants are consisting of NO2, SO2, CO, O3, PM10, and PM2.5. The proposed models are designed with an input layer consisting of meteorological variables and previous sampling times of each output variable. The models have the capability for air pollutant concentration prediction for 24 hours later. The results show that the developed models for NO2, SO2, CO, O3, PM10, and PM2.5 have the values of coefficient of determination (­R2) equal to 0.91, 0.95, 0.94, 0.97, 0.94, and 0.93. Also, the Normalized Root Mean Square Error (NRMSE) is equivalent to 0.0355, 0.2577, 0.047, 0.0397, 0.0270, and 0.0445 for NO2, SO2, CO, O3, PM10, and PM2.5 models, respectively. The results show that they all models have high accuracy and a low error value. The results may be because the model uses an almost complete set of input variables. Also the model used three sampling times as input variables through a recurrent structure to capture the dynamic behavior. The number of sampling times used as input variables through a recurrent structure that is related to the dynamic conditions of the model.