Prediction of SO2 and PM concentrations in a coastal mining area (Zonguldak, Turkey) using an artificial neural network

Abstract

In this study, artificial neural networks are proposed to predict the concentrations of SO, and PM at two different stations in Zonguldak city, a major coastal mining area in Turkey. The established artificial neural network models involve meteorological parameters and historical data on observed SO2, PM as input variables. The models are based on a three-layer neural network trained by a back-propagation algorithm. The models accurately measure the trend of SO2, and PM concentrations. The results obtained through the proposed models show that artificial neural networks can efficiently be used in the analysis and prediction of air quality.