Short Term Prediction of Surface Ozone using Artificial Neural Network Model in an Urban Area

In this paper a novel approach, based on a neural network structure, is introduced in order to face with the problem of pollutant estimation in an urban area. A neural architecture, based essentially on suitable number of layers devoted to predict alarm situations and to estimate the value of the pollutant, has been implemented. A new method for short term prediction is presented using the neural network technique. Due to increase in industrial and anthropogenic activity, air pollution is a serious subject of concern today. Surface ozone prediction using the technique of adaptive pattern recognition is developed. The model can predict the mean surface ozone based on the parameters like Nitrogen-di-oxide, temperature and % Relative Humidity, wind direction, wind speed. The model can perform well both in training and independent periods. The classical methods of short term modeling are not reliable enough. The method can also be used for short term prediction of other air pollutants.