AN ARTIFICIAL NEURAL NETWORK-BASED MODEL FOR SHORT-TERM PREDICTIONS OF DAILY MEAN PM10 CONCENTRATIONS

Abstract

Prediction of particulate matter (PM) in the air is an important issue in control and reduction of pollutants in the air. One of the most useful methods to forecast atmospheric pollution is artificial neural network (ANN) because of its high ability to forecast the atmospheric events. In this study ANN technique has been used to predict the PM10 concentration in Istanbul. Meteorological data and PM10 data, which had been collected from Sariyer-Bahcekoy for the one year data, were used. The data were separated into two groups for training and testing the model. The odd days were used for training and the remaining was used for the testing. The transfer function was sigmoid function. In the model, different hidden neuron numbers were altered for proposed ANN structure. We have altered number of neurons for hidden layer between 2 to 10. The prediction of PM10 of the model during the years 2004-2005 follows the actual values with success, with the best calculated correlation coefficient 0.60.