predicting air pollution in tehran: genetic algorithm and back propagation neural network

suspended particles have deleterious effects on human health and one of the reasons why tehran is effected is its geographically location of air pollution. one of the most important ways to reduce air pollution is to predict the concentration of pollutants. this paper proposed a hybrid method to predict the air pollution in tehran based on particulate matter less than 10 microns (pm10), and the information and data of aghdasiyeh weather quality control station and mehrabad weather station from 2007 to 2013. generally, 11 inputs have been inserted to the model, to predict the daily concentration of pm10. for this purpose, artificial neural network with back propagation (bp) with a middle layer and sigmoid activation function and its hybrid with genetic algorithm (bp-ga) were used and ultimately the performance of the proposed method was compared with basic artificial neural networks along with (bp) based on the criteria of - r2-, rmse and mae.  the finding shows that bp-ga   has higher accuracy and performance. in addition, it was also found that the results are more accurate for shorter time periods and this is because the large fluctuation of data in long-term returns negative effect on network performance. also, unregistered data have negative effect on predictions. microsoft excel and matlab 2013 conducted the simulations.