Modelling the formation of Ozone in the air by using Adaptive Neuro-Fuzzy Inference System (ANFIS) (Case study: city of Yazd, Iran)

The impact of air pollution and environmental issues on public health is one of the main topics studied in manycities around the world. Ozone is a greenhouse gas that contributes to global climate. This study was conducted topredict and model ozone of Yazd in the lower atmosphere by an adaptive neuro-fuzzy inference system (ANFIS). Allthe data were extracted from 721 samples collected daily over two successive years, from April 2012 to 29 March2014. The concentration of pollutants and meteorological variables including NOX, temperature, wind speed andwind direction were considered as input and ozone (O3) as the output of model. The results showed that among fivemembership functions used in the model, the Gaussian membership function with R2 equal to 0.949, RMSE equal to2.430 and correlation coefficient equal to 0.974 was obtained as the best model to predict the concentration of ozonein the lower atmosphere. This study showed that predicting and modelling ozone using an adaptive neuro-fuzzyinference system (ANFIS) is appropriate and, due to the expansion of the city of Yazd in the not too distant future, itis necessary to pay more attention to the permissible threshold values of pollutants such as ozone.