Forecasting CO pollutant concentration of Tabriz city air using artificial neural network and adaptive neuro-fuzzy inference system and its impact on sustainable development of urban

Abstract

Tabriz as one of the major industrial cities of Iran is not immune to air pollution and spends many days air-polluted each year. Since one of the goals of sustainable development is to achieve clean and good air for all segments of society and to attract clean air from the citizens of a city. In this study, it was attempted to present an efficient model for predicting CO pollutant concentrations using artificial neural network (ANN) and adaptive neural-fuzzy inference system (ANFIS). Air quality monitoring and developing efficient air pollution models can be suitable in providing requirements for sustainable development goals. For modelling, the meteorological and pollutant data were first obtained from the Meteorological and Environmental Agency of Tabriz city. The model inputs were temperature, wind speed, humidity and contaminant concentration daily hour index and weekly day index. The results of the study at validation step yielded 0.82 and 0.63 in terms of determination coefficient for ANN models and ANFIS models. It was also observed that the Ensemble method worked even better than the single 2 methods.