Ozone (O۳) Modeling Using Classification Algorithms andGeospatial Information System (Case Study: The City OfTehran)

Monitoring and modeling pollutants and air quality is one of the environmental challenges that humans have faced. Economic development, urbanization growth, industrial activities and increase in fossil fuel consumption are among the most important factors affecting air pollution. Air pollution as a spatial factor is a dynamic, nonlinear and ambiguous phenomenon. The city of Tehran, as the capital and one of the big cities of Iran, has the first place of air pollution in Iran and struggles with air pollution and its consequences for many days of the year. In this research, the feasibility of ozone pollutant modeling was carried out in Tehran using regression methods and artificial intelligence algorithms in modeling. By comparing the results and choosing the best model, pollutant zoning was done for the city of Tehran. According to the root mean square error which is equal to ۹.۹۳۹۷۳ for ozone pollutant, it shows that the neural network method shows better results.