Comparison of CART and C4.5 decision tree algorithms for classification of particulate matter pollution less than 2.5 microns (PM2.5)

Today, with the development of industry and the growth of cities, many environmental problems have arisen. One of the most important environmental problems is the air pollution. An important part of air pollution is related to the pollution of suspended particles less than 2.5 microns. Classification of particulate matter pollution less than 2.5 microns in order to control and reduce air pollution is of great importance to achieve sustainable development in cities. In order to classify pollution in this paper from meteorological parameters (wind speed, wind direction, temperature, relative humidity, air pressure, rainfall), topographic status, intensity of temperature inversion and pollution for the two nearest stations, and the time dependence were used as influencing factors. Among the classification methods, tree-based methods were chosen due to the better understanding and simplicity. In this paper, CART(Classification And Regression Tree) and C4.5 decision tree algorithms are used to classify the pollution of suspended particles less than 2.5 microns.Among these methods, C4.5 method with the overall accuracy of 78.3% and the kappa index of 74.8% was selected as the best classification method. Pollution parameters of the two nearest neighbours, topography, temperature, air pressure, rainfall, intensity of temperature inversion, relative humidity, wind speed, wind direction, month of the year, day of the week, hour of the day have the greatest impact on the classification of the superior method, respectively.

Comparison of CART and C4.5 decision tree algorithms for classification of particulate matter pollution less than 2.5 microns (PM2.5) Keywords:

Air pollution , CART , C4.5 , PM2.5 , Overall accuracy , Kappa index.