The evaluation of air pollution awareness based on data mining: The case of Sinop

In this study, using data mining methods, people living in the city center of Sinop have been determined about the environment and especially air pollution awareness and various factors that cause air pollution. A questionnaire consisting of 28 questions was prepared to collect data. FP-Growth association analysis and decision tree model were used as data mining methods. Finally, the simulation process, which provides the optimization of the input data in the decision tree model, is performed.Rapidminer software was used for these analyzes. As a result of the FP-Growth analysis, a reliability of 90% and above was obtained with the 171442 association rule. The most common thought among these association rules is that there is a connection between air pollution and health problems, and that the increase in air pollution causes an increase in respiratory diseases. As a result of the decision tree model, the factors causing air pollution were estimated with 73.81% accuracy. In the decision tree model, it has been determined that the most important factors causing air pollution are coal use and motor vehicles. While creating the decision tree, it was found that the most important variable was gender with a weight of 0.242. As a result of the simulation process created in the decision tree model, our input data was optimized for coal use and motor vehicles, which cause air pollution the most. In our country, studies on environmental pollution, especially air pollution, using data mining are limited. Increasing the studies on this subject can contribute to the creation of environmental awareness.