Research on air quality prediction method in Hangzhou based on machine learning

Abstract

Abstract Air pollution has become the subject of many current environmental studies, and the quality of air is directly related to the quality of life and health of human beings. In this paper, the Bayesian network model is used to predict air quality in Hangzhou. Six air pollutants SO2, NO2, O3, CO, PM2.5 and PM10 are used as the evaluation factors of the model, and AQI value is the output of the model, and then the Bayesian network model is established. Finally, the model is used to predict air quality and compare with the actual value. The results show that the accuracy of air quality prediction is over 80%, and the predicted value is close to the actual value in most cases, and this shows that Bayesian network model has a certain practical value as a means of air quality prediction.