基于K-均值聚类和贝叶斯判别的城市空气质量等级分类及预测

Classification and Prediction of Urban Air Quality Grade Based on K-means Clustering and Bayesian Discrimination

为了研究城市的空气质量情况,对65个重点环保城市2018年的样本数据进行分析,在利用K-均值聚类将样本重点环保城市划分等级的基础上,进一步利用贝叶斯判别法建立了判断城市空气质量等级的判别函数,结果表明将K-均值聚类分析和贝叶斯判别分析用于判别城市空气质量等级的方法准确可靠,能够应用于我国城市空气质量等级预测,最后分析不同等级的城市空气被污染的原因并提出针对性治理建议,为我国城市空气污染治理提供参考.

In order to study the urban air quality situation,the sample data of 65 key environmental protection cities in 2018 is analyzed.On the basis of grading the sample key environmental protection cities by using K-mean clustering,the bayes discriminant method is further used to establish the discriminant function to judge the urban air quality grade,the results show that the K-means clustering method and Bayesian discrimination method are accurate and reliable,which can be applied to the prediction of urban air quality grade in China.Finally,the causes of urban air pollution of different levels are analyzed and the corresponding treatment suggestions are put forward,providing reference for the treatment of urban air pollution in China.