Credibility Analysis of Air Quality Data Based on Improved Measurement Method

Abstract

Due to the interference of various factors, the experimental data are filled with a large number of untrustworthy data, which brings great difficulties to the prediction of PM2.5. In view of this problem, this paper establishes a data credibility measurement model to analyze the credibility of the data. Firstly, the credibility between data sources is calculated based on the similarity between data sources. Secondly, the direct credibility of the data source, the recommended credibility of the data source and the penalty value of the data source are used to calculate the comprehensive credibility of the data source. Finally, the credibility of the data is calculated based on the opposite event in which all data sources provide erroneous data. The method is applied to the prediction of PM2.5 concentration, and the credibility of air quality data is analyzed. The experimental results show that the model can calculate the credibility of the data sensitively and effectively, which increases the accuracy of PM2.5 prediction results and provides a solution for further study on data credibility evaluation methods.