title: Application Study of Comprehensive Forecasting Model Based on Entropy Weighting Method on Trend of PM2.5 Concentration in Guangzhou, China.
abstract: For the issue of haze-fog, PM2.5 is the main influence factor of haze-fog pollution in China. The trend of PM2.5 concentration was analyzed from a qualitative point of view based on mathematical models and simulation in this study. The comprehensive forecasting model (CFM) was developed based on the combination forecasting ideas. Autoregressive Integrated Moving Average Model (ARIMA), Artificial Neural Networks (ANNs) model and Exponential Smoothing Method (ESM) were used to predict the time series data of PM2.5 concentration. The results of the comprehensive forecasting model were obtained by combining the results of three methods based on the weights from the Entropy Weighting Method. The trend of PM2.5 concentration in Guangzhou China was quantitatively forecasted based on the comprehensive forecasting model. The results were compared with those of three single models, and PM2.5 concentration values in the next ten days were predicted. The comprehensive forecasting model balanced the deviation of each single prediction method, and had better applicability. It broadens a new prediction method for the air quality forecasting field.
authors.affiliation: Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China. laueastking3168@163.com.
authors.forename: Dong-jun
authors.initials: DJ
authors.lastname: Liu
authors.affiliation: Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China. ximlli@126.com.
authors.forename: Li
authors.initials: L
authors.lastname: Li
mesh_terms: D000397:Air Pollution / Q000032:analysis*; D002681:China; D019277:Entropy; D005544:Forecasting; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis*
chemical_list: D052638:Particulate Matter
keywords: PM2.5; comprehensive forecasting model; entropy weighting method; haze-fog