Application Study of Comprehensive Forecasting Model Based on Entropy Weighting Method on Trend of PM 2.5 Concentration in Guangzhou, China

Dong-jun Liu and Li Li
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Dong-jun Liu: Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China
Li Li: Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China

IJERPH, 2015, vol. 12, issue 6, 1-15

Abstract: For the issue of haze-fog, PM 2.5 is the main influence factor of haze-fog pollution in China. The trend of PM 2.5 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 PM 2.5 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 PM 2.5 concentration in Guangzhou China was quantitatively forecasted based on the comprehensive forecasting model. The results were compared with those of three single models, and PM 2.5 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.

Keywords: PM 2.5; comprehensive forecasting model; entropy weighting method; haze-fog (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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