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Int. J. Environ. Res. Public Health 2015, 12(6), 7085-7099; doi:10.3390/ijerph120607085

Application Study of Comprehensive Forecasting Model Based on Entropy Weighting Method on Trend of PM2.5 Concentration in Guangzhou, China

Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China
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Academic Editor: Michael S. Breen
Received: 27 April 2015 / Revised: 3 June 2015 / Accepted: 17 June 2015 / Published: 23 June 2015
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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. View Full-Text
Keywords: PM2.5; comprehensive forecasting model; entropy weighting method; haze-fog PM2.5; comprehensive forecasting model; entropy weighting method; haze-fog
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Liu, D.-J.; Li, L. Application Study of Comprehensive Forecasting Model Based on Entropy Weighting Method on Trend of PM2.5 Concentration in Guangzhou, China. Int. J. Environ. Res. Public Health 2015, 12, 7085-7099.

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