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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Dong-jun Liu & Li Li, 2015. "Application Study of Comprehensive Forecasting Model Based on Entropy Weighting Method on Trend of PM 2.5 Concentration in Guangzhou, China," IJERPH, MDPI, vol. 12(6), pages 1-15, June.
    • Handle: RePEc:gam:jijerp:v:12:y:2015:i:6:p:7085-7099:d:51582
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    References listed on IDEAS

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    1. Prybutok, Victor R. & Yi, Junsub & Mitchell, David, 2000. "Comparison of neural network models with ARIMA and regression models for prediction of Houston's daily maximum ozone concentrations," European Journal of Operational Research, Elsevier, vol. 122(1), pages 31-40, April.
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    Cited by:

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    2. Mei Yang & Mengyun Jiao & Jinyu Zhang, 2022. "Coupling Coordination and Interactive Response Analysis of Ecological Environment and Urban Resilience in the Yangtze River Economic Belt," IJERPH, MDPI, vol. 19(19), pages 1-23, September.
    3. Yiqun Shang & Dongya Liu & Yi Chen, 2022. "Evaluation of Urban Intensive Land Use Degree with GEE Support: A Case Study in the Pearl River Delta Region, China," Sustainability, MDPI, vol. 14(20), pages 1-15, October.
    4. Zifeng Liang & Manli Zhang & Qingduo Mao & Bingxin Yu & Ben Ma, 2018. "Improvement of Eco-Efficiency in China: A Comparison of Mandatory and Hybrid Environmental Policy Instruments," IJERPH, MDPI, vol. 15(7), pages 1-20, July.
    5. Junfeng Kang & Xinyi Zou & Jianlin Tan & Jun Li & Hamed Karimian, 2023. "Short-Term PM 2.5 Concentration Changes Prediction: A Comparison of Meteorological and Historical Data," Sustainability, MDPI, vol. 15(14), pages 1-24, July.
    6. Ping Zhang & Bo Hong & Liang He & Fei Cheng & Peng Zhao & Cailiang Wei & Yunhui Liu, 2015. "Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural N," IJERPH, MDPI, vol. 12(10), pages 1-25, September.

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