IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v12y2015i6p7085-7099d51582.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/12/6/7085/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/12/6/7085/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Xu, Alan, 2022. "Air pollution and mediation effects in stock market, longitudinal evidence from China," International Review of Financial Analysis, Elsevier, vol. 83(C).
    3. 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.
    4. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Tai-Yue & Huang, Chien-Yu, 2007. "Improving forecasting performance by employing the Taguchi method," European Journal of Operational Research, Elsevier, vol. 176(2), pages 1052-1065, January.
    2. Zhao, Yuan & Zhang, Weiguo & Gong, Xue & Wang, Chao, 2021. "A novel method for online real-time forecasting of crude oil price," Applied Energy, Elsevier, vol. 303(C).
    3. Florin Dan Pieleanu, 2016. "Predicting The Evolution Of Bet Index, Using An Arima Model," Romanian Economic Business Review, Romanian-American University, vol. 10(1), pages 151-162, May.
    4. Koutroumanidis, Theodoros & Ioannou, Konstantinos & Arabatzis, Garyfallos, 2009. "Predicting fuelwood prices in Greece with the use of ARIMA models, artificial neural networks and a hybrid ARIMA-ANN model," Energy Policy, Elsevier, vol. 37(9), pages 3627-3634, September.
    5. Sotirios Bersimis & Stavros Degiannakis & Dimitrios Georgakellos, 2017. "Real-time monitoring of carbon monoxide using value-at-risk measure and control charting," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(1), pages 89-108, January.
    6. Chu, Ching-Wu & Zhang, Guoqiang Peter, 2003. "A comparative study of linear and nonlinear models for aggregate retail sales forecasting," International Journal of Production Economics, Elsevier, vol. 86(3), pages 217-231, December.
    7. Marques, Alex & Lacerda, Daniel Pacheco & Camargo, Luís Felipe Riehs & Teixeira, Rafael, 2014. "Exploring the relationship between marketing and operations: Neural network analysis of marketing decision impacts on delivery performance," International Journal of Production Economics, Elsevier, vol. 153(C), pages 178-190.
    8. Florin Dan PIELEANU, 2016. "Comparative Study In Estimating Volkswagen’S Price: Arima Versus Ann," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 64(2), pages 98-109, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:12:y:2015:i:6:p:7085-7099:d:51582. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.