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Research and Application of an Air Quality Early Warning System Based on a Modified Least Squares Support Vector Machine and a Cloud Model

Author

Listed:
  • Jianzhou Wang

    (School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China)

  • Tong Niu

    (School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China)

  • Rui Wang

    (School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China)

Abstract

The worsening atmospheric pollution increases the necessity of air quality early warning systems (EWSs). Despite the fact that a massive amount of investigation about EWS in theory and practicality has been conducted by numerous researchers, studies concerning the quantification of uncertain information and comprehensive evaluation are still lacking, which impedes further development in the area. In this paper, firstly a comprehensive warning system is proposed, which consists of two vital indispensable modules, namely effective forecasting and scientific evaluation, respectively. For the forecasting module, a novel hybrid model combining the theory of data preprocessing and numerical optimization is first developed to implement effective forecasting for air pollutant concentration. Especially, in order to further enhance the accuracy and robustness of the warning system, interval forecasting is implemented to quantify the uncertainties generated by forecasts, which can provide significant risk signals by using point forecasting for decision-makers. For the evaluation module, a cloud model, based on probability and fuzzy set theory, is developed to perform comprehensive evaluations of air quality, which can realize the transformation between qualitative concept and quantitative data. To verify the effectiveness and efficiency of the warning system, extensive simulations based on air pollutants data from Dalian in China were effectively implemented, which illustrate that the warning system is not only remarkably high-performance, but also widely applicable.

Suggested Citation

  • Jianzhou Wang & Tong Niu & Rui Wang, 2017. "Research and Application of an Air Quality Early Warning System Based on a Modified Least Squares Support Vector Machine and a Cloud Model," IJERPH, MDPI, vol. 14(3), pages 1-33, March.
    • Handle: RePEc:gam:jijerp:v:14:y:2017:i:3:p:249-:d:91975
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    References listed on IDEAS

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    1. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    2. Xiao, Liye & Wang, Jianzhou & Hou, Ru & Wu, Jie, 2015. "A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting," Energy, Elsevier, vol. 82(C), pages 524-549.
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    Cited by:

    1. Jiaming Zhu & Peng Wu & Huayou Chen & Ligang Zhou & Zhifu Tao, 2018. "A Hybrid Forecasting Approach to Air Quality Time Series Based on Endpoint Condition and Combined Forecasting Model," IJERPH, MDPI, vol. 15(9), pages 1-19, September.
    2. Zongxi Qu & Xiaogang Hao & Fazhen Zhao & Chunhua Niu, 2023. "Uncertainty analysis–forecasting system based on decomposition–ensemble framework for PM2.5 concentration forecasting in China," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2027-2044, December.
    3. Ali Asghar Heidari & Mehdi Akhoondzadeh & Huiling Chen, 2022. "A Wavelet PM2.5 Prediction System Using Optimized Kernel Extreme Learning with Boruta-XGBoost Feature Selection," Mathematics, MDPI, vol. 10(19), pages 1-35, September.
    4. Lu Bai & Jianzhou Wang & Xuejiao Ma & Haiyan Lu, 2018. "Air Pollution Forecasts: An Overview," IJERPH, MDPI, vol. 15(4), pages 1-44, April.
    5. Shankar Subramaniam & Naveenkumar Raju & Abbas Ganesan & Nithyaprakash Rajavel & Maheswari Chenniappan & Chander Prakash & Alokesh Pramanik & Animesh Kumar Basak & Saurav Dixit, 2022. "Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health: A Narrative Review," Sustainability, MDPI, vol. 14(16), pages 1-36, August.
    6. Jie Zhao & Linjiang Yuan & Kun Sun & Han Huang & Panbo Guan & Ce Jia, 2022. "Forecasting Fine Particulate Matter Concentrations by In-Depth Learning Model According to Random Forest and Bilateral Long- and Short-Term Memory Neural Networks," Sustainability, MDPI, vol. 14(15), pages 1-18, August.

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