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A Modified γ -Sutte Indicator for Air Quality Index Prediction

Author

Listed:
  • Dong-Her Shih

    (Department of Information Management, National Yunlin University of Science and Technology, Douliu 64002, Taiwan)

  • To Thi Hien

    (Faculty of Environment, University of Science, 227 Nguyen Van Cu Street, District 5, Ho Chi Minh City 700000, Vietnam
    Vietnam National University, Linh Trung Ward, Thu Duc District, Ho Chi Minh City 700000, Vietnam)

  • Ly Sy Phu Nguyen

    (Faculty of Environment, University of Science, 227 Nguyen Van Cu Street, District 5, Ho Chi Minh City 700000, Vietnam
    Vietnam National University, Linh Trung Ward, Thu Duc District, Ho Chi Minh City 700000, Vietnam)

  • Ting-Wei Wu

    (Department of Information Management, National Yunlin University of Science and Technology, Douliu 64002, Taiwan)

  • Yen-Ting Lai

    (Department of Information Management, National Yunlin University of Science and Technology, Douliu 64002, Taiwan)

Abstract

Air pollution has become an essential issue in environmental protection. The Air Quality Index (AQI) is often used to determine the severity of air pollution. When the AQI reaches the red level, the proportion of asthma patients seeking medical treatment will increase by 30% more than usual. If the AQI can be predicted in advance, the benefits of early warning can be achieved. In recent years, a scholar has proposed an α -Sutte indicator which shows its excellence in time series prediction. However, the calculation of α -Sutte indicators uses a fixed weight. Thus, a β -Sutte indicator, using a dynamic weight with a high computation cost, has appeared. However, the computational complexity and sliding window required of the β -Sutte indicator are still high compared to the α -Sutte indicator. In this study, a modified γ -Sutte indicator, using a dynamic weight with a lower computational cost than the β -Sutte indicator, is proposed. In order to prove that the proposed γ -Sutte indicator has good generalization ability and is transferable, this study uses data from different regions and periods to predict the AQI. The results showed that the prediction accuracy of the γ -Sutte indicator proposed was better than other methods.

Suggested Citation

  • Dong-Her Shih & To Thi Hien & Ly Sy Phu Nguyen & Ting-Wei Wu & Yen-Ting Lai, 2022. "A Modified γ -Sutte Indicator for Air Quality Index Prediction," Mathematics, MDPI, vol. 10(17), pages 1-15, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3060-:d:897060
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    References listed on IDEAS

    as
    1. Li, Xing & Hu, Zhigao & Cao, Jianhua & Xu, Xing, 2022. "The impact of environmental accountability on air pollution: A public attention perspective," Energy Policy, Elsevier, vol. 161(C).
    2. Ren, Yi-Shuai & Narayan, Seema & Ma, Chao-qun, 2021. "Air quality, COVID-19, and the oil market: Evidence from China’s provinces," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 58-72.
    3. Ansari Saleh Ahmar, 2017. "Sutte Indicator: A Technical Indicator in Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 7(2), pages 223-226.
    4. Ahmar, Ansari Saleh & Rahman, Abdul & Mulbar, Usman, 2017. "Implementation of α-Sutte Indicator to Forecasting Consumer Price Index in Turkey," INA-Rxiv s8jzu, Center for Open Science.
    5. Li, Ying & Chiu, Yung-ho & Lu, Liang Chun, 2018. "Energy and AQI performance of 31 cities in China," Energy Policy, Elsevier, vol. 122(C), pages 194-202.
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