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Urban air quality forecasting based on multi-dimensional collaborative Support Vector Regression (SVR): A case study of Beijing-Tianjin-Shijiazhuang

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  • Bing-Chun Liu
  • Arihant Binaykia
  • Pei-Chann Chang
  • Manoj Kumar Tiwari
  • Cheng-Chin Tsao

Abstract

Today, China is facing a very serious issue of Air Pollution due to its dreadful impact on the human health as well as the environment. The urban cities in China are the most affected due to their rapid industrial and economic growth. Therefore, it is of extreme importance to come up with new, better and more reliable forecasting models to accurately predict the air quality. This paper selected Beijing, Tianjin and Shijiazhuang as three cities from the Jingjinji Region for the study to come up with a new model of collaborative forecasting using Support Vector Regression (SVR) for Urban Air Quality Index (AQI) prediction in China. The present study is aimed to improve the forecasting results by minimizing the prediction error of present machine learning algorithms by taking into account multiple city multi-dimensional air quality information and weather conditions as input. The results show that there is a decrease in MAPE in case of multiple city multi-dimensional regression when there is a strong interaction and correlation of the air quality characteristic attributes with AQI. Also, the geographical location is found to play a significant role in Beijing, Tianjin and Shijiazhuang AQI prediction.

Suggested Citation

  • Bing-Chun Liu & Arihant Binaykia & Pei-Chann Chang & Manoj Kumar Tiwari & Cheng-Chin Tsao, 2017. "Urban air quality forecasting based on multi-dimensional collaborative Support Vector Regression (SVR): A case study of Beijing-Tianjin-Shijiazhuang," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-17, July.
    • Handle: RePEc:plo:pone00:0179763
    DOI: 10.1371/journal.pone.0179763
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    References listed on IDEAS

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    1. Lujin Hu & Jiping Liu & Zongyi He, 2016. "Self-Adaptive Revised Land Use Regression Models for Estimating PM 2.5 Concentrations in Beijing, China," Sustainability, MDPI, vol. 8(8), pages 1-23, August.
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    Cited by:

    1. Yongli Zhang & Sanggyun Na, 2018. "Research on the Topological Properties of Air Quality Index Based on a Complex Network," Sustainability, MDPI, vol. 10(4), pages 1-20, April.
    2. Chih‐Hsuan Wang & Chia‐Rong Chang, 2023. "Forecasting air quality index considering socioeconomic indicators and meteorological factors: A data granularity perspective," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1261-1274, August.
    3. Muhammad Muhitur Rahman & Md Shafiullah & Syed Masiur Rahman & Abu Nasser Khondaker & Abduljamiu Amao & Md. Hasan Zahir, 2020. "Soft Computing Applications in Air Quality Modeling: Past, Present, and Future," Sustainability, MDPI, vol. 12(10), pages 1-33, May.
    4. Cabaneros, Sheen Mclean & Calautit, John Kaiser & Hughes, Ben, 2020. "Spatial estimation of outdoor NO2 levels in Central London using deep neural networks and a wavelet decomposition technique," Ecological Modelling, Elsevier, vol. 424(C).

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