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A Novel Air Quality Early-Warning System Based on Artificial Intelligence

Author

Listed:
  • Xinyue Mo

    (College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China)

  • Lei Zhang

    (College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China)

  • Huan Li

    (School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China)

  • Zongxi Qu

    (School of Management, Lanzhou University, Lanzhou 730000, China)

Abstract

The problem of air pollution is a persistent issue for mankind and becoming increasingly serious in recent years, which has drawn worldwide attention. Establishing a scientific and effective air quality early-warning system is really significant and important. Regretfully, previous research didn’t thoroughly explore not only air pollutant prediction but also air quality evaluation, and relevant research work is still scarce, especially in China. Therefore, a novel air quality early-warning system composed of prediction and evaluation was developed in this study. Firstly, the advanced data preprocessing technology Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with the powerful swarm intelligence algorithm Whale Optimization Algorithm (WOA) and the efficient artificial neural network Extreme Learning Machine (ELM) formed the prediction model. Then the predictive results were further analyzed by the method of fuzzy comprehensive evaluation, which offered intuitive air quality information and corresponding measures. The proposed system was tested in the Jing-Jin-Ji region of China, a representative research area in the world, and the daily concentration data of six main air pollutants in Beijing, Tianjin, and Shijiazhuang for two years were used to validate the accuracy and efficiency. The results show that the prediction model is superior to other benchmark models in pollutant concentration prediction and the evaluation model is satisfactory in air quality level reporting compared with the actual status. Therefore, the proposed system is believed to play an important role in air pollution control and smart city construction all over the world in the future.

Suggested Citation

  • Xinyue Mo & Lei Zhang & Huan Li & Zongxi Qu, 2019. "A Novel Air Quality Early-Warning System Based on Artificial Intelligence," IJERPH, MDPI, vol. 16(19), pages 1-25, September.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:19:p:3505-:d:269055
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    References listed on IDEAS

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    Cited by:

    1. Xiaobing Yu & Chenliang Li & Hong Chen & Zhonghui Ji, 2020. "Evaluate Air Pollution by Promethee Ranking in Yangtze River Delta of China," IJERPH, MDPI, vol. 17(2), pages 1-18, January.
    2. Yun Tan & Changshu Zhan & Youchun Pi & Chunhui Zhang & Jinghui Song & Yan Chen & Amir-Mohammad Golmohammadi, 2023. "A Hybrid Algorithm Based on Social Engineering and Artificial Neural Network for Fault Warning Detection in Hydraulic Turbines," Mathematics, MDPI, vol. 11(10), pages 1-18, May.
    3. 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.

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