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Evaluation of Machine Learning Models in Air Pollution Prediction for a Case Study of Macau as an Effort to Comply with UN Sustainable Development Goals

Author

Listed:
  • Thomas M. T. Lei

    (Institute of Science and Environment, University of Saint Joseph, Macau, China)

  • Jianxiu Cai

    (Faculty of Applied Sciences, Macau Polytechnic University, Macau, China)

  • Altaf Hossain Molla

    (Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia)

  • Tonni Agustiono Kurniawan

    (College of Environment and Ecology, Xiamen University, Xiamen 361102, China)

  • Steven Soon-Kai Kong

    (Department of Atmospheric Sciences, National Central University, Taoyuan 32001, Taiwan)

Abstract

To comply with the United Nations Sustainable Development Goals (UN SDGs), in particular with SDG 3, SDG 11, and SDG 13, a reliable air pollution prediction model must be developed to construct a sustainable, safe, and resilient city and mitigate climate change for a double win. Machine learning (ML) and deep learning (DL) models have been applied to datasets in Macau to predict the daily levels of roadside air pollution in the Macau peninsula, situated near the historical sites of Macau. Macau welcomed over 28 million tourists in 2023 as a popular tourism destination. Still, an accurate air quality forecast has not been in place for many years due to the lack of a reliable emission inventory. This work will develop a dependable air pollution prediction model for Macau, which is also the novelty of this study. The methods, including random forest (RF), support vector regression (SVR), artificial neural network (ANN), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), were applied and successful in the prediction of daily air pollution levels in Macau. The prediction model was trained using the air quality and meteorological data from 2013 to 2019 and validated using the data from 2020 to 2021. The model performance was evaluated based on the root mean square error (RMSE), mean absolute error (MAE), Pearson’s correlation coefficient (PCC), and Kendall’s tau coefficient (KTC). The RF model best predicted PM 10 , PM 2.5 , NO 2 , and CO concentrations with the highest PCC and KTC in a daily air pollution prediction. In addition, the SVR model had the best stability and repeatability compared to other models, with the lowest SD in RMSE, MAE, PCC, and KTC after five model runs. Therefore, the results of this study show that the RF model is more efficient and performs better than other models in the prediction of air pollution for the dataset of Macau.

Suggested Citation

  • Thomas M. T. Lei & Jianxiu Cai & Altaf Hossain Molla & Tonni Agustiono Kurniawan & Steven Soon-Kai Kong, 2024. "Evaluation of Machine Learning Models in Air Pollution Prediction for a Case Study of Macau as an Effort to Comply with UN Sustainable Development Goals," Sustainability, MDPI, vol. 16(17), pages 1-15, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7477-:d:1466825
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    References listed on IDEAS

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    1. Grigore Cican & Adrian-Nicolae Buturache & Radu Mirea, 2023. "Applying Machine Learning Techniques in Air Quality Prediction—A Bucharest City Case Study," Sustainability, MDPI, vol. 15(11), pages 1-20, May.
    2. Wenhua Yu & Yuming Guo & Liuhua Shi & Shanshan Li, 2020. "The association between long-term exposure to low-level PM2.5 and mortality in the state of Queensland, Australia: A modelling study with the difference-in-differences approach," PLOS Medicine, Public Library of Science, vol. 17(6), pages 1-19, June.
    3. Beidi Diao & Lei Ding & Qiong Zhang & Junli Na & Jinhua Cheng, 2020. "Impact of Urbanization on PM 2.5 -Related Health and Economic Loss in China 338 Cities," IJERPH, MDPI, vol. 17(3), pages 1-18, February.
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