Journal of Korea Artificial Intelligence Association

Korea Distribution Science Association (한국유통과학회)
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A Study on Fine Dust Prediction Based on Internal Factors Using Machine Learning

머신러닝을 활용한 내부 발생 요인 기반의 미세먼지 예측에 관한 연구

  • Received : 2023.10.28
  • Accepted : 2023.12.30
  • Published : 2023.12.31
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Abstract

This study aims to enhance the accuracy of fine dust predictions by analyzing various factors within the local environment, in addition to atmospheric conditions. In the atmospheric environment, meteorological and air pollution data were utilized, and additional factors contributing to fine dust generation within the region, such as traffic volume and electricity transaction data, were sequentially incorporated for analysis. XGBoost, Random Forest, and ANN (Artificial Neural Network) were employed for the analysis. As variables were added, all algorithms demonstrated improved performance. Particularly noteworthy was the Artificial Neural Network, which, when using atmospheric conditions as a variable, resulted in an MAE of 6.25. Upon the addition of traffic volume, the MAE decreased to 5.49, and further inclusion of power transaction data led to a notable improvement, resulting in an MAE of 4.61. This research provides valuable insights for proactive measures against air pollution by predicting future fine dust levels.

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References

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