Prediction of Fine Dust in Gyeonggi-do Industrial Complex using Machine Learning Methods

Dong-Jun Won, Sun-Kyum Kim, Yeonghun Kim, Gyuwon Song

http://doi.org/10.5626/JOK.2021.48.7.764

Recently, research on fine dust has been conducted through various prediction techniques. However, currently the research focused on PM₁₀ concentration prediction, and thus it is necessary to develop a model capable of predicting PM_2.5 concentration. In this paper, we have collected air quality, weather, and traffic of the Banwol Shihwa National Industrial Complex in the recent two years. The significance of the variable been identified through correlation analysis and regression analysis among PM_2.5 and PM₁₀, SO₂, NO₂, CO, O₃, temperature, humidity, wind direction, wind speed, precipitation, road section vehicle speed for each vehicle. Next, the data has been used to predict PM_2.5 concentration based on time in the industrial complex. Through the artificial intelligence techniques, Random Forest, XGBoost, LightGBM, Deep neural network and Voting models, PM_2.5 concentration industrial complexes been predicted on an hourly basis, and comparative analysis been conducted based on RMSE. As a result of prediction, RMSE was 6.27, 6.41, 6.22, 6.64, and 6.12, respectively, and each technique showed very high performance compared to 10.77 of the technique predicted by Air Korea.