Deukwoo Lee, Soowon Lee

KIPS Transactions on Software and Data Engineering, Vol. 9, No. 4, pp. 129-136, Apr. 2020
https://doi.org/10.3745/KTSDE.2020.9.4.129,   PDF Download:
Keywords: Particulate Matter, PM2.5, Time Series Data, Machine Learning, Random Forest
Abstract

PM2.5 which is a very tiny air particulate matter even smaller than PM10 has been issued in the environmental problem. Since PM2.5 can cause eye diseases or respiratory problems and infiltrate even deep blood vessels in the brain, it is important to predict PM2.5. However, it is difficult to predict PM2.5 because there is no clear explanation yet regarding the creation and the movement of PM2.5. Thus, prediction methods which not only predict PM2.5 accurately but also have the interpretability of the result are needed. To predict hourly PM2.5 of Seoul city, we propose a method using random forest with the adjusted bootstrap number from the time series ground data preprocessed on different sources. With this method, the prediction model can be trained uniformly on hourly information and the result has the interpretability. To evaluate the prediction performance, we conducted comparative experiments. As a result, the performance of the proposed method was superior against other models in all labels. Also, the proposed method showed the importance of the variables regarding the creation of PM2.5 and the effect of China.

Statistics
Cite this article