Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Forecasting daily PM10 concentrations in Seoul using various data mining techniques

  • Choi, Ji-Eun (Department of Statistics, Ewha Womans University) ;
  • Lee, Hyesun (Department of Statistics, Ewha Womans University) ;
  • Song, Jongwoo (Department of Statistics, Ewha Womans University)
  • Received : 2017.11.02
  • Accepted : 2017.12.27
  • Published : 2018.03.31
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Abstract

Interest in $PM_{10}$ concentrations have increased greatly in Korea due to recent increases in air pollution levels. Therefore, we consider a forecasting model for next day $PM_{10}$ concentration based on the principal elements of air pollution, weather information and Beijing $PM_{2.5}$. If we can forecast the next day $PM_{10}$ concentration level accurately, we believe that this forecasting can be useful for policy makers and public. This paper is intended to help forecast a daily mean $PM_{10}$, a daily max $PM_{10}$ and four stages of $PM_{10}$ provided by the Ministry of Environment using various data mining techniques. We use seven models to forecast the daily $PM_{10}$, which include five regression models (linear regression, Randomforest, gradient boosting, support vector machine, neural network), and two time series models (ARIMA, ARFIMA). As a result, the linear regression model performs the best in the $PM_{10}$ concentration forecast and the linear regression and Randomforest model performs the best in the $PM_{10}$ class forecast. The results also indicate that the $PM_{10}$ in Seoul is influenced by Beijing $PM_{2.5}$ and air pollution from power stations in the west coast.

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