Forecasting daily PM10 concentrations in Seoul using various data mining techniques
- 주제(키워드) ARFIMA , Gradient boosting , Linear regression , Neural network , PM10 concentration , Randomforest , Support vector machine
- 등재 SCOPUS, KCI등재
- 발행기관 Korean Statistical Society
- 발행년도 2018
- URI http://www.dcollection.net/handler/ewha/000000156221
- 본문언어 영어
- Published As http://dx.doi.org/10.29220/CSAM.2018.25.2.199
- 저작권 이화여자대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
Interest in PM10 concentrations have increased greatly in Korea due to recent increases in air pollution levels. Therefore, we consider a forecasting model for next day PM10 concentration based on the principal elements of air pollution, weather information and Beijing PM2.5. If we can forecast the next day PM10 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 PM10, a daily max PM10 and four stages of PM10 provided by the Ministry of Environment using various data mining techniques. We use seven models to forecast the daily PM10, 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 PM10 concentration forecast and the linear regression and Randomforest model performs the best in the PM10 class forecast. The results also indicate that the PM10 in Seoul is influenced by Beijing PM2.5 and air pollution from power stations in the west coast. © 2018 The Korean Statistical Society, and Korean International Statistical Society.
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