Non-linearity Mitigation Method of Particulate Matter using Machine Learning Clustering Algorithms

기계학습 군집 알고리즘을 이용한 미세먼지 비선형성 완화방안

  • Lee, Sang-gwon (Department of Electrical, Electronics & Communication Engineering, Korea University of Technology and Education(KOREATECH)) ;
  • Cho, Kyoung-woo (Department of Electrical, Electronics & Communication Engineering, Korea University of Technology and Education(KOREATECH)) ;
  • Oh, Chang-heon (Department of Electrical, Electronics & Communication Engineering, Korea University of Technology and Education(KOREATECH))
  • Published : 2019.05.23

Abstract

As the generation of high concentration particulate matter increases, much attention is focused on the prediction of particulate matter. Particulate matter refers to particulate matter less than $10{\mu}m$ diameter in the atmosphere and is affected by weather changes such as temperature, relative humidity and wind speed. Therefore, various studies have been conducted to analyze the correlation with weather information for particulate matter prediction. However, the nonlinear time series distribution of particulate matter increases the complexity of the prediction model and can lead to inaccurate predictions. In this paper, we try to mitigate the nonlinear characteristics of particulate matter by using cluster algorithm and classification algorithm of machine learning. The machine learning algorithms used are agglomerative clustering, density-based spatial clustering of applications with noise(DBSCAN).