기상환경데이터와 머신러닝을 활용한 미세먼지농도 예측 모델

An Estimation Model of Fine Dust Concentration Using Meteorological Environment Data and Machine Learning

Recently, as the amount of fine dust has risen rapidly, our interest is increasing day by day. It is virtually impossible to remove fine dust. However, it is best to predict the concentration of fine dust and minimize exposure to it. In this study, we developed a mathematical model that can predict the concentration of fine dust using various information related to the weather and air quality, which is provided in real time in Air Korea (http://www.airkorea.or.kr/) and Weather Data Open Portal (https://data.kma.go.kr/). In the mathematical model, various domestic seasonal variables and atmospheric state variables are extracted by multiple regression analysis. The parameters that have significant influence on the fine dust concentration are extracted, and using ANN (Artificial　Neural　Network) and SVM (Support Vector Machine), which are machine learning techniques, we proposed a prediction model. The proposed model can verify its effectiveness by using past dust and weather big data.