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http://dx.doi.org/10.9716/KITS.2019.18.1.173

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

Lim, Joon-Mook (한밭대학교 공과대학 창의융합학과)
Publication Information
Journal of Information Technology Services / v.18, no.1, 2019 , pp. 173-186 More about this Journal
Abstract
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.
Keywords
Fine Dust; Machine Learning; Weather Data; Bigdata;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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