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http://dx.doi.org/10.14372/IEMEK.2018.13.4.179

Particulate Matter (PM2.5) State Inference by Rule Induction  

Choi, Rock-Hyun (DGIST)
Kang, Won-Seok (DGIST)
Son, Chang-Sik (DGIST)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.13, no.4, 2018 , pp. 179-185 More about this Journal
Abstract
Particulate Matter (PM2.5) has various adverse effects on health. Climate and industry activity and traffic volume are the main causes, especially in urban area. In order to construct an effective forecasting system, many measurement systems are required, but it is impossible in reality. Therefore, in this study, we propose a method to infer PM2.5 condition by using rule induction technique. The experimental results showed a classification accuracy of 71%.
Keywords
Air quality; Weather; Data mining; Rule induction; PM2.5;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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