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http://dx.doi.org/10.6109/jkiice.2018.22.4.595

Development of Data Mining Algorithm for Implementation of Fine Dust Numerical Prediction Model  

Cha, Jinwook (Department of Computer Science, The University of Suwon)
Kim, Jangyoung (Department of Computer Science, The University of Suwon)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.22, no.4, 2018 , pp. 595-601 More about this Journal
Abstract
Recently, as the fine dust level has risen rapidly, there is a great interest. Exposure to fine dust is associated with the development of respiratory and cardiovascular diseases and has been reported to increase death rate. In addition, there exist damage to fine dusts continues at industrial sites. However, exposure to fine dust is inevitable in modern life. Therefore, predicting and minimizing exposure to fine dust is the most efficient way to reduce health and industrial damages. Existing fine dust prediction model is estimated as good, normal, poor, and very bad, depending on the concentration range of the fine dust rather than the concentration value. In this paper, we study and implement to predict the PM10 level by applying the Artificial neural network algorithm and the K-Nearest Neighbor algorithm, which are machine learning algorithms, using the actual weather and air quality data.
Keywords
Fine Dust; Data Mining; ANN Algorithm; K-NN Algorithm;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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