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Predicting Concentration of PM10 using Optimal Parameters of Deep Neural Network


Authors



Abstract

Accurate prediction of fine dust (PM10) concentration is currently recognized as an important problem in East Asia. In this paper, we try to predict the concentration of PM10 using Deep Neural Network (DNN). Meteorological factors, yellow dust (sand), fog, and PM10 are used as input data. We test two cases. The first case predicts the concentration of PM10 on the next day using the dayu2019s weather forecast data. The second case predicts the concentration of PM10 on the next day using the previous dayu2019s data. Based on this, we compare the various performance results from the DNN model. In the experiments, we get about 76% of accuracy with the proposed system.


Keywords


Pages

Total Pages: 8

DOI
10.31209/2019.100000095


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Published

Online Article

JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




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