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http://dx.doi.org/10.9717/kmms.2019.22.10.1187

Improvement of PM10 Forecasting Performance using DNN and Secondary Data  

Yu, SukHyun (Dept. of Information & Communication Eng., Anyang University)
Jeon, YoungTae (Dept. of Computer Eng., Anyang University)
Publication Information
Journal of Korea Multimedia Society / v.22, no.10, 2019 , pp. 1187-1198 More about this Journal
Abstract
In this study, we propose a new $PM_{10}$ forecasting model for Seoul region using DNN(Deep Neural Network) and secondary data. The previous numerical and Julian forecast model have been developed using primary data such as weather and air quality measurements. These models give excellent results for accuracy and false alarms, but POD is not good for the daily life usage. To solve this problem, we develop four secondary factors composed with primary data, which reflect the correlations between primary factors and high $PM_{10}$ concentrations. The proposed 4 models are A(Anomaly), BT(Back trajectory), CB(Contribution), CS(Cosine similarity), and ALL(model using all 4 secondary data). Among them, model ALL shows the best performance in all indicators, especially the PODs are improved.
Keywords
$PM_{10}$ Forecasting; Air Quality Index; Deep Neural Network; AI;
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Times Cited By KSCI : 1  (Citation Analysis)
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