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http://dx.doi.org/10.9717/kmms.2020.23.6.747

Improvement of PM Forecasting Performance by Outlier Data Removing  

Jeon, Young Tae (Dept. of Computer Eng., Anyang University)
Yu, Suk Hyun (Dept. of Information & Communication Eng., Anyang University)
Kwon, Hee Yong (Dept. of Computer Eng., Anyang University)
Publication Information
Journal of Korea Multimedia Society / v.23, no.6, 2020 , pp. 747-755 More about this Journal
Abstract
In this paper, we deal with outlier data problems that occur when constructing a PM2.5 fine dust forecasting system using a neural network. In general, when learning a neural network, some of the data are not helpful for learning, but rather disturbing. Those are called outlier data. When they are included in the training data, various problems such as overfitting occur. In building a PM2.5 fine dust concentration forecasting system using neural network, we have found several outlier data in the training data. We, therefore, remove them, and then make learning 3 ways. Over_outlier model removes outlier data that target concentration is low, but the model forecast is high. Under_outlier model removes outliers data that target concentration is high, but the model forecast is low. All_outlier model removes both Over_outlier and Under_outlier data. We compare 3 models with a conventional outlier removal model and non-removal model. Our outlier removal model shows better performance than the others.
Keywords
$PM_{2.5}$ Forecasting; Deep Neural Network; Outlier; AI;
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Times Cited By KSCI : 6  (Citation Analysis)
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