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지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.6
- 수록면
- 747 - 755 (9page)
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초록· 키워드
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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.
목차
ABSTRACT
1. 서론
2. Outlier
3. 실험 및 결과
4. 결론
REFERENCE
참고문헌 (19)
참고문헌 신청
S. Lee, C. Ho, and Y. Choi, “High-PM10 Concentration Episodes in Seoul, Korea: Background Sources and Related Meteorological Conditions,” Atmospheric Environment, Vol. 45, Issue 39, pp. 7240-7247, 2011.
S. Lee, C. Ho, Y. Lee, H. Choi, and C. Song, “Influence of Transboundary Air Pollutants for China on the High PM10 Episode in Seoul, Korea for the Period October 16-20, 2008,” Atmospheric Environment, Vol. 77, pp. 430- 439, 2013.
H. Oh, C. Ho, J. Kim, D. Chen, S. Lee, Y. Choi, et al., “Long-Range Transport of Air Pollutants Originating in China: A possible Major Cause of Mulit-Day High-PM10 Episodes During Cold Season in Seoul, Korea,” Atmospheric Environment, Vol. 109, pp. 23-30, 2015.
NIER, A Study of Construction of Air Quality Forecasting System Using Artificial Intelligence( I) , NIER-SP2017-148, 11-1480523-000 3221-01, 2017.
A.B. Chelani, D.G. Gajghate, and M.Z. Hasan, “Prediction of Ambient PM10 and Toxic Metals Using Artificial Neural Networks,” J ournal of the Air and Waste Management Association, Vol. 52, Issue 7, pp. 805-810, 2002.
S. Lee, C. Ho, Y. Lee, H. Choi, and C. Song, “Influence of Transboundary Air Pollutants for China on the High PM10 Episode in Seoul, Korea for the Period October 16-20, 2008,” Atmospheric Environment, Vol. 77, pp. 430- 439, 2013.
H. Oh, C. Ho, J. Kim, D. Chen, S. Lee, Y. Choi, et al., “Long-Range Transport of Air Pollutants Originating in China: A possible Major Cause of Mulit-Day High-PM10 Episodes During Cold Season in Seoul, Korea,” Atmospheric Environment, Vol. 109, pp. 23-30, 2015.
NIER, A Study of Construction of Air Quality Forecasting System Using Artificial Intelligence( I) , NIER-SP2017-148, 11-1480523-000 3221-01, 2017.
A.B. Chelani, D.G. Gajghate, and M.Z. Hasan, “Prediction of Ambient PM10 and Toxic Metals Using Artificial Neural Networks,” J ournal of the Air and Waste Management Association, Vol. 52, Issue 7, pp. 805-810, 2002.
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UCI(KEPA) : I410-ECN-0101-2020-004-000871696
