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http://dx.doi.org/10.9717/kmms.2019.22.11.1300

Development of PM10 Forecasting Model for Seoul Based on DNN Using East Asian Wide Area Data  

Yu, SukHyun (Dept. of Information & Communication Eng., Anyang University)
Publication Information
Journal of Korea Multimedia Society / v.22, no.11, 2019 , pp. 1300-1312 More about this Journal
Abstract
BSTRACT In this paper, PM10 forecast model using DNN(Deep Neural Network) is developed for Seoul region. The previous Julian forecast model has been developed using weather and air quality data of Seoul region only. This model gives excellent results for accuracy and false alarm rates, but poor result for POD(Probability of Detection). To solve this problem, an WA(Wide Area) forecasting model that uses Chinese data is developed. The data is highly correlated with the emergence of high concentrations of PM10 in Korea. As a result, the WA model shows better accuracy, and POD improving of 3%(D+0), 21%(D+1), and 36%(D+2) for each forecast period compared with the Julian model.
Keywords
AI; DNN; PM10 Forecasting; Air Quality;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 NIER, A Study of Construction of Air Quality Forecasting System Using Artificial Intelligence(I), NIER-SP2017-148, 11-1480523-000 3221-01, 2017.
2 S. Lee, C. Ho, and Y. Choi, “Hig-PM10 Concentration Episodes in Seoul, Korea: Background Sources and Related Meteorological Conditions,” Atmospheric Environment, Vol. 45, No. 39, pp. 7240-7247, 2011.   DOI
3 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.   DOI
4 H. Oh, C. Ho, J. Kim, D. Chen, S. Lee, Y. Choi, et al., "Long-range Transport of Air Pollutants Originating in China: Apossible Major Cause of Multi-day High-PM10 Episodes During Cold Season in Seoul, Korea," Atmospheric Environment, Vol. 109, pp. 23-30, 2015.   DOI
5 L.G. McKendry, "Evaluation of Artificial Neural Networks for Fine Particulate Pollution(PM10 and PM2.5) Forecasting," Journal of the Air and Waste Management Association, Vol. 52, pp. 1096-1101, 2002.   DOI
6 D. Voukantsis, K. Karatzas, J. Kukkonen, T. Rasanen, A. Karppinen, and M. Kolehmainen, "Intercomparsion of Air Quality Data Using Principal Component Analysis, and Forecasting of PM10 and PM2.5 Concentrations Using Aritficial Neural Network, in Thessaloniki and Helsinki," Science of the Total Environment, Vol. 409, Issue 9, pp. 1266-1276, 2011.   DOI
7 H. Zhang, Y. Liu, R. Shi, and Q. Yao, "Evaluation of PM10 Forecasting Based on th Aritficial Neurla Network Model and Intake Fraction in an Urban Area: A Case Study in Taiyuan City, China," Journal of the Air and Waste Management Association, Vol. 63, Issue 7, pp. 755-763, 2013.   DOI
8 S. Thomas and R.B. Jacko, "Model for Forecasting Expressway Fine ParticulateMatter and Carbon Monoxide Concentration: Application of Regression and Neural Network Models," Journal of the Air and Waste Management Association, Vol. 58, Issue 4, pp. 480-488, 2012.
9 F. Franceschi, M. Cobo, and M. Figueredo, "Discovering Relationships and Forecasting PM10 and PM2.5 Concentrations in Bogota, Colombia, Using Artificial Neural Networks, Principal Component Analysis, and K-mean Clustering," Atmospheric Pollution Research, Vol. 9, Issue 5, pp. 912-922, 2018.   DOI
10 S. Park, M. Kim, M. Kim, H. Namgung, K. Kim, K. Cho, et al., "Predicting PM10 Concentration in Seoul Metropolitan Subway Stations Using Artificial Neural Network (ANN)," Journal of Hazardous Materials, Vol. 341, pp. 75-82, 2018.   DOI
11 G.D. Gennaro, L. Trizio, A.D. Gilio, J. Pey, N. Perez, M. Cusack, et al., "Neural Network Model for The Prediction of PM10 Daily Concentrations int Two Sites in The Western Mediterranean," Science of The Total Environment, Vol. 463-464, pp. 875-883, 2013.   DOI
12 F. Biancofiore, M. Busilacchio, M. Verdecchia, B. Tomassetti, E. Aruffo, et al., "Recursive Neural Network Model for Analysis and Forecast of PM10 and PM2.5," Atmospheric Pollution Research, Vol. 8, Issue 4, pp. 652-659, 2017.   DOI
13 Y. Bai, Y. Li, X. Wang, J. Xie, and C. Li, "Air Pollutants Concentrations Forecasting Using Back Propagation Neural Network Based on Wavelet Decomposition with Meteorological Condition," Atmospheric Pollution Research, Vol. 7, Issue 3, pp. 557-566, 2016.   DOI
14 X. Feng, Q. Li, J. Hou, L. Jin, and J. Wang, "Artificial Neural Networks Forecasting of PM2.5 Pollution Using Air Mass Trajectory Based Geographic Model and Wavelet Transformation," Atmospheric Environment, Vol. 107, pp. 118-128, 2015.   DOI
15 B.S. Freeman, G. Taylor, B. Gharabaghi, and J. The, “Forecasting Air Quality Time Series Using Deep Learning,” Journal of the Air and Waste Management Association, Vol. 68, No. 8, pp. 866-886, 2018.   DOI
16 W. Lu, W. Wang, X. Wang, S. Yan, and J.C. Lam, "Potential Assessment of A Neural Network Model with PCA/RBF Approach for Forecasting Pollutant Trends in Mong Kok Urban Air, Hong Kong," Environmental Research, Vol. 96, Issue 1, pp. 79-87, 2004.   DOI
17 J. Fan, Q. Li, J. Hou, X. Feng, H. Karimian, and S. Lin, "A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN," Proceeding of ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W2, 2017 2nd International Symposium on Spatiotemporal Computing, pp. 15-22, 2017.
18 S. Yu, Y. Koo, and H. Kwon, “Inverse Model Parameter Estimation Based on Sensitivity Analysis for Improvement of PM10 Forecasting,” Journal of Korea Multimedia Society, Vol. 18, No. 7, pp. 886-894, 2015.   DOI
19 H. Bae, S. Yu, and H. Kwon, “Fast Data Assimilation Using Kernel Tridiagonal Sparse Matrix for Performance Improvement of Air Quality Forecasting,” Journal of Korea Multimedia Society, Vol. 20, No. 2, pp. 363-370, 2017.   DOI