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http://dx.doi.org/10.14400/JDC.2021.19.2.215

Design of a 1-D CRNN Model for Prediction of Fine Dust Risk Level  

Lee, Ki-Hyeok (Department of Electrical and Electronic Engineering, Hanyang University)
Hwang, Woo-Sung (Department of Electronic, Electrical, Control & Instrumentation Engineering, Hanyang University)
Choi, Myung-Ryul (Division of Electronics Engineering, Hanyang University)
Publication Information
Journal of Digital Convergence / v.19, no.2, 2021 , pp. 215-220 More about this Journal
Abstract
In order to reduce the harmful effects on the human body caused by the recent increase in the generation of fine dust in Korea, there is a need for technology to help predict the level of fine dust and take precautions. In this paper, we propose a 1D Convolutional-Recurrent Neural Network (1-D CRNN) model to predict the level of fine dust in Korea. The proposed model is a structure that combines the CNN and the RNN, and uses domestic and foreign fine dust, wind direction, and wind speed data for data prediction. The proposed model achieved an accuracy of about 76%(Partial up to 84%). The proposed model aims to data prediction model for time series data sets that need to consider various data in the future.
Keywords
Fine dust; Deep learning; CNN; RNN; Data prediction;
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  • Reference
1 Jeon, S., & Son, Y. S. (2018). Prediction of fine dust PM 10 using a deep neural network model. The Korean Journal of Applied Statistics, 31(2), 265-28   DOI
2 Kim, H. S., Kim, D. S., Kim, H., & Yi, S. M. (2012). Relationship between mortality and fine particles during Asian dust, smog-Asian dust, and smog days in Korea. International journal of environmental health research, 22(6). 518-530.   DOI
3 Graves, A., & Schmidhuber, J. (2009). Offline handwriting recognition with multidimensional recurrent neural networks. In Advances in neural information processing systems, 545-552.
4 LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.   DOI
5 Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527-1554.   DOI
6 T. Y. Kim. (2019). Python Deep Learning Keras with Blocks. Seoul : Digital books.
7 S. A. Park, & H. J. Shin. (2017). Analysis of the Factors Influencing PM2.5 in Korea : Focusing on Seasonal Factors. Journal of Environmental Policy and Administration, 25(1), 227-248.   DOI
8 Greff, K., Srivastava, R. K., Koutnik, J., Steunebrink, B. R., & Schmidhuber, J. (2016). LSTM: A search space odyssey. IEEE transactions on neural networks and learning systems, 28(10), 2222-2232.   DOI
9 Christoffersen, P., & Jacobs, K. (2004). The importance of the loss function in option valuation. Journal of Financial Economics, 72(2), 291-318.   DOI
10 Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization.ar Xiv preprint arXiv, 1412.6980.
11 Korea Meteological Office. (2019). Yellow dust observation. Korea Meteorological Agency, Weather Data Opening Portal. https://data.kma.go.kr/data/climate/selectDustRltmList.do?pgmNo=68
12 young-0. Beijing Air Quality: pm2.5. monthly comparison. http://www.young-0.com/airquality/
13 D. Y. Wi. (2017.11.2.) Fine dust prediction accuracy, actually only 50%. Electimes, No. 3345, p. 12.
14 H. S. Kim, Kim, D. S., Kim, H., & Yi, S. M. (2012). Relationship between mortality and fine particles during Asian dust, smog-Asian dust, and smog days in Korea. International journal of environmental health research, 22(6). 518-530.   DOI
15 Y. P. Kim. (2006). (Invited paper)Air Pollution in Seoul Caused by Aerosols KOSAE, 22(5), 535-553.
16 Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to linear regression analysis (Vol. 821). Hoboken : John Wiley & Sons.