Fine Dust Predicting using Recurrent Neural Network with GRU
Thanongsak Xayasouk1, Guang Yang2, Hwa Min Lee3
1Thanongsak Xayasouk, Department of Computer Science, Soonchunhyang University, Asan, Chungcheongnam, Korea, East Asian.
2Guang Yang, Department of Computer Science, Soonchunhyang University, Asan, Chungcheongnam, Korea, East Asian.
3Hwa Min Lee, Department of Computer Software and Engineering, Soonchunhyang University, Asan, Chungcheongnam, Korea, East Asian.
Manuscript received on 10 June 2019 | Revised Manuscript received on 17 June 2019 | Manuscript Published on 22 June 2019 | PP: 820-823 | Volume-8 Issue-8S2 June 2019 | Retrieval Number: H11370688S219/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The particulate matter especially PM2.5 can cause respiratory, cardiovascular and nervous system damage as many studies prove. The monitoring and forecasting system are highly required. This paper proposed a predicting model to forecast PM10 and PM2.5 concentrations in Seoul, South Korea. The proposed model combines the recurrent neural network with GRU. The proposed model can extract the hidden patterns in the long sequence data as RNN’s feature. The proposed model proved they could make satisfying particulate matter concentration in the urban area. The prediction results are reliable even for future 20 days. Meteorological data also contribute to higher predicting results as auxiliary data for the neural network. In further work, we will try to evaluate the model’s universality with more urban cities. Additionally, try to combine other deep learning methods to improve accuracy and reduce time-consuming for prediction.
Keywords: Air pollution, Deep Learning, GRU, RNN.
Scope of the Article: Deep Learning