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General Information
    • ISSN: 2010-3719 (Online)
    • Abbreviated Title: Int. J. Inf. Electron. Eng.
    • Frequency: Quarterly
    • DOI: 10.18178/IJIEE
    • Editor-in-Chief: Prof. Chandratilak De Silva Liyanage
    • Executive Editor: Jennifer Zeng
    • Abstracting/ Indexing : Google Scholar, Electronic Journals Library, Crossref and ProQuest, Ei (INSPEC, IET), EBSCO.
    • E-mail ijiee@ejournal.net
Editor-in-chief

 
University of Brunei Darussalam, Brunei Darussalam   
" It is a great honor to serve as the editor-in-chief of IJIEE. I'll work together with the editorial team. Hopefully, The value of IJIEE will be well recognized among the readers in the related field."

IJIEE 2019 Vol.9(1): 34-38 ISSN: 2010-3719
DOI: 10.18178/IJIEE.2019.9.1.701

Hybrid Model of Convolutional LSTM and CNN to Predict Particulate Matter

Seonggu Lee and Jitae Shin
Abstract—Particulate matter (PM) can harm human health by causing lung cancer, pneumonia, or cardiovascular disease. There is a growing awareness of dangerous PM among people and governments. In order to prepare for the risk, the prediction performance of PM is important. Therefore, many kinds of research are developing various prediction models. Among the models, LSTM based models show the best result and it uses various auxiliary data, including spatial features to improve performance. However, spatial features can be depreciated because all input data has to be unfolded to 1D vector. In this paper, we apply Convolutional LSTM to our model to take advantage of the spatiotemporal relation of the wind and PM forecasting problem. Also, we add CNN to extract temporal features of the dataset on our model in parallel. Finally, we combine both Convolutional LSTM and CNN to predict more accurate PM concentration. In the experiment, we compared this model with LSTM and CNN-LSTM models in previous studies. At the result, the hybrid model showed the best performance.

Index Terms—Deep learning, convolutional long short-term memory (ConvLSTM), CNN, particulate matter prediction.

Seonggu Lee is with the Department of Human ICT Convergence, Sungkyunkwan University, Suwon, Korea (e-mail: dltjdrn@skku.edu).
Jitae Shin is with the Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea (e-mail: jtshin@skku.edu).

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Cite:Seonggu Lee and Jitae Shin, "Hybrid Model of Convolutional LSTM and CNN to Predict Particulate Matter," International Journal of Information and Electronics Engineering vol. 9, no. 1, pp. 34-38, 2019.

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