Hybrid Model of Convolutional LSTM and CNN to Predict Particulate Matter

Authors

  • Seonggu Lee and Jitae Shin Author

Keywords:

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

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. 

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Published

12.11.2024

How to Cite

Hybrid Model of Convolutional LSTM and CNN to Predict Particulate Matter. (2024). International Journal of Information and Electronics Engineering, 10(4), 5-9. http://ijiee.org/index.php/ijiee/article/view/768