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Open AccessArticle

Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models

by Thanongsak Xayasouk 1,†, HwaMin Lee 2,† and Giyeol Lee 3,*
1
Department of Computer Science, Soonchunhyang University, Asan 31538, Korea
2
Department of Computer Software & Engineering, Soonchunhyang University, Asan 31538, Korea
3
Department of Landscape Architecture, Chonnam National University, Gwangju 61186, Korea
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sustainability 2020, 12(6), 2570; https://doi.org/10.3390/su12062570
Received: 2 February 2020 / Revised: 16 March 2020 / Accepted: 19 March 2020 / Published: 24 March 2020
(This article belongs to the Special Issue Air Pollution Monitoring and Environmental Sustainability)
Many countries worldwide have poor air quality due to the emission of particulate matter (i.e., PM10 and PM2.5), which has led to concerns about human health impacts in urban areas. In this study, we developed models to predict fine PM concentrations using long short-term memory (LSTM) and deep autoencoder (DAE) methods, and compared the model results in terms of root mean square error (RMSE). We applied the models to hourly air quality data from 25 stations in Seoul, South Korea, for the period from 1 January 2015, to 31 December 2018. Fine PM concentrations were predicted for the 10 days following this period, at an optimal learning rate of 0.01 for 100 epochs with batch sizes of 32 for LSTM model, and DAEs model performed best with batch size 64. The proposed models effectively predicted fine PM concentrations, with the LSTM model showing slightly better performance. With our forecasting model, it is possible to give reliable fine dust prediction information for the area where the user is located. View Full-Text
Keywords: air pollution; deep autoencoder (DAE); deep learning; long short-term memory (LSTM); fine particulate matter; PM10; PM2.5 air pollution; deep autoencoder (DAE); deep learning; long short-term memory (LSTM); fine particulate matter; PM10; PM2.5
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Xayasouk, T.; Lee, H.; Lee, G. Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models. Sustainability 2020, 12, 2570.

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