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

Thanongsak Xayasouk, HwaMin Lee and Giyeol Lee
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Thanongsak Xayasouk: Department of Computer Science, Soonchunhyang University, Asan 31538, Korea
HwaMin Lee: Department of Computer Software & Engineering, Soonchunhyang University, Asan 31538, Korea
Giyeol Lee: Department of Landscape Architecture, Chonnam National University, Gwangju 61186, Korea

Sustainability, 2020, vol. 12, issue 6, 1-17

Abstract: Many countries worldwide have poor air quality due to the emission of particulate matter (i.e., PM 10 and PM 2.5 ), 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.

Keywords: air pollution; deep autoencoder (DAE); deep learning; long short-term memory (LSTM); fine particulate matter; PM 10; PM 2.5 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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