Next Article in Journal
Tropospheric Dust and Associated Atmospheric Circulations over the Mediterranean Region with Focus on Romania’s Territory
Next Article in Special Issue
NOx Emission Reduction and Recovery during COVID-19 in East China
Previous Article in Journal
Analyzing Trend and Variability of Rainfall in The Tafna Basin (Northwestern Algeria)
Previous Article in Special Issue
Regional Differences of Primary Meteorological Factors Impacting O3 Variability in South Korea
Article

A Hybrid Deep Learning Model to Forecast Particulate Matter Concentration Levels in Seoul, South Korea

by 1,†, 2,† and 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.
Atmosphere 2020, 11(4), 348; https://doi.org/10.3390/atmos11040348
Received: 2 February 2020 / Revised: 29 March 2020 / Accepted: 30 March 2020 / Published: 31 March 2020
(This article belongs to the Special Issue Asian/Pacific Air Pollution and Environment)
Both long- and short-term exposure to high concentrations of airborne particulate matter (PM) severely affect human health. Many countries now regulate PM concentrations. Early-warning systems based on PM concentration levels are urgently required to allow countermeasures to reduce harm and loss. Previous studies sought to establish accurate, efficient predictive models. Many machine-learning methods are used for air pollution forecasting. The long short-term memory and gated recurrent unit methods, typical deep-learning methods, reliably predict PM levels with some limitations. In this paper, the authors proposed novel hybrid models to combine the strength of two types of deep learning methods. Moreover, the authors compare hybrid deep-learning methods (convolutional neural network (CNN)—long short-term memory (LSTM) and CNN—gated recurrent unit (GRU)) with several stand-alone methods (LSTM, GRU) in terms of predicting PM concentrations in 39 stations in Seoul. Hourly air pollution data and meteorological data from January 2015 to December 2018 was used for these training models. The results of the experiment confirmed that the proposed prediction model could predict the PM concentrations for the next 7 days. Hybrid models outperformed single models in five areas selected randomly with the lowest root mean square error (RMSE) and mean absolute error (MAE) values for both PM10 and PM2.5. The error rate for PM10 prediction in Gangnam with RMSE is 1.688, and MAE is 1.161. For hybrid models, the CNN–GRU better-predicted PM10 for all stations selected, while the CNN–LSTM model performed better on predicting PM2.5. View Full-Text
Keywords: air quality; particulate matter; long short-term memory; gated recurrent unit; hybrid models air quality; particulate matter; long short-term memory; gated recurrent unit; hybrid models
Show Figures

Figure 1

MDPI and ACS Style

Yang, G.; Lee, H.; Lee, G. A Hybrid Deep Learning Model to Forecast Particulate Matter Concentration Levels in Seoul, South Korea. Atmosphere 2020, 11, 348. https://doi.org/10.3390/atmos11040348

AMA Style

Yang G, Lee H, Lee G. A Hybrid Deep Learning Model to Forecast Particulate Matter Concentration Levels in Seoul, South Korea. Atmosphere. 2020; 11(4):348. https://doi.org/10.3390/atmos11040348

Chicago/Turabian Style

Yang, Guang, HwaMin Lee, and Giyeol Lee. 2020. "A Hybrid Deep Learning Model to Forecast Particulate Matter Concentration Levels in Seoul, South Korea" Atmosphere 11, no. 4: 348. https://doi.org/10.3390/atmos11040348

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop