PM2.5 forecasting for an urban area based on deep learning and decomposition method
air pollutant; air quality; article; deep learning; empirical mode decomposition; human; Malaysia; particulate matter; particulate matter 2.5; predictive model; short term memory; urban area; air pollutant; air pollution; forecasting; Air Pollutants; Air Pollution; Deep Learning; Forecasting; Humans...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Nature Research
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Tenaga Nasional |
| id |
my.uniten.dspace-26646 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-266462023-05-29T17:36:01Z PM2.5 forecasting for an urban area based on deep learning and decomposition method Zaini N. Ean L.W. Ahmed A.N. Abdul Malek M. Chow M.F. 56905328500 55324334700 57214837520 57221404206 57214146115 air pollutant; air quality; article; deep learning; empirical mode decomposition; human; Malaysia; particulate matter; particulate matter 2.5; predictive model; short term memory; urban area; air pollutant; air pollution; forecasting; Air Pollutants; Air Pollution; Deep Learning; Forecasting; Humans; Neural Networks, Computer; Particulate Matter Rapid growth in industrialization and urbanization have resulted in high concentration of air pollutants in the environment and thus causing severe air pollution. Excessive emission of particulate matter to ambient air has negatively impacted the health and well-being of human society. Therefore, accurate forecasting of air pollutant concentration is crucial to mitigate the associated health risk. This study aims to predict the hourly PM2.5 concentration for an urban area in Malaysia using a hybrid deep learning model. Ensemble empirical mode decomposition (EEMD) was employed to decompose the original sequence data of particulate matter into several subseries. Long short-term memory (LSTM) was used to individually forecast the decomposed subseries considering the influence of air pollutant parameters for 1-h ahead forecasting. Then, the outputs of each forecast were aggregated to obtain the final forecasting of PM2.5 concentration. This study utilized two air quality datasets from two monitoring stations to validate the performance of proposed hybrid EEMD-LSTM model based on various data distributions. The spatial and temporal correlation for the proposed dataset were analysed to determine the significant input parameters for the forecasting model. The LSTM architecture consists of two LSTM layers and the data decomposition method is added in the data pre-processing stage to improve the forecasting accuracy. Finally, a comparison analysis was conducted to compare the performance of the proposed model with other deep learning models. The results illustrated that EEMD-LSTM yielded the highest accuracy results among other deep learning models, and the hybrid forecasting model was proved to have superior performance as compared to individual models. � 2022, The Author(s). Final 2023-05-29T09:36:01Z 2023-05-29T09:36:01Z 2022 Article 10.1038/s41598-022-21769-1 2-s2.0-85140233282 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140233282&doi=10.1038%2fs41598-022-21769-1&partnerID=40&md5=d4550a44640e572b8c76528626f0ed40 https://irepository.uniten.edu.my/handle/123456789/26646 12 1 17565 All Open Access, Gold Nature Research Scopus |
| institution |
Universiti Tenaga Nasional |
| building |
UNITEN Library |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Tenaga Nasional |
| content_source |
UNITEN Institutional Repository |
| url_provider |
http://dspace.uniten.edu.my/ |
| description |
air pollutant; air quality; article; deep learning; empirical mode decomposition; human; Malaysia; particulate matter; particulate matter 2.5; predictive model; short term memory; urban area; air pollutant; air pollution; forecasting; Air Pollutants; Air Pollution; Deep Learning; Forecasting; Humans; Neural Networks, Computer; Particulate Matter |
| author2 |
56905328500 |
| author_facet |
56905328500 Zaini N. Ean L.W. Ahmed A.N. Abdul Malek M. Chow M.F. |
| format |
Article |
| author |
Zaini N. Ean L.W. Ahmed A.N. Abdul Malek M. Chow M.F. |
| spellingShingle |
Zaini N. Ean L.W. Ahmed A.N. Abdul Malek M. Chow M.F. PM2.5 forecasting for an urban area based on deep learning and decomposition method |
| author_sort |
Zaini N. |
| title |
PM2.5 forecasting for an urban area based on deep learning and decomposition method |
| title_short |
PM2.5 forecasting for an urban area based on deep learning and decomposition method |
| title_full |
PM2.5 forecasting for an urban area based on deep learning and decomposition method |
| title_fullStr |
PM2.5 forecasting for an urban area based on deep learning and decomposition method |
| title_full_unstemmed |
PM2.5 forecasting for an urban area based on deep learning and decomposition method |
| title_sort |
pm2.5 forecasting for an urban area based on deep learning and decomposition method |
| publisher |
Nature Research |
| publishDate |
2023 |
| _version_ |
1806425637730123776 |