| Title |
Air-pollution prediction in smart city, deep learning approach |
| ID_Doc |
40211 |
| Authors |
Bekkar, A; Hssina, B; Douzi, S; Douzi, K |
| Title |
Air-pollution prediction in smart city, deep learning approach |
| Year |
2021 |
| Published |
Journal Of Big Data, 8, 1 |
| DOI |
10.1186/s40537-021-00548-1 |
| Abstract |
Over the past few decades, due to human activities, industrialization, and urbanization, air pollution has become a life-threatening factor in many countries around the world. Among air pollutants, Particulate Matter with a diameter of less than 2.5 mu m (PM2.5) is a serious health problem. It causes various illnesses such as respiratory tract and cardiovascular diseases. Hence, it is necessary to accurately predict the PM2.5 concentrations in order to prevent the citizens from the dangerous impact of air pollution beforehand. The variation of PM2.5 depends on a variety of factors, such as meteorology and the concentration of other pollutants in urban areas. In this paper, we implemented a deep learning solution to predict the hourly forecast of PM2.5 concentration in Beijing, China, based on CNN-LSTM, with a spatial-temporal feature by combining historical data of pollutants, meteorological data, and PM2.5 concentration in the adjacent stations. We examined the difference in performances among Deep learning algorithms such as LSTM, Bi-LSTM, GRU, Bi-GRU, CNN, and a hybrid CNN-LSTM model. Experimental results indicate that our method "hybrid CNN-LSTM multivariate" enables more accurate predictions than all the listed traditional models and performs better in predictive performance. |
| Author Keywords |
Air-pollution; PM2.5; Deep learning; Forecasting; LSTM; GRU; CNN-LSTM |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000733902200002 |
| WoS Category |
Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-021-00548-1
|