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Air-pollution prediction in smart city, deep learning approach.
- Source :
- Journal of Big Data; 12/22/2021, Vol. 8 Issue 1, p1-21, 21p
- Publication Year :
- 2021
-
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 μ m ( P M 2.5 ) is a serious health problem. It causes various illnesses such as respiratory tract and cardiovascular diseases. Hence, it is necessary to accurately predict the P M 2.5 concentrations in order to prevent the citizens from the dangerous impact of air pollution beforehand. The variation of P M 2.5 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 P M 2.5 concentration in Beijing, China, based on CNN-LSTM, with a spatial-temporal feature by combining historical data of pollutants, meteorological data, and P M 2.5 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 21961115
- Volume :
- 8
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Journal of Big Data
- Publication Type :
- Academic Journal
- Accession number :
- 154275199
- Full Text :
- https://doi.org/10.1186/s40537-021-00548-1