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An Air Quality Prediction Model Based on a Noise Reduction Self-Coding Deep Network
Joint Authors
Cai, Jianxian
Dai, Xun
Hong, Li
Gao, Zhitao
Qiu, Zhongchao
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-05-15
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Aiming at remedying the problem of low prediction accuracy of existing air pollutant prediction models, a denoising autoencoder deep network (DAEDN) model that is based on long short-term memory (LSTM) networks was designed.
This model created a noise reduction autoencoder with an LSTM network to extract the inherent air quality characteristics of original monitoring data and to implement noise reduction processing on monitoring data to improve the accuracy of air quality predictions.
The LSTM network structure in the DAEDN model was designed as bidirectional LSTM (Bi-LSTM) to solve the problem of a lag in the unidirectional LSTM prediction results and thereby to further improve the prediction accuracy of the prediction model.
Using air pollutant time series data, the DAEDN model was trained using hourly PM2.5 concentration data collected in Beijing over 5 years.
The experimental results show that the DAEDN model can extract more stable features from the noisy input after training was completed.
The models were evaluated using RMSE and MAE, and the results show that the indexes are 15.504 and 6.789; compared with unidirectional LSTM, it is reduced by 7.33% and 5.87%, respectively.
In addition, the new prediction model essentially considered the time series properties of the prediction of the concentration of spatial pollutants and the fully integrated environmental big data, such as air quality monitoring, meteorological monitoring, and forecasting.
American Psychological Association (APA)
Cai, Jianxian& Dai, Xun& Hong, Li& Gao, Zhitao& Qiu, Zhongchao. 2020. An Air Quality Prediction Model Based on a Noise Reduction Self-Coding Deep Network. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1194441
Modern Language Association (MLA)
Cai, Jianxian…[et al.]. An Air Quality Prediction Model Based on a Noise Reduction Self-Coding Deep Network. Mathematical Problems in Engineering No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1194441
American Medical Association (AMA)
Cai, Jianxian& Dai, Xun& Hong, Li& Gao, Zhitao& Qiu, Zhongchao. An Air Quality Prediction Model Based on a Noise Reduction Self-Coding Deep Network. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1194441
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1194441