An optimized hybrid deep learning model for PM2.5 and O3 concentration prediction-Reference-Cited by-同舟云学术

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An optimized hybrid deep learning model for PM2.5 and O3 concentration prediction

Published:2023-02-07 Issue:4 Volume:16 Page:857-871
ISSN:1873-9318
Container-title:Air Quality, Atmosphere & Health
language:en
Short-container-title:Air Qual Atmos Health

Author:

Hu Juntao,Chen Yiyuan,Wang Wei,Zhang Shicheng,Cui Can,Ding Wenke,Fang Yong^ORCID

Funder

Fundamental Research Funds for Central Universities of the Central South University

Guangxi Innovation-Driven Development Project

Publisher

Springer Science and Business Media LLC

Subject

Health, Toxicology and Mutagenesis,Management, Monitoring, Policy and Law,Atmospheric Science,Pollution

Link

https://link.springer.com/content/pdf/10.1007/s11869-023-01317-0.pdf

Reference29 articles.

1. Alimissis A, Philippopoulos K, Tzanis CG et al (2018) Spatial estimation of urban air pollution with the use of artificial neural network models. Atmospheric Environ 191:205–213. https://doi.org/10.1016/j.atmosenv.2018.07.058

2. Athira V, Geetha P, Vinayakumar R, Soman KP (2018) DeepAirNet: applying recurrent networks for air quality prediction. Procedia Comput Sci 132:1394–1403. https://doi.org/10.1016/j.procs.2018.05.068

3. Bashir Shaban K, Kadri A, Rezk E (2016) Urban air pollution monitoring system with forecasting models. IEEE Sens J 16(8):2598–2606. https://doi.org/10.1109/JSEN.2016.2514378

4. Chu HJ, Lin CY, Liau Cj, Kuo YM (2012) Identifying controlling factors of ground-level ozone levels over southwestern Taiwan using a decision tree. Atmos Environ 60:142152. https://doi.org/10.1016/j.atmosenv.2012.06.032

5. Chakma A, Vizena B, Cao T, Lin J, Zhang J (2017) Image-based air quality analysis using deep convolutional neural network. In: 2017 IEEE International Conference on Image Processing (ICIP), IEEE 3949–3952. https://doi.org/10.1109/ICIP.2017.8297023

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