BREATH-Net: a novel deep learning framework for NO2 prediction using bi-directional encoder with transformer-Reference-Cited by-同舟云学术

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BREATH-Net: a novel deep learning framework for NO2 prediction using bi-directional encoder with transformer

Published:2024-03-04 Issue:4 Volume:196 Page:
ISSN:0167-6369
Container-title:Environmental Monitoring and Assessment
language:en
Short-container-title:Environ Monit Assess

Author:

Verma Abhishek,Ranga Virender,Vishwakarma Dinesh Kumar

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10661-024-12455-y.pdf

Reference46 articles.

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2. Bucsela, E. J., Krotkov, N. A., Celarier, E. A., Lamsal, L. N., Swartz, W. H., Bhartia, P. K., Boersma, K. F., Veefkind, J. P., Gleason, J. F., & Pickering, K. E. (2013). A new stratospheric and tropospheric NO2 retrieval algorithm for nadir-viewing satellite instruments: Applications to OMI. Atmospheric Measurement Techniques, 6(10), 2607–2626. https://doi.org/10.5194/amt-6-2607-2013

3. Cahuantzi, R., Chen, X., & Güttel, S. (2023). A comparison of LSTM and GRU networks for learning symbolic sequences (arXiv:2107.02248). arXiv. https://doi.org/10.48550/arXiv.2107.02248

4. Chen, Zhao-Yue., Zhang, Tian-Hao., Zhang, Rong, Zhu, Zhong-Min., Yang, Jun, Chen, Ping-Yan., Chun-Quan, Ou., & Guo, Yuming. (2019). Extreme gradient boosting model to estimate PM25 concentrations with missing-filled satellite data in China. Atmospheric Environment, 202, 180–189. https://doi.org/10.1016/j.atmosenv.2019.01.027

5. Cohen, A. J., Brauer, M., Burnett, R., Anderson, H. R., Frostad, J., Estep, K., Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R., Feigin, V., Freedman, G., Hubbell, B., Jobling, A., Kan, H., Knibbs, L., Liu, Y., Martin, R., Morawska, L., … Forouzanfar, M. H. (2017). Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: An analysis of data from the Global Burden of Diseases Study 2015. The Lancet, 389(10082), 1907–1918. https://doi.org/10.1016/S0140-6736(17)30505-6

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