An enhanced hybrid ensemble deep learning approach for forecasting daily PM2.5
Author:
Publisher
Springer Science and Business Media LLC
Subject
Metals and Alloys,General Engineering
Link
https://link.springer.com/content/pdf/10.1007/s11771-022-5051-4.pdf
Reference45 articles.
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3. LIANG Chun-sheng, DUAN Feng-kui, HE Ke-bin, et al. Review on recent progress in observations, source identifications and countermeasures of PM2.5 [J]. Environment International, 2016, 86: 150–170. DOI: https://doi.org/10.1016/j.envint.2015.10.016.
4. GRELL G A, PECKHAM S E, SCHMITZ R, et al. Fully coupled “online” chemistry within the WRF model [J]. Atmospheric Environment, 2005, 39(37): 6957–6975. DOI: https://doi.org/10.1016/j.atmosenv.2005.04.027.
5. DJALALOVA I, DELLE MONACHE L, WILCZAK J. PM2.5 analog forecast and Kalman filter post-processing for the Community Multiscale Air Quality (CMAQ) model [J]. Atmospheric Environment, 2015, 119: 431–442. DOI: https://doi.org/10.1016/j.atmosenv.2015.05.057.
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