Learning physically interpretable deep networks from reanalysis data for medium-term regional PM2.5 forecasts
- DOI
- 10.1088/1748-9326/ade606
- Published
- 2025
- Container
- Environmental Research Letters
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1088/1748-9326/ade606,
title = {Learning physically interpretable deep networks from reanalysis data for medium-term regional PM2.5 forecasts},
author = {Mingming Zhu and Lin Wu and Marc Bocquet and Junji Cao and Lei Kong and Si Zhang and Wudi Cao and Xiao Tang and Hang Su and Jiang Zhu and Zifa Wang},
year = {2025},
journal = {Environmental Research Letters},
doi = {10.1088/1748-9326/ade606},
url = {https://doi.org/10.1088/1748-9326/ade606}
}RIS
TY - JOUR TI - Learning physically interpretable deep networks from reanalysis data for medium-term regional PM2.5 forecasts AU - Mingming Zhu AU - Lin Wu AU - Marc Bocquet AU - Junji Cao AU - Lei Kong AU - Si Zhang AU - Wudi Cao AU - Xiao Tang AU - Hang Su AU - Jiang Zhu AU - Zifa Wang PY - 2025 JO - Environmental Research Letters DO - 10.1088/1748-9326/ade606 UR - https://doi.org/10.1088/1748-9326/ade606 ER -
APA
Zhu, M., Wu, L., Bocquet, M., Cao, J., Kong, L., Zhang, S., Cao, W., Tang, X., Su, H., Zhu, J., & Wang, Z. (2025). Learning physically interpretable deep networks from reanalysis data for medium-term regional PM2.5 forecasts. Environmental Research Letters. https://doi.org/10.1088/1748-9326/ade606
Source records
- doaj · retrieved 2026-09-25T20:55:16.186Z