Iterative integration of deep learning in hybrid Earth surface system modelling-Reference-Cited by-同舟云学术

Iterative integration of deep learning in hybrid Earth surface system modelling

Published:2023-07-11 Issue:8 Volume:4 Page:568-581
ISSN:2662-138X
Container-title:Nature Reviews Earth & Environment
language:en
Short-container-title:Nat Rev Earth Environ

Author:

Chen Min^ORCID,Qian Zhen^ORCID,Boers Niklas,Jakeman Anthony J.,Kettner Albert J.,Brandt Martin^ORCID,Kwan Mei-Po^ORCID,Batty Michael,Li Wenwen,Zhu Rui,Luo Wei,Ames Daniel P.^ORCID,Barton C. Michael,Cuddy Susan M.,Koirala Sujan^ORCID,Zhang Fan,Ratti Carlo,Liu Jian^ORCID,Zhong Teng,Liu Junzhi,Wen Yongning,Yue Songshan,Zhu Zhiyi^ORCID,Zhang Zhixin^ORCID,Sun Zhuo,Lin Jian,Ma Zaiyang^ORCID,He Yuanqing^ORCID,Xu Kai,Zhang Chunxiao,Lin Hui,Lü Guonian^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Nature and Landscape Conservation,Atmospheric Science,Earth-Surface Processes,Pollution

Link

https://www.nature.com/articles/s43017-023-00452-7.pdf

Reference171 articles.

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3. Zipkin, E. F. et al. Addressing data integration challenges to link ecological processes across scales. Front. Ecol. Environ. 19, 30–38 (2021).

4. Knight, J. & Harrison, S. The impacts of climate change on terrestrial Earth surface systems. Nat. Clim. Change 3, 24–29 (2013).

5. Chen, M. et al. Geographic modeling and simulation systems for geographic research in the new era: some thoughts on their development and construction. Sci. China Earth Sci. 64, 1207–1223 (2021).

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