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  Iterative integration of deep learning in hybrid Earth surface system modelling

Chen, M., Qian, Z., Boers, N., Jakeman, A. J., Kettner, A. J., Brandt, M., Kwan, M.-P., Batty, M., Li, W., Zhu, R., Luo, W., Ames, D. P., Barton, C. M., Cuddy, S. M., Koirala, S., Zhang, F., Ratti, C., Liu, J., Zhong, T., Liu, J., Wen, Y., Yue, S., Zhu, Z., Zhang, Z., Sun, Z., Lin, J., Ma, Z., He, Y., Xu, K., Zhang, C., Lin, H., Lü, G. (2023): Iterative integration of deep learning in hybrid Earth surface system modelling. - Nature Reviews Earth & Environment, 4, 568-581.
https://doi.org/10.1038/s43017-023-00452-7

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 Creators:
Chen, Min1, Author
Qian, Zhen1, Author
Boers, Niklas2, Author              
Jakeman, Anthony J.1, Author
Kettner, Albert J.1, Author
Brandt, Martin1, Author
Kwan, Mei-Po1, Author
Batty, Michael1, Author
Li, Wenwen1, Author
Zhu, Rui1, Author
Luo, Wei1, Author
Ames, Daniel P.1, Author
Barton, C. Michael1, Author
Cuddy, Susan M.1, Author
Koirala, Sujan1, Author
Zhang, Fan1, Author
Ratti, Carlo1, Author
Liu, Jian1, Author
Zhong, Teng1, Author
Liu, Junzhi1, Author
Wen, Yongning1, AuthorYue, Songshan1, AuthorZhu, Zhiyi1, AuthorZhang, Zhixin1, AuthorSun, Zhuo1, AuthorLin, Jian1, AuthorMa, Zaiyang1, AuthorHe, Yuanqing1, AuthorXu, Kai1, AuthorZhang, Chunxiao1, AuthorLin, Hui1, AuthorLü, Guonian1, Author more..
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Earth system modelling (ESM) is essential for understanding past, present and future Earth processes. Deep learning (DL), with the data-driven strength of neural networks, has promise for improving ESM by exploiting information from Big Data. Yet existing hybrid ESMs largely have deep neural networks incorporated only during the initial stage of model development. In this Perspective, we examine progress in hybrid ESM, focusing on the Earth surface system, and propose a framework that integrates neural networks into ESM throughout the modelling lifecycle. In this framework, DL computing systems and ESM-related knowledge repositories are set up in a homogeneous computational environment. DL can infer unknown or missing information, feeding it back into the knowledge repositories, while the ESM-related knowledge can constrain inference results of the DL. By fostering collaboration between ESM-related knowledge and DL systems, adaptive guidance plans can be generated through question-answering mechanisms and recommendation functions. As users interact iteratively, the hybrid system deepens its understanding of their preferences, resulting in increasingly customized, scalable and accurate guidance plans for modelling Earth processes. The advancement of this framework necessitates interdisciplinary collaboration, focusing on explainable DL and maintaining observational data to ensure the reliability of simulations.

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Language(s): eng - English
 Dates: 2023-07-112023-08
 Publication Status: Finally published
 Pages: 14
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s43017-023-00452-7
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: FutureLab - Artificial Intelligence in the Anthropocene
Research topic keyword: Land use
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Sustainable Development
 Degree: -

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Title: Nature Reviews Earth & Environment
Source Genre: Journal, SCI, Scopus
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Pages: - Volume / Issue: 4 Sequence Number: - Start / End Page: 568 - 581 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/nature-reviews-earth-environment
Publisher: Nature