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Journal Article

Iterative integration of deep learning in hybrid Earth surface system modelling

Authors

Chen,  Min
External Organizations;

Qian,  Zhen
External Organizations;

/persons/resource/Niklas.Boers

Boers,  Niklas
Potsdam Institute for Climate Impact Research;

Jakeman,  Anthony J.
External Organizations;

Kettner,  Albert J.
External Organizations;

Brandt,  Martin
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Kwan,  Mei-Po
External Organizations;

Batty,  Michael
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Li,  Wenwen
External Organizations;

Zhu,  Rui
External Organizations;

Luo,  Wei
External Organizations;

Ames,  Daniel P.
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Barton,  C. Michael
External Organizations;

Cuddy,  Susan M.
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Koirala,  Sujan
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Zhang,  Fan
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Ratti,  Carlo
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Liu,  Jian
External Organizations;

Zhong,  Teng
External Organizations;

Liu,  Junzhi
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Wen,  Yongning
External Organizations;

Yue,  Songshan
External Organizations;

Zhu,  Zhiyi
External Organizations;

Zhang,  Zhixin
External Organizations;

Sun,  Zhuo
External Organizations;

Lin,  Jian
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Ma,  Zaiyang
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He,  Yuanqing
External Organizations;

Xu,  Kai
External Organizations;

Zhang,  Chunxiao
External Organizations;

Lin,  Hui
External Organizations;

Lü,  Guonian
External Organizations;

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Citation

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


Cite as: https://publications.pik-potsdam.de/pubman/item/item_28599
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.