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Iterative integration of deep learning in hybrid Earth surface system modelling

Chen, Min; Qian, Zhen; Boers, Niklas; Jakeman, Anthony J; Kettner, Albert J; Brandt, Martin; Kwan, Mei-Po; ... Lü, Guonian; + view all (2023) Iterative integration of deep learning in hybrid Earth surface system modelling. Nature Reviews Earth & Environment , 4 (8) pp. 568-581. 10.1038/s43017-023-00452-7. Green open access

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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.

Type: Article
Title: Iterative integration of deep learning in hybrid Earth surface system modelling
Open access status: An open access version is available from UCL Discovery
DOI: 10.1038/s43017-023-00452-7
Publisher version: https://doi.org/10.1038/s43017-023-00452-7
Language: English
Additional information: This version is the author-accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis
URI: https://discovery.ucl.ac.uk/id/eprint/10174315
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