兰州大学机构库
Iterative integration of deep learning in hybrid Earth surface system modelling
2023-08
Online publication date2023-07
Source PublicationNATURE REVIEWS EARTH & ENVIRONMENT   Impact Factor & Quartile Of Published Year  The Latest Impact Factor & Quartile
ISSN2662-138X
Volume4Issue:8Pages:568-581
page numbers14
AbstractMethods to integrate Earth system modelling (ESM) with deep learning offer promise for advancing understanding of Earth processes. This Perspective explores the development and applications of hybrid Earth system modelling, a framework that integrates neural networks into ESM throughout the modelling lifecycle. 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.
PublisherSPRINGERNATURE
DOI10.1038/s43017-023-00452-7
Indexed BySCIE
Language英语
WOS Research AreaEnvironmental Sciences & Ecology ; Geology
WOS SubjectEnvironmental Sciences ; Geosciences, Multidisciplinary
WOS IDWOS:001026496700002
Original Document TypeArticle
Citation statistics
Document Type期刊论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/568506
Collection兰州大学
Corresponding AuthorChen, Min; Lue, Guonian
Affiliation
1.Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing, Jiangsu, Peoples R China;
2.Int Res Ctr Big Data Sustainable Dev Goals, Beijing, Peoples R China;
3.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing, Jiangsu, Peoples R China;
4.State Key Lab Cultivat Base Geog Environm Evolut, Nanjing, Jiangsu, Peoples R China;
5.Tech Univ Munich, Sch Engn & Design, Munich, Germany;
6.Potsdam Inst Climate Impact Res, Potsdam, Germany;
7.Univ Exeter, Global Syst Inst, Exeter, England;
8.Univ Exeter, Dept Math, Exeter, England;
9.Australian Natl Univ, Fenner Sch Environm & Soc, Canberra, Australia;
10.Univ Colorado, Inst Arctic & Alpine Res INSTAAR, Boulder, CO USA;
11.Univ Copenhagen, Dept Geosci & Nat Resource Management, Copenhagen, Denmark;
12.Chinese Univ Hong Kong, Dept Geog & Resource Management, Shatin, Hong Kong, Peoples R China;
13.Chinese Univ Hong Kong, Inst Space & Earth Informat Sci, Shatin, Hong Kong, Peoples R China;
14.Univ Coll London UCL, Bartlett Ctr Adv Spatial Anal CASA, London, England;
15.British Lib, Alan Turing Inst, London, England;
16.Arizona State Univ, Sch Geog Sci & Urban Planning, Tempe, AZ USA;
17.ASTAR, Syst Sci Dept, Inst High Performance Comp IHPC, Singapore, Singapore;
18.Natl Univ Singapore, Dept Geog, Singapore, Singapore;
19.Brigham Young Univ, Dept Civil & Construct Engn, Provo, UT USA;
20.Arizona State Univ, Sch Human Evolut & Social Change, Tempe, AZ USA;
21.CSIRO Environm, Canberra, ACT, Australia;
22.Max Planck Inst Biogeochem, Dept Biogeochem Integrat, Jena, Germany;
23.Hong Kong Univ Sci & Technol, Dept Civil & Environm Engn, Hong Kong, Peoples R China;
24.MIT, Dept Urban Studies & Planning, Cambridge, MA USA;
25.Lanzhou Univ, Ctr Pan 3 Pole Environm, Lanzhou, Peoples R China;
26.Nanjing Univ, Coll Geog & Marine, Nanjing, Jiangsu, Peoples R China;
27.China Univ Geosci Beijing, Sch Informat Engn, Beijing, Peoples R China;
28.Jiangxi Normal Univ, Key Lab Poyang Lake Wetland & Watershed Res, Minist Educ, Nanchang, Jiangxi, Peoples R China
Recommended Citation
GB/T 7714
Chen, Min,Qian, Zhen,Boers, Niklas,et al. Iterative integration of deep learning in hybrid Earth surface system modelling[J]. NATURE REVIEWS EARTH & ENVIRONMENT,2023,4(8):568-581.
APA Chen, Min.,Qian, Zhen.,Boers, Niklas.,Jakeman, Anthony J..,Kettner, Albert J..,...&Lue, Guonian.(2023).Iterative integration of deep learning in hybrid Earth surface system modelling.NATURE REVIEWS EARTH & ENVIRONMENT,4(8),568-581.
MLA Chen, Min,et al."Iterative integration of deep learning in hybrid Earth surface system modelling".NATURE REVIEWS EARTH & ENVIRONMENT 4.8(2023):568-581.
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