Machine learning in Earth and environmental science requires education and research policy reforms – Nature.com

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.
Advertisement
Nature Geoscience volume 14pages 878–880 (2021)
5196 Accesses
14 Citations
34 Altmetric
Metrics details
A Publisher Correction to this article was published on 24 December 2021
This article has been updated
Leveraging advances in artificial intelligence could revolutionize the Earth and environmental sciences. We must ensure that our research funding and training choices give the next generation of geoscientists the capacity to realize this potential.
In Earth and environmental science (EES), quantitative prediction models gauge the state of scientific knowledge and help put it to practical use. With the emergence of big data, exponential growth in computational speed and increasing awareness of the practical limits of classical physics-based and statistical models, a new modelling approach has appeared: artificial intelligence (AI) (for a short glossary of machine learning-related jargon, see Table 1). A major component of the broader data-science tidal wave, which has been deemed the fourth industrial revolution1 and fourth paradigm of science2, AI can accelerate discovery and prediction thanks to its scalability, capacity to determine patterns within large datasets and wide applicability.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Scientific Reports Open Access 21 November 2023

Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
24,99 € / 30 days
cancel any time

Subscribe to this journal
Receive 12 print issues and online access
251,40 € per year
only 20,95 € per issue

Rent or buy this article
Prices vary by article type
from$1.95
to$39.95

Prices may be subject to local taxes which are calculated during checkout
A Correction to this paper has been published: https://doi.org/10.1038/s41561-021-00881-3
Schwab, K. The Fourth Industrial Revolution (Penguin Random House, 2017).
Hey, T., Tansley, S. & Tolle, K. The Fourth Paradigm: Data-Intensive Scientific Discovery (Microsoft Research, 2009).
Fleming, S. W. Where the River Flows: Scientific Reflections on Earth’s Waterways (Princeton Univ. Press, 2017).
Fleming, S. W. & Gupta, H. V. Phys. Today 73, 46–52 (2020).
Article  Google Scholar 
McGovern, A. et al. Bull. Am. Meteorol. Soc. 100, 2175–2199 (2019).
Article  Google Scholar 
Hutchinson, M. et al. Solving industrial materials problems by using machine learning across diverse computational and experimental data. In American Physical Society March Meeting 2018 BAPS.2018.MAR.K32.2 (American Physical Society, 2018); http://meetings.aps.org/link/BAPS.2018.MAR.K32.2
Karpatne, A. et al. IEEE Trans. Knowl. Data Eng. 29, 2318–2331 (2017).
Article  Google Scholar 
Ellenson, A., Pei, Y., Wilson, G., Özkan-Haller, H. T. & Fern, X. Coast. Eng. 157, 103595 (2020).
Article  Google Scholar 
Download references
National Water and Climate Center, Natural Resources Conservation Service, US Department of Agriculture, Portland, OR, USA
Sean W. Fleming
College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR, USA
Sean W. Fleming, James R. Watson & Ashley Ellenson
Water Resources Graduate Program, Oregon State University, Corvallis, OR, USA
Sean W. Fleming
Department of Earth, Ocean, and Atmospheric Sciences, University of British Columbia, Vancouver, British Columbia, Canada
Sean W. Fleming & Alex J. Cannon
Department of Civil and Construction Engineering, Oregon State University, Corvallis, OR, USA
Ashley Ellenson
Climate Research Division, Environment and Climate Change Canada, Vancouver, British Columbia, Canada
Alex J. Cannon
Computational Earth Sciences Group, Environmental and Earth Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA
Velimir C. Vesselinov
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
Correspondence to Sean W. Fleming.
The authors declare no competing interests.
Supplementary Table 1 and associated references.
Reprints and Permissions
Fleming, S.W., Watson, J.R., Ellenson, A. et al. Machine learning in Earth and environmental science requires education and research policy reforms. Nat. Geosci. 14, 878–880 (2021). https://doi.org/10.1038/s41561-021-00865-3
Download citation
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41561-021-00865-3
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative
Scientific Reports (2023)
Nature Reviews Earth & Environment (2023)

Advertisement
Nature Geoscience (Nat. Geosci.) ISSN 1752-0908 (online) ISSN 1752-0894 (print)
© 2023 Springer Nature Limited
Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

source

Leave a Comment

WP2Social Auto Publish Powered By : XYZScripts.com