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Nature Geoscience volume 14, pages 878–880 (2021)
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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.
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A Correction to this paper has been published: https://doi.org/10.1038/s41561-021-00881-3
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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
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Correspondence to Sean W. Fleming.
The authors declare no competing interests.
Supplementary Table 1 and associated references.
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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
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DOI: https://doi.org/10.1038/s41561-021-00865-3
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