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Machine learning in Earth and environmental science requires education and research policy reforms

Nature Geoscience volume 14, pages 878–880 (2021)Cite this article

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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.

Table 1 Data science 101
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Author information

Affiliations

  1. National Water and Climate Center, Natural Resources Conservation Service, US Department of Agriculture, Portland, OR, USA

    Sean W. Fleming

  2. College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR, USA

    Sean W. Fleming, James R. Watson & Ashley Ellenson

  3. Water Resources Graduate Program, Oregon State University, Corvallis, OR, USA

    Sean W. Fleming

  4. Department of Earth, Ocean, and Atmospheric Sciences, University of British Columbia, Vancouver, British Columbia, Canada

    Sean W. Fleming & Alex J. Cannon

  5. Department of Civil and Construction Engineering, Oregon State University, Corvallis, OR, USA

    Ashley Ellenson

  6. Climate Research Division, Environment and Climate Change Canada, Vancouver, British Columbia, Canada

    Alex J. Cannon

  7. Computational Earth Sciences Group, Environmental and Earth Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA

    Velimir C. Vesselinov

Authors
  1. Sean W. Fleming
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  2. James R. Watson
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  3. Ashley Ellenson
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  4. Alex J. Cannon
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  5. Velimir C. Vesselinov
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Corresponding author

Correspondence to Sean W. Fleming.

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The authors declare no competing interests.

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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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