What Role Does Hydrological Science Play in the Age of Machine Learning?

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Authors

Grey Nearing , Frederik Kratzert, Alden Keefe Sampson , Craig S. Pelissier, Daniel Klotz, Jonathan Frame , Cristina Prieto, Hoshin Gupta

Abstract

We suggest that there is a potential danger to the hydrological sciences community in not recognizing how transformative machine learning will be for the future of hydrological modeling. Given the recent success of machine learning applied to modeling problems, it is unclear what the role of hydrological theory might be in the future. We suggest that a central challenge in hydrology right now should be to clearly delineate where and when hydrological theory adds value to prediction systems. Lessons learned from the history of hydrological modeling motivate several clear next steps toward integrating machine learning into hydrological modeling workflows.

DOI

https://doi.org/10.31223/osf.io/3sx6g

Subjects

Earth Sciences, Hydrology, Physical Sciences and Mathematics

Keywords

machine learning, Deep learning, uncertainty, Hydrological Modeling

Dates

Published: 2020-02-21 11:45

Last Updated: 2020-10-22 19:21

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License

GNU Lesser General Public License (LGPL) 2.1

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