Peer-reviewed paper on linking hydrology and theory-guided machine learning

By Marshall Moutenot on April 24, 2020

Water networks and neural networks
Water networks and neural networks

The peer-reviewed academic paper authored by Grey S. Nearing, Frederik Kratzert, Alden Keefe Sampson, Craig S. Pelissier, Daniel Klotz, Jonathan M. Frame, and Hoshin V. Gupta is published in Water Resources Research, and is available for open access. Co-author Keefe Sampson is Technical Director at the company and Kratzert is a part-time collaborator.

You can find the abstract below.


We suggest that there is a potential danger to the hydrological sciences community in not recognizing how transformative machine learning could 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.