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

Abstract: This paper is derived from a keynote talk given at the Google's 2020 Flood Forecasting Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall?runoff simulation indicate that there is significantly more information in large?scale hydrological data sets than hydrologists have been able to translate into theory or models. While there is a growing interest in machine learning in the hydrological sciences community, in many ways, our community still holds deeply subjective and nonevidence?based preferences for models based on a certain type of ?process understanding? that has historically not translated into accurate theory, models, or predictions. This commentary is a call to action for the hydrology community to focus on developing a quantitative understanding of where and when hydrological process understanding is valuable in a modeling discipline increasingly dominated by machine learning. We offer some potential perspectives and preliminary examples about how this might be accomplished.

 Fuente: Water Resources Research Volume 57, Issue 3 03e2020WR028091

Editorial: American Geophysical Union

 Fecha de publicación: 01/03/2021

Nº de páginas: 15

Tipo de publicación: Artículo de Revista

 DOI: 10.1029/2020WR028091

ISSN: 0043-1397,1944-7973

Autores/as

NEARING, GREY S.

KRATZERT, FREDERIK

SAMPSON, ALDEN KEEFE

PELISSIER, CRAIG S.

KLOTZ, DANIEL

FRAME, JONATHAN M.

CRISTINA PRIETO SIERRA

GUPTA, HOSHIN V.