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.
Original language | English (US) |
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Article number | e2020WR028091 |
Journal | Water Resources Research |
Volume | 57 |
Issue number | 3 |
DOIs |
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State | Published - Mar 2021 |
Externally published | Yes |
Keywords
- Deep Learning
- Machine Learning
- Modeling
- Uncertainty
ASJC Scopus subject areas
- Water Science and Technology