What Role Does Hydrological Science Play in the Age of Machine Learning?
By Grey Nearing, Frederik Kratzert, Alden Keefe Sampson, Craig Pelissier, Daniel Klotz, Jonathan Frame, C. Prieto, Hoshin V. Gupta. Water Resources Research, 2020
596 citations recorded by the source corpus.
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.
Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/62d4aaaf562df94c4bdb116ee1e5cc2843c88bec
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Citations in the corpus
- Deep Learning for Geophysics: Current and Future Trends (2021, background): “(Nearing et al., 2020).”
- The role of deep learning in urban water management: A critical review. (2022, background): “…(e.g., physical constraints or process-based theories) to develop more accurate, generalizable machine learning models, and it has found many applications in hydrology (Nearing et al., 2021) and water quality (Var-adharajan et al., 2022) and is potentially useful to develop digital twins of UWSs.”