Semantic Scholar Open Access 2020 525 sitasi

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

G. Nearing Frederik Kratzert A. Sampson C. Pelissier D. Klotz +3 lainnya

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Abstrak

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.

Topik & Kata Kunci

Computer Science

Penulis (8)

G. Nearing

Frederik Kratzert

A. Sampson

C. Pelissier

D. Klotz

J. Frame

C. Prieto

H. Gupta

Format Sitasi

APA MLA BibTeX

Nearing, G., Kratzert, F., Sampson, A., Pelissier, C., Klotz, D., Frame, J. et al. (2020). What Role Does Hydrological Science Play in the Age of Machine Learning?. https://doi.org/10.1029/2020WR028091

Akses Cepat

Lihat di Sumber doi.org/10.1029/2020WR028091

Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Total Sitasi: 525×
Sumber Database: Semantic Scholar
DOI: 10.1029/2020WR028091
Akses: Open Access ✓