Articles | Volume 23, issue 12
Hydrol. Earth Syst. Sci., 23, 5089–5110, 2019
https://doi.org/10.5194/hess-23-5089-2019
Hydrol. Earth Syst. Sci., 23, 5089–5110, 2019
https://doi.org/10.5194/hess-23-5089-2019

Research article 17 Dec 2019

Research article | 17 Dec 2019

Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets

Frederik Kratzert et al.

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Interactive discussion

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Status: closed
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (17 Oct 2019) by Nadav Peleg
AR by Anna Wenzel on behalf of the Authors (05 Nov 2019)  Author's response
ED: Publish as is (06 Nov 2019) by Nadav Peleg
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Short summary
A new approach for regional rainfall–runoff modeling using long short-term memory (LSTM)-based models is presented and benchmarked against a range of well-known hydrological models. The approach significantly outperforms regionally calibrated hydrological models but also basin-wise calibrated models. Furthermore, we propose an adaption of the LSTM that allows us to extract the learned catchment understanding of the model and show that it matches our hydrology expert knowledge.