Articles | Volume 25, issue 5
Research article
20 May 2021
Research article |  | 20 May 2021

A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling

Frederik Kratzert, Daniel Klotz, Sepp Hochreiter, and Grey S. Nearing


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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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) (07 Aug 2020) by Dimitri Solomatine
AR by Frederik Kratzert on behalf of the Authors (07 Sep 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (30 Sep 2020) by Dimitri Solomatine
RR by Anonymous Referee #1 (08 Jan 2021)
RR by Thomas Lees (06 Mar 2021)
ED: Publish subject to minor revisions (review by editor) (10 Mar 2021) by Dimitri Solomatine
AR by Frederik Kratzert on behalf of the Authors (16 Mar 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (05 Apr 2021) by Dimitri Solomatine
Short summary
We investigate how deep learning models use different meteorological data sets in the task of (regional) rainfall–runoff modeling. We show that performance can be significantly improved when using different data products as input and further show how the model learns to combine those meteorological input differently across time and space. The results are carefully benchmarked against classical approaches, showing the supremacy of the presented approach.