Articles | Volume 22, issue 11
Research article
22 Nov 2018
Research article |  | 22 Nov 2018

Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks

Frederik Kratzert, Daniel Klotz, Claire Brenner, Karsten Schulz, and Mathew Herrnegger


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (24 Sep 2018) by Uwe Ehret
AR by Frederik Kratzert on behalf of the Authors (25 Sep 2018)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (25 Sep 2018) by Uwe Ehret
RR by Anonymous Referee #1 (09 Oct 2018)
RR by Anonymous Referee #2 (12 Nov 2018)
ED: Publish subject to technical corrections (13 Nov 2018) by Uwe Ehret
AR by Frederik Kratzert on behalf of the Authors (14 Nov 2018)  Author's response    Manuscript
Short summary
In this paper, we propose a novel data-driven approach for rainfall–runoff modelling, using the long short-term memory (LSTM) network, a special type of recurrent neural network. We show in three different experiments that this network is able to learn to predict the discharge purely from meteorological input parameters (such as precipitation or temperature) as accurately as (or better than) the well-established Sacramento Soil Moisture Accounting model, coupled with the Snow-17 snow model.