Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 22, issue 11
Articles | Volume 22, issue 11
https://doi.org/10.5194/hess-22-6005-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-22-6005-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
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

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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.
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