Articles | Volume 22, issue 11
Hydrol. Earth Syst. Sci., 22, 6005–6022, 2018
https://doi.org/10.5194/hess-22-6005-2018
Hydrol. Earth Syst. Sci., 22, 6005–6022, 2018
https://doi.org/10.5194/hess-22-6005-2018

Research article 22 Nov 2018

Research article | 22 Nov 2018

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

Frederik Kratzert et al.

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Cited articles

Abrahart, R. J., Anctil, F., Coulibaly, P., Dawson, C. W., Mount, N. J., See, L. M., Shamseldin, A. Y., Solomatine, D. P., Toth, E., and Wilby, R. L.: Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting, Prog. Phys. Geog., 36, 480–513, 2012. a
Adams, T. E. and Pagaon, T. C. (Eds.): Flood Forecasting: A Global Perspective, Academic Press, Boston, MA, USA, 2016. a
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017a. a, b, c
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: Catchment attributes for large-sample studies, UCAR/NCAR, Boulder, CO, USA, https://doi.org/10.5065/D6G73C3Q, 2017b. a, b
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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.