Articles | Volume 25, issue 4
https://doi.org/10.5194/hess-25-2045-2021
https://doi.org/10.5194/hess-25-2045-2021
Research article
 | 
19 Apr 2021
Research article |  | 19 Apr 2021

Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network

Martin Gauch, Frederik Kratzert, Daniel Klotz, Grey Nearing, Jimmy Lin, and Sepp Hochreiter

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Cited articles

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
Addor, N., Newman, A., Mizukami, M., and Clark, M. P.: Catchment attributes for large-sample studies [data set], Boulder, CO, UCAR/NCAR, https://doi.org/10.5065/D6G73C3Q (last access: 14 April 2021), 2017. a
Addor, N., Nearing, G., Prieto, C., Newman, A. J., Le Vine, N., and Clark, M. P.: A Ranking of Hydrological Signatures Based on Their Predictability in Space, Water Resour. Res., 54, 8792–8812, https://doi.org/10.1029/2018WR022606, 2018. a, b
Araya, I. A., Valle, C., and Allende, H.: A Multi-Scale Model based on the Long Short-Term Memory for day ahead hourly wind speed forecasting, Pattern Recognition Letters, 136, 333–340, https://doi.org/10.1016/j.patrec.2019.10.011, 2019. a
Bengio, Y., Simard, P., and Frasconi, P.: Learning long-term dependencies with gradient descent is difficult, IEEE Transactions on Neural Networks, 5, 157–166, https://doi.org/10.1109/72.279181, 1994. a
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Short summary
We present multi-timescale Short-Term Memory (MTS-LSTM), a machine learning approach that predicts discharge at multiple timescales within one model. MTS-LSTM is significantly more accurate than the US National Water Model and computationally more efficient than an individual LSTM model per timescale. Further, MTS-LSTM can process different input variables at different timescales, which is important as the lead time of meteorological forecasts often depends on their temporal resolution.