Articles | Volume 25, issue 4
https://doi.org/10.5194/hess-25-2045-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-2045-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network
Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada
Frederik Kratzert
Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
Daniel Klotz
Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
Grey Nearing
Google Research, Mountain View, CA, USA
Department of Land, Air and Water Resources, University of California Davis, Davis, CA, USA
Jimmy Lin
David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada
Sepp Hochreiter
Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
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Latest update: 26 Apr 2024
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.
We present multi-timescale Short-Term Memory (MTS-LSTM), a machine learning approach that...