Articles | Volume 23, issue 12
https://doi.org/10.5194/hess-23-5089-2019
© Author(s) 2019. 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-23-5089-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets
LIT AI Lab & Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
Daniel Klotz
LIT AI Lab & Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
Guy Shalev
Google Research, Tel Aviv, Israel
Günter Klambauer
LIT AI Lab & Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
Sepp Hochreiter
LIT AI Lab & Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
Grey Nearing
Department of Geological Sciences, University of Alabama, Tuscaloosa, AL, USA
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Latest update: 26 Sep 2023
Short summary
A new approach for regional rainfall–runoff modeling using long short-term memory (LSTM)-based models is presented and benchmarked against a range of well-known hydrological models. The approach significantly outperforms regionally calibrated hydrological models but also basin-wise calibrated models. Furthermore, we propose an adaption of the LSTM that allows us to extract the learned catchment understanding of the model and show that it matches our hydrology expert knowledge.
A new approach for regional rainfall–runoff modeling using long short-term memory (LSTM)-based...