Articles | Volume 25, issue 5
https://doi.org/10.5194/hess-25-2685-2021
© 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-25-2685-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling
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
Sepp Hochreiter
LIT AI Lab & Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
Grey S. Nearing
CORRESPONDING AUTHOR
Google Research, Mountain View, CA, United States
Land, Air and Water Resources Department, University of California Davis, Davis, CA, USA
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Latest update: 06 Dec 2024
Short summary
We investigate how deep learning models use different meteorological data sets in the task of (regional) rainfall–runoff modeling. We show that performance can be significantly improved when using different data products as input and further show how the model learns to combine those meteorological input differently across time and space. The results are carefully benchmarked against classical approaches, showing the supremacy of the presented approach.
We investigate how deep learning models use different meteorological data sets in the task of...