Reconstructing daily streamflow and floods from large-scale atmospheric variables with feed-forward and recurrent neural networks in high latitude climates-Reference-Cited by-同舟云学术

Reconstructing daily streamflow and floods from large-scale atmospheric variables with feed-forward and recurrent neural networks in high latitude climates

Published:2023-02-06 Issue:3 Volume:68 Page:412-431
ISSN:0262-6667
Container-title:Hydrological Sciences Journal
language:en
Short-container-title:Hydrological Sciences Journal

Author:

Hagen Jenny Sjåstad¹²^ORCID,Hasibi Ramin³,Leblois Etienne⁴,Lawrence Deborah⁵,Sorteberg Asgeir¹²^ORCID

Affiliation:

1. Geophysical Institute, University of Bergen, Bergen, Norway

2. Bjerknes Centre for Climate Research, University of Bergen, Bergen, Norway

3. Department of Informatics, University of Bergen, Bergen, Norway

4. Riverly-Lyon Research Unit, French National Institute for Agriculture, Food, and Environment (INRAE), Villeurbanne, France

5. Hydrology Department, Norwegian Water Resources and Energy Directorate (NVE), Oslo, Norway

Funder

Geophysical Institute, University of Bergen, and the Bjerknes Centre for Climate Research through the Climate Hazards and Extremes

Publisher

Informa UK Limited

Subject

Water Science and Technology

Link

https://www.tandfonline.com/doi/pdf/10.1080/02626667.2023.2165927

Reference36 articles.

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4. Collet, F., 2015. Keras. GitHub. Available from: https://keras.io/#keras-the-python-deep-learning-library.

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