10.60507/FK2/RD9E33
Shams Eddin, Mohamad Hakam
Mohamad Hakam
Shams Eddin
https://orcid.org/0000-0003-3136-4619
University of Bonn
Gall, Juergen
Juergen
Gall
https://orcid.org/0000-0002-9447-3399
University of Bonn
Identifying spatio-temporal drivers of extreme events [data set]
bonndata
2024
Agricultural Sciences
Computer and Information Science
Earth and Environmental Sciences
Physics
Shams Eddin, Mohamad Hakam
Mohamad Hakam
Shams Eddin
University of Bonn
Shams Eddin, Mohamad Hakam
Mohamad Hakam
Shams Eddin
University of Bonn
2023-08-01
2024-10-09
2024-10-21
2410.24075
10.60507/FK2/RD9E33/ZG8GD1
10.60507/FK2/RD9E33/FL003Y
10.60507/FK2/RD9E33/5ZIGDL
10.60507/FK2/RD9E33/ZOSXSZ
10.60507/FK2/RD9E33/HHSH1H
10.60507/FK2/RD9E33/VVIDJR
10.60507/FK2/RD9E33/QYUURM
10.60507/FK2/RD9E33/AHDIQP
10.60507/FK2/RD9E33/LKP4J4
10.60507/FK2/RD9E33/WCYNGQ
10.60507/FK2/RD9E33/IWMA8C
10.60507/FK2/RD9E33/FWRTCM
10.60507/FK2/RD9E33/QQMEE1
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1.0
Creative Commons Attribution 4.0 International License.
This data set allows to systematically evaluate approaches for the task of identifying extreme events in water cycle components by developing deep neural networks that detect anomalies and drivers of extremes in simulated data.
German Research Foundation (DFG) - Collaborative Research Center (CRC)
SFB 1502/1–2022 – project no. 450058266