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Evaluating the Robustness of PCMCI+ for Causal Discovery of Flood Drivers

Miersch, Peter and Günther, Wiebke and Runge, Jakob and Zscheischler, Jakob (2025) Evaluating the Robustness of PCMCI+ for Causal Discovery of Flood Drivers. Artificial Intelligence for the Earth Systems, 4 (4), e240114. American Meteorological Society. doi: 10.1175/AIES-D-24-0114.1. ISSN 2769-7525.

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Official URL: https://journals.ametsoc.org/view/journals/aies/4/4/AIES-D-24-0114.1.xml


Item URL in elib:https://elib.dlr.de/220097/
Document Type:Article
Additional Information:Extreme events; Statistical techniques; Time series; Flood events; Machine learning
Title:Evaluating the Robustness of PCMCI+ for Causal Discovery of Flood Drivers
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Miersch, PeterUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Günther, Wiebkewiebke.guenther (at) dlr.deUNSPECIFIEDUNSPECIFIED
Runge, JakobJakob.Runge (at) dlr.deUNSPECIFIEDUNSPECIFIED
Zscheischler, JakobUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:October 2025
Journal or Publication Title:Artificial Intelligence for the Earth Systems
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:Yes
Volume:4
DOI:10.1175/AIES-D-24-0114.1
Page Range:e240114
Publisher:American Meteorological Society
ISSN:2769-7525
Status:Published
Keywords:Extreme events; Statistical techniques; Time series; Flood events; Machine learning
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:other
DLR - Research area:Raumfahrt
DLR - Program:R - no assignment
DLR - Research theme (Project):R - no assignment
Location: Jena
Institutes and Institutions:Institute of Data Science > Data Analysis and Intelligence
Deposited By: Gerhardus, Andreas
Deposited On:05 Dec 2025 14:24
Last Modified:05 Dec 2025 14:24

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