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  Landslide topology uncovers failure movements

Bhuyan, K., Rana, K., Ferrer, J. V., Cotton, F., Ozturk, U., Catani, F., Malik, N. (2024): Landslide topology uncovers failure movements. - Nature Communications, 15, 2633.
https://doi.org/10.1038/s41467-024-46741-7

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 Urheber:
Bhuyan, Kushanav1, Autor
Rana, Kamal1, Autor
Ferrer, Joaquin Vicente2, Autor              
Cotton, Fabrice1, Autor
Ozturk, Ugur1, Autor
Catani, Filippo1, Autor
Malik, Nishant1, Autor
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: The death toll and monetary damages from landslides continue to rise despite advancements in predictive modeling. These models’ performances are limited as landslide databases used in developing them often miss crucial information, e.g., underlying movement types. This study introduces a method of discerning landslide movements, such as slides, flows, and falls, by analyzing landslides’ 3D shapes. By examining landslide topological properties, we discover distinct patterns in their morphology, indicating different movements including complex ones with multiple coupled movements. We achieve 80-94% accuracy by applying topological properties in identifying landslide movements across diverse geographical and climatic regions, including Italy, the US Pacific Northwest, Denmark, Turkey, and Wenchuan in China. Furthermore, we demonstrate a real-world application on undocumented datasets from Wenchuan. Our work introduces a paradigm for studying landslide shapes to understand their underlying movements through the lens of landslide topology, which could aid landslide predictive models and risk evaluations.

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Sprache(n): eng - Englisch
 Datum: 2024-03-082024-03-252024-03-25
 Publikationsstatus: Final veröffentlicht
 Seiten: 13
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1038/s41467-024-46741-7
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Model / method: Machine Learning
OATYPE: Gold Open Access
 Art des Abschluß: -

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Titel: Nature Communications
Genre der Quelle: Zeitschrift, SCI, Scopus, p3, oa
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Seiten: - Band / Heft: 15 Artikelnummer: 2633 Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals354
Publisher: Nature