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Landslide geometry reveals its trigger

  • Electronic databases of landslides seldom include the triggering mechanisms, rendering these inventories unusable for landslide hazard modeling. We present a method for classifying the triggering mechanisms of landslides in existing inventories, thus, allowing these inventories to aid in landslide hazard modeling corresponding to the correct event chain. Our method uses various geometric characteristics of landslides as the feature space for the machine-learning classifier random forest, resulting in accurate and robust classifications of landslide triggers. We applied the method to six landslide inventories spread over the Japanese archipelago in several different tests and training configurations to demonstrate the effectiveness of our approach. We achieved mean accuracy ranging from 67% to 92%. We also provide an illustrative example of a real-world usage scenario for our method using an additional inventory with unknown ground truth. Furthermore, our feature importance analysis indicates that landslides having identical triggerElectronic databases of landslides seldom include the triggering mechanisms, rendering these inventories unusable for landslide hazard modeling. We present a method for classifying the triggering mechanisms of landslides in existing inventories, thus, allowing these inventories to aid in landslide hazard modeling corresponding to the correct event chain. Our method uses various geometric characteristics of landslides as the feature space for the machine-learning classifier random forest, resulting in accurate and robust classifications of landslide triggers. We applied the method to six landslide inventories spread over the Japanese archipelago in several different tests and training configurations to demonstrate the effectiveness of our approach. We achieved mean accuracy ranging from 67% to 92%. We also provide an illustrative example of a real-world usage scenario for our method using an additional inventory with unknown ground truth. Furthermore, our feature importance analysis indicates that landslides having identical trigger mechanisms exhibit similar geometric properties.show moreshow less

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Metadaten
Author details:Kamal Rana, Ugur ÖztürkORCiDGND, Nishant MalikORCiDGND
DOI:https://doi.org/10.1029/2020GL090848
ISSN:0094-8276
ISSN:1944-8007
Title of parent work (English):Geophysical research letters : GRL / American Geophysical Union
Publisher:American Geophysical Union
Place of publishing:Washington
Publication type:Article
Language:English
Date of first publication:2021/01/14
Publication year:2021
Release date:2024/06/05
Tag:Japan | landslides; databases; random forest
Volume:48
Issue:4
Article number:e2020GL090848
Number of pages:8
Funding institution:RIT's College of Science DRIG Grant; German Academic Exchange Service (DAAD) within the Co-PREPARE project of the German-Indian Partnerships Support ProgramDeutscher Akademischer Austausch Dienst (DAAD) [57553291]; Federal Ministry of Education and Research of Germany (BMBF) within the project CLIENT II-CaTeNAFederal Ministry of Education & Research (BMBF) [FKZ 03G0878A]
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Umweltwissenschaften und Geographie
DDC classification:5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften
Peer review:Referiert
Publishing method:DOAJ gelistet
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