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.…
Author details: | Kamal Rana, Ugur ÖztürkORCiDGND, Nishant MalikORCiDGND |
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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 |