Statistical analysis of the landslides triggered by the 2021 SW Chelgard earthquake (ML = 6) using an automatic linear regression (LINEAR) and artificial neural network (ANN) model based on controlling parameters
A. A. Ghaedi Vanani (),
M. Eslami (),
Y. Ghiasi () and
F. Keyvani
Additional contact information
A. A. Ghaedi Vanani: Tarbiat Modares University
M. Eslami: Shiraz University
Y. Ghiasi: University of Waterloo
F. Keyvani: University of Azad Joumhouri Eslami
Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2024, vol. 120, issue 2, No 4, 1069 pages
Abstract:
Abstract This study uses automatic linear regression (LINEAR) and artificial neural network (ANN) models to statistically analyze the area of landslides triggered by the 2021 SW Chelgard earthquake (ML = 6) based on controlling parameters. We recorded and mapped the number of 632 landslides into four groups (based on the Hungr et al. 2014): rock avalanche-rock fall, debris avalanche-debris flow, rock slump, and slide earth flow-soil slump using remote sensing method, satellite images (before and after the earthquake), and field observation. The spatial distribution of landslides showed that the highest values of the landslide area percentage (LAP %) and of the landslide number density (LND, N/km2) occurred in the northern part of the fault on the hanging wall. The ANN models with R2 = 0.51–0.80 provided more accurate predictions of landslide area (LA, m2) than the LINEAR models, with R2 = 0.40–0.61 using multiple parameters. The LINEAR models revealed that the most influential controlling parameters for landslides were the topographic factors and ANN models showed that seismic parameters are effective on the coseismic landslides (e.g., the distance from the epicenter on the rock slumps; the PGA on debris avalanches- debris flow; the distance from the rupture surface of the fault and Ia on the rock avalanches-rockfall and slide earth flow-soil slump). Therefore, the classification of coseismic landslides can be helpful for predicting the LA more accurately and better understanding the failure mechanism.
Keywords: Statistical analysis; LINEAR and ANN model; Coseismic landslide; SW Chelgard earthquake (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s11069-023-06240-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:120:y:2024:i:2:d:10.1007_s11069-023-06240-2
Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069
DOI: 10.1007/s11069-023-06240-2
Access Statistics for this article
Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk
More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().