Format:
Online-Ressource
ISSN:
2194-9034
Content:
Abstract. Landslides triggered by earthquakes are significant geological hazards that can have devastating consequences, posing risks to human lives, infrastructure, and the environment. These seismic events may cause the instability of slopes and result in the displacement of soil and rock materials, leading to landslides. It is crucial to understand the characteristics and mechanisms of earthquake-triggered landslides in order to effectively manage and mitigate their associated risks. The number of landslides triggered by the 2023 Kahramanmaraş earthquakes (with magnitudes of 7.7 and 7.6) was over three thousand and their destructive effects were also devastating as secondary hazards. This study aims to examine the characteristics of landslides using the frequency ratio (FR) model. A landslide susceptibility map (LSM) was also produced using the output. For this purpose, in this study, we derived landslides triggered by the earthquakes in a part of the earthquake-affected region, between Golbasi town of Adiyaman and Erkenek village of Malatya covering an area with a size of 625 km2. The study utilized a landslide inventory that was manually delineated by visual interpretation based on pre-event and post-event. These associations can serve as a foundation for the application of various data-driven machine learning techniques. The findings of this study will contribute to the development of accurate LSMs, providing crucial insights into the behavior of earthquake-triggered landslides.
In:
volume:XLVIII-M-1-2023
In:
year:2023
In:
pages:477-483
In:
extent:7
In:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Katlenburg-Lindau : Copernicus Publications, [2011?]-, XLVIII-M-1-2023 (2023), 477-483 (gesamt 7), 2194-9034
Language:
English
DOI:
10.5194/isprs-archives-XLVIII-M-1-2023-477-2023
URN:
urn:nbn:de:101:1-2023081704161397309762
URL:
https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-477-2023
URL:
https://nbn-resolving.org/urn:nbn:de:101:1-2023081704161397309762
URL:
https://d-nb.info/1299456456/34
URL:
https://isprs-archives.copernicus.org/articles/XLVIII-M-1-2023/477/2023/isprs-archives-XLVIII-M-1-2023-477-2023.pdf
URL:
https://isprs-archives.copernicus.org/articles/XLVIII-M-1-2023/477/2023/
Bookmarklink