Analysis of landslide susceptibility prediction accuracy with an event-based inventory: The 6 February 2023 Turkiye earthquakes

被引:4
|
作者
Karakas, Gizem [1 ,2 ]
Unal, Erdinc Orsan [1 ]
Cetinkaya, Sinem [1 ]
Ozcan, Nazli Tunar [3 ]
Karakas, Veysel Emre [4 ]
Can, Recep [1 ]
Gokceoglu, Candan [3 ]
Kocaman, Sultan [2 ]
机构
[1] Hacettepe Univ, Grad Sch Sci & Engn, TR-06800 Beytepe, Ankara, Turkiye
[2] Hacettepe Univ, Dept Geomatics Engn, TR-06800 Beytepe, Ankara, Turkiye
[3] Hacettepe Univ, Dept Geol Engn, TR-06800 Beytepe, Ankara, Turkiye
[4] Gen Directorate Mineral Res & Explorat, Sogutozu, Ankara, Turkiye
关键词
Co-seismic landslides; Susceptibility mapping; Aerial photogrammetry; Random forest; 6 February 2023 kahramanmaras earthquakes (Turkiye); LOGISTIC-REGRESSION; HEAVY RAINFALL; PROVINCE; MACHINE; MODELS; HAZARD; REGION; MAPS; AREA;
D O I
10.1016/j.soildyn.2024.108491
中图分类号
P5 [地质学];
学科分类号
0709 ; 081803 ;
摘要
Landslide susceptibility assessment is a complex challenge explored by various scientists, but not fully resolved. In this study, we produced the landslide susceptibility map of a large region covering 38,500 km2 area in SouthEast Turkiye severely affected by the 6 February 2023 Kahramanmaras Earthquakes (Mw 7.7 and Mw 7.6) using an inventory produced in previous years. We employed random forest regression with a total of nine geomorphological and environmental features and evaluated the results using the co-seismic inventory with 2611 landslides compiled here. Although high accuracy was obtained from pixel-based assessments of test data split from the learning set, the independent validation set of co-seismic landslides showed that attention needs to be paid unseen features such as rare lithological units. Given the significant damage caused by latent hazards with the Kahramanmaras earthquakes, producing reliable inventories and precise landslide susceptibility maps is crucial for risk reduction and minimizing damages.
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页数:16
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