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Landslide susceptibility evolution in an area affected by the 2015 Nepal earthquakes, derived from multitemporal landslide inventories

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Published under licence by IOP Publishing Ltd
, , Citation Yingying Tian et al 2021 IOP Conf. Ser.: Earth Environ. Sci. 861 052015 DOI 10.1088/1755-1315/861/5/052015

1755-1315/861/5/052015

Abstract

The Mw 7.8 and Mw 7.3 Nepal earthquakes in 2015 triggered >47,000 landslides and significantly enhanced landslide activity in the affected region in the following years, likely due to a drop in rock strength of the slopes there. This work analyzed the evolution of landslide susceptibility of an area close to the Zhangmu border crossing on the Araniko Highway in Nepal, which was seriously affected by the mainshock, aftershock, and monsoon rainfalls. Using eight landslide inventories we constructed to register landslides triggered by monsoon rainfalls before 2015, the mainshock, the aftershock, and the monsoon rainfalls in 2015–2019, eight susceptibility assessment models were built using the logistic regression method considering 11 control factors of landslides. The changes in the high-susceptibility zones before, during, and after the earthquakes were examined. The backward prediction abilities of the models were checked by taking landsliding and nonlandsliding data in the following years. The results show that the best susceptibility mapping results are obtained based on landslides triggered by the mainshock and the aftershock, which both have successful prediction rates >87%. The high-susceptibility regions have moved from the slope surfaces to channels since 2015, suggesting that transportation of residual debris induced by the strong ground motion resulted from erosion related to the strong post-seismic rainfall and the rivers. A series of attempts using susceptibility models to backward predict landslides in the following years demonstrate that the combination of the susceptibility results of pre-seismic and mainshock-triggered landslides, which have a higher prediction accuracy, has prediction rates >70% until 2018. This study not only will further the understanding of post-seismic landsliding effects, but also will aid in efforts toward future seismic landslide mitigation and post-seismic reconstruction.

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10.1088/1755-1315/861/5/052015