Articles | Volume 22, issue 11
https://doi.org/10.5194/nhess-22-3751-2022
https://doi.org/10.5194/nhess-22-3751-2022
Research article
 | 
22 Nov 2022
Research article |  | 22 Nov 2022

Landsifier v1.0: a Python library to estimate likely triggers of mapped landslides

Kamal Rana, Nishant Malik, and Ugur Ozturk

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Cited articles

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Amato, G., Palombi, L., and Raimondi, V.: Data-driven classification of landslide types at a national scale by using Artificial Neural Networks, Int. J. Appl. Earth Obs. Geoinf., 104, 102549, https://doi.org/10.1016/j.jag.2021.102549, 2021. a
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Short summary
The landslide hazard models assist in mitigating losses due to landslides. However, these models depend on landslide databases, which often have missing triggering information, rendering these databases unusable for landslide hazard models. In this work, we developed a Python library, Landsifier, consisting of three different methods to identify the triggers of landslides. These methods can classify landslide triggers with high accuracy using only a landslide polygon shapefile as an input.
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