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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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-141', Anonymous Referee #1, 09 Jun 2022
    • AC1: 'Reply on RC1', Kamal Rana, 13 Sep 2022
  • RC2: 'Comment on nhess-2022-141', Luigi Lombardo, 12 Aug 2022
    • AC2: 'Reply on RC2', Kamal Rana, 13 Sep 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to minor revisions (review by editor) (15 Sep 2022) by Vitor Silva
AR by Kamal Rana on behalf of the Authors (19 Sep 2022)  Author's response   Author's tracked changes 
EF by Sarah Buchmann (19 Sep 2022)  Manuscript 
ED: Publish as is (20 Sep 2022) by Vitor Silva
ED: Publish as is (22 Sep 2022) by Philip Ward (Executive editor)
AR by Kamal Rana on behalf of the Authors (02 Oct 2022)
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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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