Articles | Volume 22, issue 11
Nat. Hazards Earth Syst. Sci., 22, 3751–3764, 2022
https://doi.org/10.5194/nhess-22-3751-2022

Special issue: Advances in machine learning for natural hazards risk...

Nat. Hazards Earth Syst. Sci., 22, 3751–3764, 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 et al.

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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
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
ED: Publish as is (20 Sep 2022) by Vitor Silva
ED: Publish as is (22 Sep 2022) by Philip Ward(Executive Editor)
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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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