Articles | Volume 22, issue 4
Nat. Hazards Earth Syst. Sci., 22, 1395–1417, 2022
https://doi.org/10.5194/nhess-22-1395-2022
Nat. Hazards Earth Syst. Sci., 22, 1395–1417, 2022
https://doi.org/10.5194/nhess-22-1395-2022
Research article
21 Apr 2022
Research article | 21 Apr 2022

Assessing the importance of conditioning factor selection in landslide susceptibility for the province of Belluno (region of Veneto, northeastern Italy)

Sansar Raj Meena 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-2021-299', Anonymous Referee #1, 01 Dec 2021
    • AC2: 'Reply on RC1', Sansar Raj Meena, 19 Jan 2022
  • RC2: 'Comment on nhess-2021-299', Anonymous Referee #2, 07 Dec 2021
    • AC1: 'Reply on RC2', Sansar Raj Meena, 19 Jan 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (24 Jan 2022) by Daniele Giordan
AR by Sansar Raj Meena on behalf of the Authors (26 Jan 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (09 Feb 2022) by Daniele Giordan
RR by Anonymous Referee #3 (21 Feb 2022)
RR by Anonymous Referee #2 (22 Feb 2022)
ED: Publish subject to minor revisions (review by editor) (03 Mar 2022) by Daniele Giordan
AR by Sansar Raj Meena on behalf of the Authors (11 Mar 2022)  Author's response    Author's tracked changes
ED: Publish as is (21 Mar 2022) by Daniele Giordan
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Short summary
The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors (features) in the overall prediction capabilities of the statistical and machine learning algorithms.
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