Articles | Volume 22, issue 4
Nat. Hazards Earth Syst. Sci., 22, 1151–1157, 2022
https://doi.org/10.5194/nhess-22-1151-2022
Nat. Hazards Earth Syst. Sci., 22, 1151–1157, 2022
https://doi.org/10.5194/nhess-22-1151-2022
Brief communication
04 Apr 2022
Brief communication | 04 Apr 2022

Brief communication: Introducing rainfall thresholds for landslide triggering based on artificial neural networks

Pierpaolo Distefano 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-2021-206', Anonymous Referee #1, 16 Jul 2021
    • AC1: 'Reply on RC1', David J. Peres, 05 Oct 2021
  • RC2: 'Comment on nhess-2021-206', Anonymous Referee #2, 12 Aug 2021
    • AC2: 'Reply on RC2', David J. Peres, 05 Oct 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (14 Oct 2021) by Paolo Tarolli
AR by David J. Peres on behalf of the Authors (23 Nov 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (24 Nov 2021) by Paolo Tarolli
RR by Samuele Segoni (26 Dec 2021)
RR by Anonymous Referee #3 (17 Feb 2022)
ED: Publish subject to minor revisions (review by editor) (19 Feb 2022) by Paolo Tarolli
AR by David J. Peres on behalf of the Authors (01 Mar 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (04 Mar 2022) by Paolo Tarolli
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Short summary
In the communication, we introduce the use of artificial neural networks (ANNs) for improving the performance of rainfall thresholds for landslide early warning. Results show how ANNs using rainfall event duration and mean intensity perform significantly better than a classical power law based on the same variables. Adding peak rainfall intensity as input to the ANN improves performance even more. This further demonstrates the potentialities of the proposed machine learning approach.
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