Articles | Volume 22, issue 3
https://doi.org/10.5194/nhess-22-947-2022
https://doi.org/10.5194/nhess-22-947-2022
Research article
 | 
22 Mar 2022
Research article |  | 22 Mar 2022

Ground motion prediction maps using seismic-microzonation data and machine learning

Federico Mori, Amerigo Mendicelli, Gaetano Falcone, Gianluca Acunzo, Rose Line Spacagna, Giuseppe Naso, and Massimiliano Moscatelli

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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-2021-282', Anonymous Referee #1, 16 Oct 2021
    • AC1: 'Reply on RC1', Federico Mori, 15 Dec 2021
  • RC2: 'Comment on nhess-2021-282', Anonymous Referee #2, 03 Nov 2021
    • AC2: 'Reply on RC2', Federico Mori, 15 Dec 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (19 Dec 2021) by Hans-Balder Havenith
AR by Federico Mori on behalf of the Authors (11 Jan 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (23 Jan 2022) by Hans-Balder Havenith
RR by Anonymous Referee #1 (24 Jan 2022)
RR by Anonymous Referee #3 (01 Feb 2022)
RR by Anonymous Referee #4 (08 Feb 2022)
ED: Publish subject to technical corrections (09 Feb 2022) by Hans-Balder Havenith
AR by Federico Mori on behalf of the Authors (23 Feb 2022)  Author's response   Manuscript 
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Short summary
This work addresses the problem of the ground motion estimation over large areas as an important tool for seismic-risk reduction policies. In detail, the near-real-time estimation of ground motion is a key issue for emergency system management. Starting from this consideration, the present work proposes the application of a machine learning approach to produce ground motion maps, using nine input proxies. Such proxies consider seismological, geophysical, and morphological parameters.
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