Articles | Volume 23, issue 3
https://doi.org/10.5194/nhess-23-1207-2023
https://doi.org/10.5194/nhess-23-1207-2023
Research article
 | 
22 Mar 2023
Research article |  | 22 Mar 2023

Development of a seismic loss prediction model for residential buildings using machine learning – Ōtautahi / Christchurch, New Zealand

Samuel Roeslin, Quincy Ma, Pavan Chigullapally, Joerg Wicker, and Liam Wotherspoon

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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-2022-227', Zoran Stojadinovic, 22 Sep 2022
    • AC1: 'Reply on RC1', Samuel Roeslin, 22 Nov 2022
  • RC2: 'Comment on nhess-2022-227', Anonymous Referee #2, 27 Sep 2022
    • AC2: 'Reply on RC2', Samuel Roeslin, 22 Nov 2022
    • AC3: 'Reply on RC2', Samuel Roeslin, 22 Nov 2022

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) (24 Nov 2022) by Vitor Silva
AR by Samuel Roeslin on behalf of the Authors (05 Jan 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (06 Jan 2023) by Vitor Silva
RR by Anonymous Referee #2 (07 Jan 2023)
RR by Zoran Stojadinovic (20 Jan 2023)
ED: Publish subject to minor revisions (review by editor) (23 Jan 2023) by Vitor Silva
AR by Samuel Roeslin on behalf of the Authors (01 Feb 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (03 Feb 2023) by Vitor Silva
ED: Publish as is (07 Feb 2023) by Philip Ward (Executive editor)
AR by Samuel Roeslin on behalf of the Authors (10 Feb 2023)
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Short summary
This paper presents a new framework for the rapid seismic loss prediction for residential buildings in Christchurch, New Zealand. The initial model was trained on insurance claims from the Canterbury earthquake sequence. Data science techniques, geospatial tools, and machine learning were used to develop the prediction model, which also delivered useful insights. The model can rapidly be updated with data from new earthquakes. It can then be applied to predict building loss in Christchurch.
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