Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 23, issue 3
Articles | Volume 23, issue 3
https://doi.org/10.5194/nhess-23-1207-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-23-1207-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 23, issue 3
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

Viewed

Total article views: 1,764 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,325 380 59 1,764 44 41
  • HTML: 1,325
  • PDF: 380
  • XML: 59
  • Total: 1,764
  • BibTeX: 44
  • EndNote: 41
Views and downloads (calculated since 01 Sep 2022)
Cumulative views and downloads (calculated since 01 Sep 2022)

Viewed (geographical distribution)

Total article views: 1,764 (including HTML, PDF, and XML) Thereof 1,694 with geography defined and 70 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 29 Jun 2024
Download
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.
Read more
Special issue
Advances in machine learning for natural hazards risk...
Altmetrics
Final-revised paper
Preprint