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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Cited articles

Atkinson, J., Salmond, C., and Crampton, P.: NZDep2018 Index of Deprivation, Final Research Report, Final Research Report, University of Otago, Wellington, New Zealand, 1–65, https://www.otago.ac.nz/wellington/otago823833.pdf (last access: 4 March 2023), 2020. a
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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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