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
Bellagamba, X., Lee, R., and Bradley, B. A.: A neural network for automated quality screening of ground motion records from small magnitude earthquakes, Earthq. Spectra, 35, 1637–1661, https://doi.org/10.1193/122118EQS292M, 2019. a
Burkov, A.: Machine Learning Engineering, Vol. 1, True Positive Inc., ISBN 10: 1999579577/ISBN 13: 9781999579579, 2020. a
Cousins, J. and McVerry, G. H.: Overview of strong-motion data from the Darfield earthquake, Bulletin of the New Zealand Society for Earthquake Engineering, 43, 222–227, https://doi.org/10.5459/bnzsee.43.4.222-227, 2010. a
Cubrinovski, M., Green, R. A., Allen, J., Ashford, S., Bowman, E., Bradley, B., Cox, B., Hutchinson, T., Kavazanjian, E., Orense, R., Pender, M., Quigley, M., and Wotherspoon, L.: Geotechnical reconnaissance of the 2010 Darfield (Canterbury) earthquake, Bulletin of the New Zealand Society for Earthquake Engineering, 43, 243–320, https://doi.org/10.5459/bnzsee.43.4.243-320, 2010. 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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