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

Related authors

Assessing transportation vulnerability to tsunamis: utilising post-event field data from the 2011 Tōhoku tsunami, Japan, and the 2015 Illapel tsunami, Chile
James H. Williams, Thomas M. Wilson, Nick Horspool, Ryan Paulik, Liam Wotherspoon, Emily M. Lane, and Matthew W. Hughes
Nat. Hazards Earth Syst. Sci., 20, 451–470, https://doi.org/10.5194/nhess-20-451-2020,https://doi.org/10.5194/nhess-20-451-2020, 2020
Short summary

Related subject area

Earthquake Hazards
Testing machine learning models for heuristic building damage assessment applied to the Italian Database of Observed Damage (DaDO)
Subash Ghimire, Philippe Guéguen, Adrien Pothon, and Danijel Schorlemmer
Nat. Hazards Earth Syst. Sci., 23, 3199–3218, https://doi.org/10.5194/nhess-23-3199-2023,https://doi.org/10.5194/nhess-23-3199-2023, 2023
Short summary
The seismic hazard from the Lembang Fault, Indonesia, derived from InSAR and GNSS data
Ekbal Hussain, Endra Gunawan, Nuraini Rahma Hanifa, and Qori'atu Zahro
Nat. Hazards Earth Syst. Sci., 23, 3185–3197, https://doi.org/10.5194/nhess-23-3185-2023,https://doi.org/10.5194/nhess-23-3185-2023, 2023
Short summary
Rapid estimation of seismic intensities by analyzing early aftershock sequences using the robust locally weighted regression program (LOWESS)
Huaiqun Zhao, Wenkai Chen, Can Zhang, and Dengjie Kang
Nat. Hazards Earth Syst. Sci., 23, 3031–3050, https://doi.org/10.5194/nhess-23-3031-2023,https://doi.org/10.5194/nhess-23-3031-2023, 2023
Short summary
Towards improving the spatial testability of aftershock forecast models
Asim M. Khawaja, Behnam Maleki Asayesh, Sebastian Hainzl, and Danijel Schorlemmer
Nat. Hazards Earth Syst. Sci., 23, 2683–2696, https://doi.org/10.5194/nhess-23-2683-2023,https://doi.org/10.5194/nhess-23-2683-2023, 2023
Short summary
Accounting for path and site effects in spatial ground-motion correlation models using Bayesian inference
Lukas Bodenmann, Jack W. Baker, and Božidar Stojadinović
Nat. Hazards Earth Syst. Sci., 23, 2387–2402, https://doi.org/10.5194/nhess-23-2387-2023,https://doi.org/10.5194/nhess-23-2387-2023, 2023
Short summary

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
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.
Altmetrics
Final-revised paper
Preprint