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.
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.
the Creative Commons Attribution 4.0 License.
Development of a seismic loss prediction model for residential buildings using machine learning – Ōtautahi / Christchurch, New Zealand
Department of Civil and Environmental Engineering, Waipapa Taumata Rau / University of Auckland, Tāmaki Makaurau / Auckland, New Zealand
Quincy Ma
Department of Civil and Environmental Engineering, Waipapa Taumata Rau / University of Auckland, Tāmaki Makaurau / Auckland, New Zealand
Pavan Chigullapally
Department of Civil and Environmental Engineering, Waipapa Taumata Rau / University of Auckland, Tāmaki Makaurau / Auckland, New Zealand
Joerg Wicker
School of Computer Science, Waipapa Taumata Rau / University of Auckland, Tāmaki Makaurau / Auckland, New Zealand
Liam Wotherspoon
Department of Civil and Environmental Engineering, Waipapa Taumata Rau / University of Auckland, Tāmaki Makaurau / Auckland, New Zealand
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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.
This paper presents a new framework for the rapid seismic loss prediction for residential...
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