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Data Integration for the Development of a Seismic Loss Prediction Model for Residential Buildings in New Zealand

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1168)

Abstract

In 2010–2011, New Zealand experienced the most damaging earthquakes in its history. It led to extensive damage to Christchurch buildings, infrastructure and its surroundings; affecting commercial and residential buildings. The direct economic losses represented 20% of New Zealand’s GDP in 2011. Owing to New Zealand’s particular insurance structure, the insurance sector contributed to over 80% of losses for a total of more than NZ$31 billion. Amongst this, over NZ$11 billion of the losses arose from residential building claims and were covered either partially or entirely from the NZ government backed Earthquake Commission (EQC) cover insurance scheme. In the process of resolving the claims, EQC collected detailed financial loss data, post-event observations and building characteristics for each of the approximately 434,000 claims lodged following the Canterbury Earthquake sequence (CES). Added to this, the active NZ earthquake engineering community treated the event as a large scale outdoor experiment and collected extensive data on the ground shaking levels, soil conditions, and liquefaction occurrence throughout wider Christchurch. This paper discusses the necessary data preparation process preceding the development of a machine learning seismic loss model. The process draws heavily upon using Geographic Information System (GIS) techniques to aggregate relevant information from multiple databases interpolating data between categories and converting data between continuous and categorical forms. Subsequently, the database is processed, and a residential seismic loss prediction model is developed using machine learning. The aim is to develop a ‘grey-box’ model enabling human interpretability of the decision steps.

Keywords

Seismic loss Christchurch earthquake sequence Data aggregation using GIS 

Notes

Acknowledgments

We acknowledge EQC for generously providing the claim data to realize this study. Many thanks to Geoffrey Spurr for his interpretation, assistance and review of the paper. We gratefully acknowledge the New Zealand Society for Earthquake Engineering (NZSEE) for the financial support. Thanks also goes to Dr. Sjoerd Van Ballegooy for his insightful advices.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.The University of AucklandAucklandNew Zealand

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