01 Sep 2022
01 Sep 2022
Status: this preprint is currently under review for the journal NHESS.

Development of a Seismic Loss Prediction Model for Residential Buildings using Machine Learning – Christchurch, New Zealand

Samuel Roeslin1, Quincy Ma1, Pavan Chigullapally1, Joerg Wicker2, and Liam Wotherspoon1 Samuel Roeslin et al.
  • 1Department of Civil and Environmental Engineering, The University of Auckland, Auckland, New Zealand
  • 2School of Computer Science, The University of Auckland, Auckland, New Zealand

Abstract. This paper presents a new framework for the seismic loss prediction of residential buildings in Christchurch, New Zealand. It employs data science techniques, geospatial tools, and machine learning (ML) trained on insurance claims data from the Earthquake Commission (EQC) collected following the 2010–2011 Canterbury Earthquake Sequence (CES). The seismic loss prediction obtained from the ML model is shown to outperform the output from existing risk analysis tools for New Zealand for each of the main earthquakes of the CES. In addition to the prediction capabilities, the ML model delivered useful insights into the most important features contributing to losses during the CES. ML correctly highlighted that liquefaction significantly influenced buildings losses for the 22 February 2011 earthquake. The results are consistent with observations, engineering knowledge, and previous studies, confirming the potential of data science and ML in the analysis of insurance claims data and the development of seismic loss prediction models using empirical loss data.

Samuel Roeslin et al.

Status: open (until 13 Oct 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-227', Zoran Stojadinovic, 22 Sep 2022 reply
  • RC2: 'Comment on nhess-2022-227', Anonymous Referee #2, 27 Sep 2022 reply

Samuel Roeslin et al.

Samuel Roeslin et al.


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Short summary
This paper presents a new framework for the seismic damage and loss prediction for NZ residential buildings based on Canterbury Earthquake Sequence data. Data science techniques and machine learning were applied to develop models that can rank damage drivers, allowing decision-makers to prioritise future interventions. The model framework developed here can be updated with new data easily and rapidly to accurately predict building damage and the economic impact of future earthquake events.