Document Type

Article

Publication Date

8-20-2023

Abstract

With the emergence of large amounts of historical records on adverse impacts of hazardous events, empirical predictive modeling has been revived as a foundational paradigm for quantifying disaster vulnerability of societal systems. This paradigm models societal vulnerability to hazardous events as a vulnerability curve indicating an expected loss rate of a societal system with respect to a possible spectrum of intensity measure (IM) of an event. Although the empirical predictive models (EPMs) of societal vulnerability are calibrated on historical data, they should not be experimentally tested with data derived from field experiments on any societal system. Alternatively, in this paper, we propose a Monte Carlo simulation-based approach to experimentally test EPMs of societal vulnerability. Our study applied an eigenvalue-based method to generate data on societal experiences of IM and pre-event vulnerability indicators. True models were designed to simulate event loss data. Supervised machine learning (ML) models were then trained on simulated data and were found to provide similar predictive performances as the true models. Our results suggested that the calibrated ML-EPMs could effectively quantify societal vulnerability given a normally experienced IM. To extrapolate a vulnerability curve for large IMs, however, simple models should be preferred.

Comments

This article was originally published in Reliability Engineering & System Safety, volume 240, in 2023. https://doi.org/10.1016/j.ress.2023.109593

Copyright

The authors

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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