Empirical Predictive Modeling Approach to Quantifying Social Vulnerability to Natural Hazards

Abstract

Conventionally, natural hazard scholars quantify social vulnerability based on social indicators to manifest the extent to which locational communities are susceptible to adverse impacts of natural hazard events and are prone to limited or delayed recoveries. They usually overlook the different geographical distributions of social vulnerability at different hazard intensities and in distinct response and recovery phases, however. In addition, conventional approaches to quantifying social vulnerability usually establish the relationship between social indicators and social vulnerability with little evidence from empirical data science. In this article, we introduce a general framework of a predictive modeling approach to quantifying social vulnerability given intensity during a response or recovery phase. We establish the relationship between social indicators and social vulnerability with an empirical statistical method and historical data on hazard effects. The new metric of social vulnerability given an intensity measure can be coupled with hazard maps for risk analysis to predict adverse impacts or poor recoveries associated with future natural hazard events. An example based on data on casualties, house damages, and peak ground accelerations of the 2015 Gorkha earthquake in Nepal and pre-event social indicators at the district level shows that the proposed approach can be applied for vulnerability quantification and risk analysis in terms of specific hazard impacts.