Thinking inside the black box: enhancing the social vulnerability index with an artificial neural network

The Social Vulnerability Index (SoVI) has served the hazards community well for more than a decade, but it is based on linear statistical methods. Using Utah as a test case, a state with a population exposed to a variety of hazards, this study sought to build upon the SoVI approach by augmenting it with a nonlinear Artificial Neural Network (ANN). A SoVI was created for the state of Utah at the census block group level using five-year data (2008-2012) from the American Community Survey. The SoVI provided a dataset from which to train a neural network. The ANN was then used to classify a subset of the state to determine if it could provide a comparable classification of vulnerability. The ANN produced a vulnerability classification that was approximately 26% consistent with the SoVI created using the traditional approach. The differences in classifications were assessed using radar plots of block group variable averages to explore how the variables were handled in each classification. The results of this study warrant further investigation of the capabilities of an ANN-enhanced SoVI.