Quantifying the Role of Vulnerability in Hurricane Damage via a Machine Learning Case Study

Pre-disaster damage predictions and post-disaster damage assessments are challenging because they result from complicated interactions between multiple drivers, including exposure to various hazards as well as differing levels of community resiliency. Certain societal characteristics, in particular, can greatly magnify the impact of a natural hazard, however they are frequently ignored in disaster management because they are difficult to incorporate into quantitative analyses. In order to more accurately identify areas of greatest need in the wake of a disaster, both the hazards and the vulnerabilities need to be carefully assessed since they have been shown to be positively correlated with damage patterns. This study evaluated the contribution of eight drivers of structural damage from Hurricane Mar'ia in Puerto Rico, leveraging machine learning algorithms to determine the role that societal factors played. Random Forest and Stochastic Gradient Boosting Trees algorithms analyzed a diverse set of data including wind, flooding, landslide, and vulnerability measures. These data trained models to predict the structural damage caused by Hurricane Mar'ia in Puerto Rico and the importance of each predictive feature was calculated. Results indicate that vulnerability measures are the leading predictors of damage in this case study, followed by wind, flood, and landslide measures. Each predictive variable exhibits unique, often nonlinear, relationships with damage. These results demonstrate that societal-driven vulnerabilities play critical roles in damage pattern analysis and that targeted, pre-disaster mitigation efforts should be enacted to reinforce household resiliency in socioeconomically vulnerable areas. Recovery programs may need to be reworked to focus on the highly impacted vulnerable populations to avoid the persistence, or potential enhancement, of preexisting social inequalities in the wake of a disaster.