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

Abstract

Predisaster damage predictions and postdisaster damage assessments often inadequately capture the intensity and spatial–temporal complexity of natural hazard-caused damage. Accurate identification of areas with the greatest need in the wake of a disaster requires assessment of both the hazards and community vulnerabilities. This study evaluated the contribution of eight hazard and vulnerability drivers of structural damage due to Hurricane María in Puerto Rico, including wind, flood, landslide, and vulnerability measures via ensemble decision tree algorithms. Results from the algorithms indicate that vulnerability measures, including a structural vulnerability index and a social vulnerability index, were the leading predictors of damage, followed by wind, flood, and landslide measures. Therefore, it is critical to consider community vulnerabilities in damage pattern analyses and targeted, predisaster mitigation efforts.