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 spatialtemporal 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.

Uma avaliação preditiva do risco das casas contra desastres causados por ondas de frio usando aprendizado de máquina

“…Mais caraterísticas exigem mais tempo computacional para a pesquisa de hiperparâmetros. No entanto, o espaço de caraterísticas da pesquisa é multidimensional e supera as limitações de estudos anteriores que consideraram apenas fatores socioeconômicos (Szczyrba et al, 2021).…”

Explainable deep learning powered building risk assessment model for proactive hurricane response

“…To specify the hurricane risks of individual buildings and overcome the limitations of fragility functions, some research has utilized machine learning models to model the relationships among building features and hurricane risk levels (Spekkers et al., 2014; Szczyrba et al., 2021). Compared to the methodology of fragility functions, the studies with machine‐learning models have some advantages.…”

“…Fragility functions may not represent the performance of building structures resisting hurricane hazards because their form and parameters’ value were based on experimental data (such as wind tunnel experiments) instead of real‐world conditions (Vickery et al., 2006). To avoid the limitations of fragility functions, some studies have proposed to utilize statistical machine learning models to assess buildings’ hurricane risks with real‐world damage datasets and consider buildings’ features, environmental features, and meteorology features (Spekkers et al., 2014; Szczyrba et al., 2021). However, these naïve models may not be able to learn the complex and interactive relationships between the high‐dimension input features and hurricane risk levels sufficiently (Kim & Yoon, 2018; Kulkarni et al., 2018; Nateghi et al., 2014; Sejnowski, 2020).…”

Machine Learning Methods for Evaluating Public Crisis: Meta-Analysis

“…It appears to be a quick option for identifying disruption events, getting authentic feeds or detecting periodic incidents in real-time [25]. Learning such patterns was also a major game-changer, as the approach tries to map patterns of interest or similarities in a given dataset [26], while also showing the capacity to learn and produce accurate results [27]. The pattern that is key to understanding human actions are cues demonstrating preference.…”