Machine Learning-based Approach for Ex-post Assessment of Community Risk and Resilience Based on Coupled Human-infrastructure Systems Performance

2024-03-24

arXiv.org

AI Summary

Existing community risk and resilience assessments lack quantitative characterization of human–infrastructure system co-adaptation mechanisms. This study addresses this gap by developing a multidimensional machine learning framework—applied to Hurricane Harvey—that integrates population protective behaviors, infrastructure performance, and recovery dynamics, enabling the first post-event, community-scale quantitative analysis of risk–resilience co-evolution. Using K-means clustering and spatial statistics, we identify four distinct risk–resilience spatial archetypes; reveal that resilience in high-risk areas is predominantly driven by evacuation response, whereas in low-risk areas it hinges on pre-event preparedness; and quantitatively characterize income-based resilience disparities and their systemic drivers. The work advances a novel methodology and empirical paradigm for human–infrastructure coupled-system resilience assessment.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Machine Learning: Multi-instance/Multi-view Learning
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

There is a limitation in the literature of data-driven analyses for the ex-post evaluation of community risk and resilience, particularly using features related to the performance of coupled human-infrastructure systems. To address this gap, in this study we created a machine learning-based method for the ex-post assessment of community risk and resilience and their interplay based on features related to the coupled human-infrastructure systems performance. Utilizing feature groups related to population protective actions, infrastructure/building performance features, and recovery features, we examined the risk and resilience performance of communities in the context of the 2017 Hurricane Harvey in Harris County, Texas. These features related to the coupled human-infrastructure systems performance were processed using the K-means clustering method to classify census block groups into four distinct clusters then, based on feature analysis, these clusters were labeled and designated into four quadrants of risk-resilience archetypes. Finally, we analyzed the disparities in risk-resilience status of spatial areas across different clusters as well as different income groups. The findings unveil the risk-resilience status of spatial areas shaped by their coupled human-infrastructure systems performance and their interactions. The results also inform about features that contribute to high resilience in high-risk areas. For example, the results indicate that in high-risk areas, evacuation rates contributed to a greater resilience, while in low-risk areas, preparedness contributed to greater resilience.

Problem

• Community resilience
• Disaster risk assessment
• Infrastructure interdependency

Innovation

• Machine Learning
• Disaster Resilience Assessment
• Socio-Infrastructure Interaction