Developing a Multi-Hazard Weighting Scheme for Community Resilience Indicators

Abstract eng:
Community resilience is the ability of a community to resist and recover from adversity, such as natural disasters, terror attack, and influenza pandemic. Quantifying community resilience can help communities better understand their strengths and vulnerabilities, prepare for different types of hazards, estimate losses in case of adverse situations, and take effective measures to reduce losses. However, such a task is extremely challenging, because community resilience is essentially a comprehensive and complex concept with entrenched difficulties in defining appropriate criteria for its quantification. The commonly-used approach usually considers multiple domains of a community and selects some indicators to capture features of each domain. Then, indicators are equally weighted across the domain and aggregated together to come up with an index to quantify community resilience. This study chooses a set of commonly used indicators in the engineered system domain and aims to develop a multi-hazard weighting scheme for these indicators. A multi-hazard weighting scheme is meaningful because the importance of each indicator, as a contributing factor to worsen or lessen damages, could vary significantly across different hazards. In this study, we mainly focus on earthquakes and hurricanes, which are the two typical representatives of natural hazards. We choose different response variables from recent earthquakes and hurricanes. The historical data of engineered system indicators and hazard response variables can be collected from publicly available databases. Based on these data, we apply linear regression method to form statistical models and use these models to determine the variable importance for each indicator for different hazards. By comparing the weighting schemes for earthquakes and hurricanes, we discuss the possible reasons accounting for the differences and summarize the pros and cons of our multi-hazard weighting scheme. Moreover, the direction of indicators obtained by the regression models coincides with the direction obtained from expert judgment, which validates our methodology and choice for response variables. This multi-hazard weighting scheme contributes to quantifying community resilience and assessing urban risks under attacks of earthquakes and hurricanes.

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