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Quantifying Resilience and Uncertainty in Coastal Flooding Events: Framework for Assessing Urban Vulnerability
AbstractFlood resilience is a desirable state of natural and built environments in dealing with hazards such as extreme rainfalls, storm surges, and hurricanes. Here, resilience means the capacity to reach an acceptable level of functionality during and after a flood. Observations of the recent history of flood disasters in New Orleans and New York City (NYC) have suggested the necessity of quantifying resilience in these regions utilizing a floodplain’s vulnerability assessment platform. Once a meaningful and well-recognized method is developed, then resources can be allocated to increase resiliency. In this study, redundancy, resourcefulness, robustness, and rapidity metrics based on socioeconomic, manmade, and natural conditions are developed to quantify coastal resilience. Exposure and susceptibility are also defined and evaluated for quantifying vulnerability. The proposed evaluation method, which is based on the linear combination of the developed metrics, uses a multicriteria decision making technique for ranking the factors of the system and their weights. Sensitivity and uncertainty analyses are performed to determine the sensitivity of the developed platform to 54 different factors affecting the system’s vulnerability and the uncertainty associated with estimating the representative values and weights of those factors. The proposed platform allows identifying those factors that have the most significant impact on resiliency. As a result, resource allocation could be directed to strengthen those factors rather than ad hoc allocations without consideration of their overall impact on building a more resilient region. Application of the proposed scheme is investigated using a coastal area in the Bronx Borough of NYC. The proposed algorithm can be applied to other coastal areas in identifying effective factors on their system’s flood resiliency.
Quantifying Resilience and Uncertainty in Coastal Flooding Events: Framework for Assessing Urban Vulnerability
AbstractFlood resilience is a desirable state of natural and built environments in dealing with hazards such as extreme rainfalls, storm surges, and hurricanes. Here, resilience means the capacity to reach an acceptable level of functionality during and after a flood. Observations of the recent history of flood disasters in New Orleans and New York City (NYC) have suggested the necessity of quantifying resilience in these regions utilizing a floodplain’s vulnerability assessment platform. Once a meaningful and well-recognized method is developed, then resources can be allocated to increase resiliency. In this study, redundancy, resourcefulness, robustness, and rapidity metrics based on socioeconomic, manmade, and natural conditions are developed to quantify coastal resilience. Exposure and susceptibility are also defined and evaluated for quantifying vulnerability. The proposed evaluation method, which is based on the linear combination of the developed metrics, uses a multicriteria decision making technique for ranking the factors of the system and their weights. Sensitivity and uncertainty analyses are performed to determine the sensitivity of the developed platform to 54 different factors affecting the system’s vulnerability and the uncertainty associated with estimating the representative values and weights of those factors. The proposed platform allows identifying those factors that have the most significant impact on resiliency. As a result, resource allocation could be directed to strengthen those factors rather than ad hoc allocations without consideration of their overall impact on building a more resilient region. Application of the proposed scheme is investigated using a coastal area in the Bronx Borough of NYC. The proposed algorithm can be applied to other coastal areas in identifying effective factors on their system’s flood resiliency.
Quantifying Resilience and Uncertainty in Coastal Flooding Events: Framework for Assessing Urban Vulnerability
Zahmatkesh, Zahra (Autor:in) / Karamouz, Mohammad
2017
Aufsatz (Zeitschrift)
Englisch
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