Fragility-Based Flood Risk Modeling to Quantify the Effect of Policy Change on Losses at the Community Level

Omar M. Nofal
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引用次数: 4

Abstract

Flooding is a devastating natural hazard whose consequences include loss of life, and damage to community infrastructure, with even further impacts resulting from interdependencies of physical and non-physical systems. Flood risk prediction is a critical component of a comprehensive risk-informed decision framework and is used in combination with information on community resilience planning strategies, flood impacts, and recovery. In this research, a physics-based flood risk model was developed to determine flood hazard characteristics and their corresponding level of damage at the community level. Fragility functions for the impacted buildings from an extensive past field study were used to capture the effect of policy change in terms of increasing first-floor elevation on flood losses to the building stock in the illustrative example community. The unique point about this study is overcoming the flood-related data scarcity by considering different resources, models, and modern technology using Google Street Map View to collect buildings information. In addition to, the algorithm that was developed to handle the spatial characteristics of these data. Therefore, the provided framework can provide policymakers the ability to explore the financial effect of policy changes and allow them to better mitigate flood risk and increase the community resiliency.
基于脆弱性的洪水风险建模:量化政策变化对社区损失的影响
洪水是一种毁灭性的自然灾害,其后果包括生命损失和对社区基础设施的破坏,物质和非物质系统的相互依赖甚至会产生进一步的影响。洪水风险预测是全面的风险知情决策框架的关键组成部分,并与社区复原力规划战略、洪水影响和恢复等信息结合使用。本研究建立了基于物理的洪水风险模型,以确定洪水灾害特征及其相应的社区损失水平。在过去广泛的实地研究中,受影响建筑的脆弱性函数被用来捕捉政策变化的影响,即增加一楼高度对举例社区中建筑存量的洪水损失的影响。本研究的独特之处在于,利用谷歌街景地图收集建筑信息,利用不同的资源、模型和现代技术,克服了洪水相关数据的稀缺性。此外,还开发了用于处理这些数据的空间特征的算法。因此,所提供的框架可以为政策制定者提供探索政策变化的财务影响的能力,并使他们能够更好地减轻洪水风险并提高社区的复原力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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16 weeks
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