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基于动态人口分布和模糊综合评价的城市洪涝风险评估

Urban Flood Risk Assessment Based on Dynamic Population Distribution and Fuzzy Comprehensive Evaluation.

机构信息

College of Water Sciences, Beijing Normal University, Beijing 100875, China.

Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China.

出版信息

Int J Environ Res Public Health. 2022 Dec 7;19(24):16406. doi: 10.3390/ijerph192416406.

Abstract

Floods are one of the most common natural disasters that can cause considerable economic damage and loss of life in many regions of the world. Urban flood risk assessment is important for urban flood control, disaster reduction, and risk management. In this study, a novel approach for assessing urban flood risk was proposed based on the dynamic population distribution, improved entropy weight method, fuzzy comprehensive evaluation method, and the principle of maximum membership, and the spatial distribution of flood risk in four different sessions or daily time segments (TS1-TS4) in the northern part of the Shenzhen River Basin (China) was assessed using geographic information system technology. Results indicated that risk levels varied with population movement. The areas of highest risk were largest in TS1 and TS3, accounting for 7.03% and 7.07% of the total area, respectively. The areas of higher risk were largest in TS2 and TS4, accounting for 4.54% and 4.64% of the total area, respectively. The findings of this study could provide a theoretical basis for assessing urban flood risk management measures in Shenzhen (and even throughout China), and a scientific basis for development of disaster prevention and reduction strategies by flood control departments.

摘要

洪水是世界上许多地区最常见的自然灾害之一,可能造成相当大的经济损失和生命损失。城市洪水风险评估对于城市防洪、减灾和风险管理非常重要。本研究提出了一种基于动态人口分布、改进的熵权法、模糊综合评价法和最大隶属度原则的城市洪水风险评估新方法,并利用地理信息系统技术评估了深圳市北部四个不同时段(TS1-TS4)的洪水风险空间分布。结果表明,风险水平随人口流动而变化。高风险区域在 TS1 和 TS3 最大,分别占总面积的 7.03%和 7.07%。较高风险区域在 TS2 和 TS4 最大,分别占总面积的 4.54%和 4.64%。本研究结果可为深圳市(乃至全国)城市洪水风险管理措施的评估提供理论依据,为防洪部门制定防灾减灾策略提供科学依据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3bb/9778856/1655ad9abe6d/ijerph-19-16406-g001.jpg

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