基于矩估计加权和改进的灰色目标模型的城市洪水承载脆弱性评估

Dengming Yan, Liu Su, Simin Liu, Hong Lv, Jin Lin, Zhilei Yu, Lucong Cao
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引用次数: 0

摘要

日益严重的洪涝灾害严重威胁着城市安全。科学的城市洪水承载脆弱性评估模型对提高城市风险管理能力意义重大。灰色目标模型(GTM)在城市洪涝灾害脆弱性评估中具有优势。然而,指标相关性和单一靶心往往被忽视,导致评估结果存在缺陷。通过整合四个基础权重,提出了一种基于矩估计的改进权重法。然后,利用边际距离来量化指标相关性,并引入 TOPSIS 模型来定义相对靶心距离。由此,建立了一种改进的灰色目标评价方法。最后,基于矩估计加权改进的 GTM,提出了城市洪涝灾害脆弱性评价模型。本研究以中国郑州市为例。研究了 2006-2020 年郑州市承洪脆弱性的时空变化特征。结果表明(1)在时间尺度上,郑州市的承灾脆弱性在 15 年间呈上升趋势;(2)在空间尺度上,郑州市管城区的城市内涝脆弱性相对最高。本研究有望为城市防洪和抗灾建设提供科学参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Urban flood-bearing vulnerability evaluation based on the moment estimate weighting and improved gray target model
Increasingly severe flooding seriously threatens urban safety. A scientific urban flood-bearing vulnerability assessment model is significant to improve urban risk management capacity. The gray target model (GTM) has advantages in urban flood-bearing vulnerability assessment. However, indicator correlation and single bull's-eye are commonly neglected, leading to defective evaluation results. By integrating the four base weights, an improved weighting method based on the moment estimate was proposed. Then, the marginal distance was used to quantify the indicator correlation, and the TOPSIS model was introduced to define the relative bull's-eye distance. Thus, an improved gray target evaluation method was established. Finally, an urban flood-bearing vulnerability evaluation model was presented based on the moment estimate weighting-improved GTM. In this study, Zhengzhou City, China, was taken as an example. The spatial and temporal changing characteristics of the flood-bearing vulnerability of Zhengzhou from 2006 to 2020 were investigated. The results show that: (1) On the temporal scale, the disaster-bearing vulnerability of Zhengzhou City showed an upward trend during the 15 years; (2) On the spatial scale, Guancheng District of Zhengzhou City had the relatively highest vulnerability to urban flooding. This study is expected to provide a scientific reference for urban flood prevention and resilient construction.
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