基于灰色关联分析的中国典型省份风暴潮经济损失

Z. Yin, Xuemei Li, Xue Jin, Zhangjian Chen
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引用次数: 0

摘要

近年来,中国沿海省份遭受风暴潮的侵袭,造成了巨大的经济损失。本文的目的是利用GCRA模型,根据各省之间直接经济损失变化率的相似性计算灰色关联度,并对沿海省份进行聚类。本文选取最具代表性的五个沿海省份,采用变化率关联法和灰色聚类法对2009 - 2016年风暴潮造成的直接经济损失进行了分析。通过GCRA模型对5个典型沿海省份风暴潮直接损失进行分析,得出5个典型省份风暴潮直接损失可分为3种类型。对于政府和相关灾害管理部门而言,在风暴潮预防过程中制定政策和采取相关措施时,可能会针对同类型省份采取类似的政策或措施,以提高行政效率。所提出的GCRA模型对于根据序列间变化率的相似性计算灰色关联度具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Storm surge economic losses of China's typical provinces based on grey relational analysis
Chinese coastal provinces are suffering from storm surge, which causes huge economic losses, since several years. The purpose of this paper is to calculate the grey correlation degree according to the similarity in the change rate of direct economic losses between different provinces with GCRA model and cluster the coastal provinces. This paper selects the most typical five coastal provinces, using the rate of change associated and grey clustering method to analyze the direct economic losses caused by the storm surge from 2009 to 2016. Through analyzing the direct loss of storm surge in five typical coastal provinces by GCRA model, we can draw the conclusion that five typical provinces can be divided into three types. For the government and related disaster management departments, when they make the policy and take relevant measures in the process of storm surge prevention, they may take similar policies or measures for the same type of provinces, in order to improve administrative efficiency. The proposed GCRA model is very important for calculating the grey correlation degree according to the similarity in the change rate between sequences.
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