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Storm surge economic losses of China's typical provinces based on grey relational analysis

机译:基于灰色关联分析的中国典型省份风暴潮经济损失

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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.
机译:中国沿海省份经历了数年的风暴潮,造成了巨大的经济损失。本文的目的是利用GCRA模型,根据不同省份之间直接经济损失的变化率的相似度,计算出灰色关联度,并对沿海省份进行聚类。本文选择了最典型的五个沿海省份,采用变化率关联法和灰色聚类方法,分析了2009年至2016年风暴潮直接造成的直接经济损失。通过GCRA模型,我们可以得出结论,五个典型的省可以分为三种类型。对于政府及相关的灾害管理部门,在防暴增效过程中制定政策并采取相关措施时,对同一类型的省份可能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度计算灰度相关度非常重要。

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