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

机译:基于灰色关系分析的中国典型省势风暴浪涌经济损失

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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:中国沿海省份由风暴潮,这将导致巨大的经济损失,因为几年的痛苦。本文的目的是根据在不同的省份GCRA模型和簇沿海省份之间的直接的经济损失的变化率的相似性来计算灰关联度。本文选取最典型的沿海五个省,利用变化相关的和灰色聚类法的通过率来分析通过对风暴潮的直接造成的损失从2009年的风暴潮至2016年的直接经济损失在五个典型的沿海省份GCRA模型,我们可以得出这样的结论五种典型省份可分为三种类型。对于政府及相关灾害管理部门,当他们的政策,并采取相关措施预防风暴潮的过程中,他们可能会采取类似的政策或措施相同类型省份,以提高行政效率。所提出的模型GCRA是用于根据在序列之间的变化率的相似性计算的灰度关联度非常重要。

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