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基于多样性的自然灾害风险评估与决策加权方法

On the Diversity-Based Weighting Method for Risk Assessment and Decision-Making about Natural Hazards.

作者信息

Chen Pengyu

机构信息

School of Geography & Resource Science, Neijiang Normal University, Neijiang 641100, China.

出版信息

Entropy (Basel). 2019 Mar 11;21(3):269. doi: 10.3390/e21030269.

Abstract

The entropy-weighting method (EWM) and variation coefficient method (VCM) are two typical diversity-based weighting methods, which are widely used in risk assessment and decision-making for natural hazards. However, for the attributes with a specific range of values (RV), the weights calculated by EWM and VCM (abbreviated as and ) may be irrational. To solve this problem, a new indicator representing the dipartite degree is proposed, which is called the coefficient of dipartite degree (CDD), and the corresponding weighting method is called the dipartite coefficient method (DCM). Firstly, based on a large amount of statistical data, a comparison between the EWM and VCM is carried out. It is found that there is a strong correlation between the weights calculated by the EWM and VCM (abbreviated as and ); however, in some cases the difference between and is big. Especially when the diversity of attributes is high, may be much larger than . Then, a comparison of the DCM, EWM and VCM is carried out based on two case studies. The results indicate that DCM is preferred for determining the weights of the attributes with a specific RV, and if the values of attributes are large enough, the EWM and VCM are both available. The EWM is more suitable for distinguishing the alternatives, but prudence is required when the diversity of an attribute is high. Finally, the applications of the diversity-based weighting method in natural hazards are discussed.

摘要

熵权法(EWM)和变异系数法(VCM)是两种典型的基于多样性的赋权方法,广泛应用于自然灾害风险评估与决策。然而,对于具有特定取值范围(RV)的属性,由EWM和VCM计算得到的权重(分别简称为 和 )可能不合理。为解决这一问题,提出了一种表示二分程度的新指标,称为二分程度系数(CDD),相应的赋权方法称为二分系数法(DCM)。首先,基于大量统计数据,对EWM和VCM进行了比较。发现EWM和VCM计算得到的权重(分别简称为 和 )之间存在很强的相关性;然而,在某些情况下, 和 之间的差异较大。特别是当属性多样性较高时, 可能远大于 。然后,基于两个案例研究对DCM、EWM和VCM进行了比较。结果表明,对于具有特定RV的属性,DCM更适合确定权重;如果属性值足够大,EWM和VCM都可行。EWM更适合区分备选方案,但当属性多样性较高时需谨慎使用。最后,讨论了基于多样性的赋权方法在自然灾害中的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d4c/7514749/c29408a19af1/entropy-21-00269-g001.jpg

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