4.6 Article

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

期刊

ENTROPY
卷 21, 期 3, 页码 -

出版社

MDPI
DOI: 10.3390/e21030269

关键词

diversity-based weighting method; entropy-weighting method; variation coefficient method; risk assessment; decision-making; natural hazards

资金

  1. CRSRI Open Research Program
  2. Scientific Research Fund of Sichuan Provincial Education Department [17ZB0222]

向作者/读者索取更多资源

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 W-E and W-V) 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 W-E and W-V); however, in some cases the difference between W-E and W-V is big. Especially when the diversity of attributes is high, W-E may be much larger than W-V. 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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据