Because of climatic hazards and extreme weather events, meteorological disasters attract more and more attention of government, national, and international agencies. Every event tests people’s ability to cope with meteorological disasters and generates the need for disaster risk research and assessments. Social vulnerability is an important measure of disaster risk assessments. Social vulnerability assessment problem can be viewed as a multi-criteria decision-making problem. In order to satisfy the perception of special disaster bearers, we need a local-context approach to construct a social vulnerability evaluation index system. The key to this approach is to identify the evaluation criteria structure by analyzing the complicated information gathering from special disaster bearers. It’s natural to use fuzzy language to express disaster bearers’ preferences in a complicated context. This paper attempts to describe the interrelationship between the evaluation factors with linguistic preferences since linguistic variables can better reflect the vagueness of human being. The fuzzy interpretive structural modeling (FISM) approach has been employed to develop the structural relationship between social vulnerability evaluation factors. In FISM, we apply some computational models of computing with words to quantify the fuzzy interrelationship. Finally, we give an example to show the process of our method.

由于气候灾害和极端天气事件，气象灾害越来越引起政府，国家和国际机构的关注。每个事件都会测试人们应对气象灾害的能力，并引发对灾害风险研究和评估的需求。社会脆弱性是评估灾害风险的重要措施。社会脆弱性评估问题可以看作是多标准决策问题。为了满足对特殊灾难承担者的看法，我们需要一种本地上下文方法来构建社会脆弱性评估指标体系。这种方法的关键是通过分析从特殊灾难携带者那里收集的复杂信息来确定评估标准的结构。在复杂的情况下，使用模糊语言来表达容灾者的偏好是很自然的。本文试图描述评估因素与语言偏好之间的相互关系，因为语言变量可以更好地反映人的模糊性。模糊解释结构模型（FISM）方法已被用来发展社会脆弱性评估因素之间的结构关系。在FISM中，我们应用一些带字的计算模型来量化模糊相互关系。最后，我们给出一个例子来说明我们方法的过程。模糊解释结构模型（FISM）方法已被用来发展社会脆弱性评估因素之间的结构关系。在FISM中，我们应用一些带字的计算模型来量化模糊相互关系。最后，我们给出一个例子来说明我们方法的过程。模糊解释结构模型（FISM）方法已被用来发展社会脆弱性评估因素之间的结构关系。在FISM中，我们应用一些带字的计算模型来量化模糊相互关系。最后，我们给出一个例子来说明我们方法的过程。