区域适宜性多准则评价中聚合算子的选择

S. Kuznichenko, I. Buchynska
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引用次数: 0

摘要

本文讨论了在对土地适宜性进行多标准分析时,通过选择适当类型的聚合算子来提高标准评估卷积结果的充分性和有效性的问题,这些聚合算子可以在GIS环境中执行,并且具有允许对该应用区域的特征进行最完整形式化的专家知识的属性。在分析多准则决策模型中信息模糊性产生原因的基础上,提出了聚合算子必须具备的属性。比较分析了各种聚合算子用于构建复杂的领土适宜性地图。研究了聚合算子的执行特征:最小、最大、算术平均、加权和、OWA Yager算子。结果表明,最合理的选择是使用带有模糊量词的OWA Yager操作符,它允许您提供关于根据各个标准的评估之间折衷的可接受形式的专家信息。提出了一组RIM量词来形式化决策者在决策中对风险的态度。给出了一个使用带有模糊量词的OWA Yager算子计算准则评价卷积的例子。结果表明,带有模糊量词的Yager OWA算子是一种通用的聚合算子,因为它能够实现从最小算子到最大算子的各种决策策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SELECTION OF AGGREGATION OPERATORS FOR A MULTI-CRITERIA EVALUTION OF SUTABILITY OF TERRITORIES
The article discusses the issues of improving the adequacy and validity of the results of the convolution of criteria evaluation into a generalized evaluation when conducting a multi-criteria analysis of the land suitability  by choosing the appropriate type of aggregation operator that can be performed in the GIS environment and has the properties that allow the most complete formalization of expert knowledge about the features of this applied area. Based on the analysis of the causes of the fuzziness of information in multicriteria decision-making models, the properties that the aggregation operator must have are formulated. A comparative analysis of various aggregation operators for the construction of complex maps of the suitability of territories is carried out. The features of the execution of aggregation operators are investigated: minimum, maximum, arithmetic mean, weighted sum, OWA Yager operator. It is shown that the most justified choice is to use the OWA Yager operator with fuzzy quantifiers, which allows you to provide expert information on the acceptable form of a compromise between evalutions by individual criteria. The use a family of the RIM quantifiers to formalize the attitude of DM to risk in making decisions is proposed. An example of the use of the OWA Yager operator with fuzzy quantifiers for calculating the convolution of criteria evalutions is given. It is shown that the Yager OWA operator with fuzzy quantifiers is a universal aggregation operator, since it has the ability to implement a wide range of decision-making strategies: from  minimum operator to maximum operator.
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