The intuitionistic fuzzy linguistic assessment of forest soil quality with multi-granularity qualitative information

IF 2.9 4区环境科学与生态学 Q3 ENVIRONMENTAL SCIENCES

Environmental Monitoring and Assessment Pub Date : 2025-01-31 DOI:10.1007/s10661-024-13612-z

Junzhe Zhang, Tao Wu, Jian Lin

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引用次数: 0

Abstract

The soil quality of forest land is directly related to the growth of forest trees and the local ecological environment. This paper proposes an intuitionistic fuzzy linguistic aggregation method for heterogeneous linguistic assessment information, to solve the multi-index assessment problem containing heterogeneous linguistic information. Based on this method, the soil quality of forest land can be evaluated reasonably. Firstly, the multi-granularity heterogeneous linguistic terms are effectively transformed by the defined linguistic scaling function. Secondly, the formula of the deviation coefficient between linguistic terms is given. By fully considering evaluators’ risk preferences, the intuitionistic fuzzy linguistic numbers are determined using this formula. Simultaneously, the assessment index’s objective weight is obtained based on the distance calculation formula of intuitionistic fuzzy linguistics. Finally, the intuitionistic fuzzy linguistic multi-attribute assessment (IFLMA) method with heterogeneous linguistic assessment information is proposed. The numerical example is developed to illustrate the validity of the proposed method in assessing the soil quality of forest land. Comparative and sensitivity analyses prove that this method has unique advantages in processing heterogeneous linguistic assessment information.

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来源期刊

Environmental Monitoring and Assessment 环境科学-环境科学

CiteScore

4.70

自引率

6.70%

发文量

1000

审稿时长

7.3 months

期刊介绍： Environmental Monitoring and Assessment emphasizes technical developments and data arising from environmental monitoring and assessment, the use of scientific principles in the design of monitoring systems at the local, regional and global scales, and the use of monitoring data in assessing the consequences of natural resource management actions and pollution risks to man and the environment.