Volume 38, Issue 4 e12681
ORIGINAL ARTICLE

Extended TODIM-PROMETHEE II method with hesitant probabilistic information for solving potential risk evaluation problems of water resource carrying capacity

Xiao-kang Wang

Xiao-kang Wang

School of Business, Central South University, Changsha, China

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Hong-yu Zhang

Hong-yu Zhang

School of Business, Central South University, Changsha, China

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Jian-qiang Wang

Jian-qiang Wang

School of Business, Central South University, Changsha, China

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Jun-bo Li

Corresponding Author

Jun-bo Li

Business School, Guilin University of Technology, Guilin, China

Correspondence

Jun-bo Li, Business School, Guilin University of Technology, Guilin, China.

Email: [email protected]

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Lin Li

Lin Li

School of Business, Hunan University, Changsha, China

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First published: 10 March 2021
Citations: 15

Funding information: China Scholarship Council; National Natural Science Foundation of China

Abstract

With the excessive consumption and pollution of water resources, the sustainable development of water resources poses a serious threat currently. How to perceive and prevent the degradation of water resource in advance is an urgent problem. The water resource carrying capacity (WRCC) is a significant indicator to reflect the condition of water resources in a region. Resounding to these circumstances, our research establishes a decision support framework to solve WRCC risk evaluation issues. First, hesitant probabilistic fuzzy sets (HPFSs) are selected as a representation for the evaluation information in the expert group. Aimed at existing studies of HPFSs, some limitations are overcome involving the distance and comparison rule. Secondly, a TODIM-PROMETHEE II based multi-criteria group decision making (MCGDM) method is developed to overcome the inherent restrictions of PROMETHEE II method and make it suitable for a practical decision-making condition with bounded rationality. Subsequently, a case study is utilized to demonstrate the feasibility of our newly proposed decision support framework, followed by a sensitivity analysis and a comparison analysis. The outcome indicates that the framework has an excellent performance to solve this kind of MCGDM issues.

DATA AVAILABILITY STATEMENT

The data that supports the findings of this study are available in the supplementary material of this article.

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