Extended TODIM‐PROMETHEE II method with hesitant probabilistic information for solving potential risk evaluation problems of water resource carrying capacity. Issue 4 (10th March 2021)

• Record Type: Journal Article
• Authors: Wang, Xiao‐kang; Zhang, Hong‐yu; Wang, Jian‐qiang; Li, Jun‐bo; Li, Lin
• Other Names: Chakraborty Tanmoy guestEditor.; Bhatia Sumit guestEditor.; Caragea Cornelia guestEditor.; Moreira Fernando guestEditor.; Rocha Álvaro guestEditor.; Dubey Ashwani Kumar guestEditor.
• Is Part Of: Expert systems. Volume 38:Issue 4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-03-10
• Subjects: distance measure -- hesitant probabilistic fuzzy set -- multi‐criteria group decision‐making -- TODIM‐PROMETHEE II method; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12681
• Languages: English
• ISSNs: 0266-4720

Abstract

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.