A Z-number based multi-attribute decision-making algorithm for hydro-environmental system management

• Published in: Neural computing & applications 2023-03, Vol.35 (9), p.6405-6421
• Main Authors: Nourani, Vahid, Najafi, Hessam
• Format: Article
• Language: English
• Subjects: Algorithms; Alternatives; Analytic hierarchy process; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Decision making; Environmental management; Fuzzy logic; Hierarchies; Image Processing and Computer Vision; Irrigation efficiency; Original Article; Probability and Statistics in Computer Science; Rehabilitation; Reliability; Sustainable development; Uncertainty
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-022-08025-3

Multi-Attribute Decision-Making (MADM) is still an open issue under uncertain circumstances. This research aimed to introduce a new MADM method for dealing with highly uncertain circumstances. Lake Urmia in Iran suffers from natural and human stimuli. For rapid and sustainable lake rehabilitation, a multidisciplinary and flexible approach is needed to address the divergent benefits and multiple goals. MADM can be accomplished using various techniques such as the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the Analytic Hierarchy Process (AHP), and others. According to the uncertainty, in recent years the hybrid of fuzzy logic with well-known MADM methods such as fuzzy AHP (FAHP) has been used frequently. The main problem of the traditional MADM methods (and fuzzy-based MADM methods) is their weakness in dealing with the ambiguous situations common in real-life scenarios, especially in water crisis problems. For example, the decision, which was accepted based on low-reliability data, tends to be useless or harmful. In contrast to the traditional MADM methods, which ignore the reliability of the information, Z-numbers, as a new generation of the fuzzy logic method, include both constraint and reliability of information and have great potential to explain human knowledge uncertainty. This study developed a new MADM approach based on the Z-numbers concept. In this regard, seven alternatives were proposed and evaluated using the economic, social, environmental, and technical criteria of sustainable development. The obtained results were then compared with the results of the AHP, FAHP, and TOPSIS methods. Improving irrigation efficiency was chosen as the best alternative based on the final ranking of the alternatives.