Citation

Anbazhagan, Kanchana and Deivanayagampillai, Nagarajan and Thanagodi, Nithya (2026) A Machine Learning-Based Multi-Criteria Decision-Making Approach Utilizing D-Numbers for Water-Energy-Food Nexus Assessment. Nature Environment and Pollution Technology, 25 (1). B4326. ISSN 2395-3454

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Abstract

The interdependency between the water and energy infrastructure represents the core challenge of resource management. Effective decision-making for water-energy-food (WEN) scenarios requires robust tools. Traditional Multi-Criteria Decision-Making (MCDM) approaches are undermined by uncertainty because they assume perfect and complete information, which rarely occurs in Water-Energy Nexus (WEN) issues. Classical models oversimplify the complex interconnections between water and energy systems and therefore result in suboptimal decision-making approaches. Although fuzzy and intuitionistic models are efforts towards uncertainty modelling, they also fall short of fully capturing the dynamics of real-world scenarios. They are inefficient in addressing conflicting and uncertain information, which hinders the practical implementation of these techniques. In addition, the lack of a platform that unites MCDM with integrated uncertainty management increases decisionmaking complications. To bridge these gaps, the current study proposes a new framework that integrates D-number-based multi-criteria analysis with Dempster-Shafer theory (DST) for WEN decision-making. The integration of DST rigorously enhances the ability of DST to process complete, uncertain, and conflicting information for WEN decision-making. The study also compared the performance of the Random Forest and Optimized Artificial Neural Network models.

Item Type:	Article
Uncontrolled Keywords:	Water-energy nexus
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	02 Apr 2026 02:40
Last Modified:	06 Apr 2026 04:00
URII:	http://shdl.mmu.edu.my/id/eprint/15619

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