Multi-criteria evaluation of tree species for afforestation in arid regions using a hybrid cubic bipolar fuzzy soft rough set framework

Dhuha Saleh Aldhuhayyan; Kholood Mohammad Alsager; Dhuha Saleh Aldhuhayyan; Kholood Mohammad Alsager

doi:10.3934/math.2025534

AIMS Mathematics

2025, Volume 10, Issue 5: 11813-11841. doi: 10.3934/math.2025534

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Research article Topical Sections

Multi-criteria evaluation of tree species for afforestation in arid regions using a hybrid cubic bipolar fuzzy soft rough set framework

Department of Mathematics, College of Sciences, Qassim University, Buraydah, Saudi Arabia; 441211878@qu.edu.sa

Received: 08 March 2025 Revised: 04 May 2025 Accepted: 12 May 2025 Published: 22 May 2025
MSC : 05C25, 11E04, 20G15

Decision rules are effective tools for managing information and characterizing datasets. As a result, they contribute significantly to fuzzy rough-set theory-based decision-making procedures. Rough set theory (RS) is a robust method for analyzing ambiguity in data. Moreover, cubic bipolar fuzzy sets (CBFS), an extension of bipolar fuzzy sets, can discuss both uncertainty and bipolarity in numerous situations. This article presents the robust decision-making approach named cubic bipolar fuzzy soft rough sets (CBFSRSs) by integrating RS and cubic bipolar fuzzy soft sets. This study explores the construction and fundamental characteristics of a novel approach based on CBFSs. We introduce and examine the concept of rough sets based on CBFSs, develop level sets for CBFSs, and highlight their key properties through illustrative examples. Additionally, we propose a decision-making framework based on CBFSRS that is capable of effectively managing uncertain, conflicting, and imprecise information. This approach demonstrates the potential of CBFSs in enhancing decision-making processes in large data environments. To demonstrate the practical benefit of CBFSRSs in decision-making, we provide an example of how CBFSRS standards might be used in decision-making processes to help decision-makers make well-informed and reasoned decisions. The example shows that the proposed strategies are useful and effective by applying them to real-life problems. It proves that they can handle complex, uncertain, and conflicting information in real decision-making situations.
- CBFSRSs,
- bipolar fuzzy set,
- rough set,
- decision-making
Citation: Dhuha Saleh Aldhuhayyan, Kholood Mohammad Alsager. Multi-criteria evaluation of tree species for afforestation in arid regions using a hybrid cubic bipolar fuzzy soft rough set framework[J]. AIMS Mathematics, 2025, 10(5): 11813-11841. doi: 10.3934/math.2025534

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Abstract

Decision rules are effective tools for managing information and characterizing datasets. As a result, they contribute significantly to fuzzy rough-set theory-based decision-making procedures. Rough set theory (RS) is a robust method for analyzing ambiguity in data. Moreover, cubic bipolar fuzzy sets (CBFS), an extension of bipolar fuzzy sets, can discuss both uncertainty and bipolarity in numerous situations. This article presents the robust decision-making approach named cubic bipolar fuzzy soft rough sets (CBFSRSs) by integrating RS and cubic bipolar fuzzy soft sets. This study explores the construction and fundamental characteristics of a novel approach based on CBFSs. We introduce and examine the concept of rough sets based on CBFSs, develop level sets for CBFSs, and highlight their key properties through illustrative examples. Additionally, we propose a decision-making framework based on CBFSRS that is capable of effectively managing uncertain, conflicting, and imprecise information. This approach demonstrates the potential of CBFSs in enhancing decision-making processes in large data environments. To demonstrate the practical benefit of CBFSRSs in decision-making, we provide an example of how CBFSRS standards might be used in decision-making processes to help decision-makers make well-informed and reasoned decisions. The example shows that the proposed strategies are useful and effective by applying them to real-life problems. It proves that they can handle complex, uncertain, and conflicting information in real decision-making situations.

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