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

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.