A regret theory-based multi-granularity three-way decision model with incomplete T-spherical fuzzy information and its application in forest fire management

Forest fires are an abrupt and highly destructive meteorological disaster that can occur in all regions of the world, resulting in significant ecological, economic and social losses. Moreover, the causes of forest fire disasters are usually complex, involving several uncertain factors such as temperature, relative humidity, wind speed and rainfall. All of those pose the greatest challenge to the study of forest fire management (FRM). In order to efficiently explore FRM via valid intelligent decision-making techniques, a novel model of regret theory (RT)-based multi-granularity (MG) three-way decisions (TWD) in incomplete T-spherical fuzzy (T-SF) environments has been constructed, where incomplete T-spherical fuzzy sets (T-SFSs) have been employed to describe diverse types of uncertain information in FRM, and RT-based MG TWD is conducive to analyzing multi-source T-SF information via reducing decision risks and modeling bounded rationality owned by decision-makers (DMs). Specifically, the concept of MG T-SF incomplete information systems (IISs) has been first constructed for information depictions of FRM. Then, MG T-SF IISs have been processed via the presented T-SF similarity principles for developing adjustable MG T-SF probabilistic rough sets (PRSs). Afterwards, an RT-based MG TWD approach has been built with the support of adjustable MG T-SF PRSs. Finally, a real-world FRM case analysis has been performed by using the built RT-based MG TWD approach, and extensive comparative and experimental analyses have been performed to validate the practicability of the presented methodology. To sum up, the presented methodology has simultaneously incorporated MG T-SF IISs, MG TWD and RT to model various uncertainties, valid information fusion processes and bounded rationality for FRM, which serves as a valid intelligent decision-making technique in processing incomplete and imprecise multi-source information with plentiful decision risks and regret emotions.& COPY; 2023 Elsevier B.V. All rights reserved.