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

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dc.authoridLi, Deyu/0000-0003-2489-9404
dc.authoridZhang, Chao/0000-0001-6248-9962
dc.authoridLi, Wentao/0000-0002-7777-0818
dc.authorwosidLi, Deyu/G-6816-2011
dc.contributor.authorZhang, Chao
dc.contributor.authorZhang, Jingjing
dc.contributor.authorLi, Wentao
dc.contributor.authorPedrycz, Witold
dc.contributor.authorLi, Deyu
dc.date.accessioned2024-05-19T14:41:56Z
dc.date.available2024-05-19T14:41:56Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractForest 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.en_US
dc.description.sponsorshipNational Natural Science Foundation of China [12201518, 62072294, 61972238, 2022Y147]; Graduate Education Innovation Programs of Shanxi Province [202204051001015]; Special Fund for Science and Technology Innovation Teams of Shanxi [KJQN202100206]; Science and Technology Research Program of Chongqing Education Commission [KJQN202100205, 2019SK036]; Training Program for Young Scientific Researchers of Higher Education Institutions in Shanxi; Cultivate Scientific Research Excellence Programs of Higher Education Institutions in Shanxi [2022-007]; Shanxi Scholarship Council of China; [62272284]en_US
dc.description.sponsorshipThis paper was supported in part by the National Natural Science Foundation of China (62272284; 12201518; 62072294; 61972238) , the Graduate Education Innovation Programs of Shanxi Province (2022Y147) , the Special Fund for Science and Technology Innovation Teams of Shanxi (202204051001015) , the Science and Technology Research Program of Chongqing Education Commission (KJQN202100206; KJQN202100205) , the Training Program for Young Scientific Researchers of Higher Education Institutions in Shanxi, the Cultivate Scientific Research Excellence Programs of Higher Education Institutions in Shanxi (CSREP) (2019SK036) , and the Research Project Supported by Shanxi Scholarship Council of China (2022-007) .en_US
dc.identifier.doi10.1016/j.asoc.2023.110539
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-85164225511en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.asoc.2023.110539
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5180
dc.identifier.volume145en_US
dc.identifier.wosWOS:001055698300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofApplied Soft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectGranular Computingen_US
dc.subjectThree-Way Decisionen_US
dc.subjectMulti-Granularityen_US
dc.subjectInformation Granularityen_US
dc.subjectFuzzy Rough Seten_US
dc.titleA regret theory-based multi-granularity three-way decision model with incomplete T-spherical fuzzy information and its application in forest fire managementen_US
dc.typeArticleen_US

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