Novel situation assessment method for amphibious aircraft maritime rescue using probabilistic linguistic hybrid cloud model and best-worst method

Maritime rescue aims to safeguard individuals and assets amidst maritime emergencies, wherein amphibious aircraft play a proactive role by providing reactive and dependable support for rescue operations. The uncertain maritime emergency environment poses significant challenges to amphibious aircraft maritime rescue (AAMR), necessitating an urgent need for a systematic situation assessment to capture and address the threats and risks involved. Current assessment methods, however, suffer from critical deficiencies in addressing multiple uncertainties in situation information and measuring the impacts of interacting threat factors in the maritime emergency environment. To fill these gaps, this paper proposes a novel situation assessment method using probability linguistic hybrid cloud (PLHC) model and best-worst method (BWM) to delineate the optimal situation level of AAMR. Initially, the situation assessment model for AAMR is developed where the threatening factors are identified through literature reviews and empirical analysis. Then, to facilitate reasonable knowledge utilization, the PLHC model, which combines probability linguistic term sets (PLTSs) and hybrid normal and trapezium clouds, is introduced to address experts’ assessment with various uncertainties such as hesitation, and fuzziness. Moreover, the enhanced BWM method is extended to determine the weights of the threatening factors and their mutual interactions. Finally, validated through vessel-sinking emergency scenarios, the proposed method can offer significant situation information for pilots and maritime authorities to facilitate more effective maritime rescue strategies, highlighting its vital role in enhancing maritime emergency response capabilities.