Marine management and monitoring system based on AHP cost-benefit analysis with integrated evaluation and genetic algorithms for recurrent searches

Marine management and monitoring systems need to develop a safety procedure for submersibles that predicts changes in the position of the submersible over time. This would effectively prevent the submersible from losing communication with the host and would allow it to react to possible deficiencies. In this paper, a random walk model is used to predict the position of the submersible at each moment on the seafloor. The probability of the highest point on each plane is 0.87-0.92. The error in the position of the submersible caused by the uncertainty on the seafloor is corrected by the Kalman filter model. In this paper, we use AHP cost-benefit analysis to comprehensively evaluate and select the best additional search equipment. In this paper, the genetic algorithm based on cyclic search is used to determine the optimal initial deployment point and search pattern. After a large number of iterations, the shortest search path length of 817.273 is obtained, and the shortest search path is taken as the optimal path for search and rescue. In this paper, the model is extended using particle swarm algorithm and environment modelling. The predictive model of the submersible is combined with the environmental model of different sea areas. Therefore, the actual motion model of the submersible is approximated. After simulation, it was found that the accuracy of the submersible probability increased by 23.72%.