An Intelligent Decision Algorithm for the Generation of Maritime Search and Rescue Emergency Response Plans

Abstract

Maritime search and rescue (SAR) decisions are the most important part of maritime SAR operations. In the process of making maritime SAR decisions, a key factor affecting efficiency and success rate is how to quickly respond to accidents and develop an emergency response plan. At present, maritime SAR emergency response plans are still mostly obtained through a combination of drift prediction models and SAR experience. There is a lack of SAR resource scheduling and SAR task assignment. The primary purpose of this paper is to explore the possibility of using an intelligent decision-making algorithm to formulate maritime SAR emergency response plans so as to produce results more scientifically. First, the relevant research areas and research data are briefly introduced, and the main mathematical models involved in the optimal search theory are expounded. Next, key technologies involved in the process of maritime SAR emergency response plan generation, including the determination of search area, the scheduling of SAR resources, the allocation of search tasks, and the planning of search routes, are analyzed in detail. Two optimization model algorithms, namely the SAR resource scheduling model based on genetic simulated annealing algorithm (GSAA) and the regional task allocation algorithm based on space-time characteristics, are proposed as approaches to solving the problem of resource scheduling and task allocation. Finally, the effectiveness and optimization of the proposed algorithms are verified by analyzing the emergency response of a real case which occurred in the Bohai Sea and comparing the different schemes. Through the algorithm proposed in this paper, the efficiency of maritime SAR operations can be effectively improved and the loss of life and property can be reduced.

A Time Domain-Based Iterative Method for Helicopter Maritime Search Area Planning and Construction of the Simulation Environment

“…The samples detected in the simulation are marked in blue, and the POS was calculated using the ratio of the number of detected samples to the total number of samples. For comparison, search areas were also determined by the PSO [20], [21] and MBR [18] [19] methods, which are the most commonly used planning methods. The simulation results are shown in Fig.…”

“…Step 2. Generate the minimum convex hull using the Graham scan algorithm [19], [31], [32], which is an efficient algorithm for finding the convex hull with time complexity O(nlgn). Fig.…”

“…Content may change prior to final publication. while the POS of the search areas planned by the TIP, MBR [18] [19], and PSO [20], [21] methods were compared.…”

“…Using the cell iterative search method, they determined the optimal search area [18]. In a further study of Agbissoh's work, Bo Ai et al defined the probability of successful search and rescue (POSSAR) and proposed a SAR resource scheduling model based on the genetic simulated annealing algorithm with the goal of maximizing the POSSAR [19]. In addition, a metaheuristic algorithm has been adopted to determine the optimal search areas.…”

An Asynchronous Trajectory Matching Method Based on Piecewise Space-Time Constraints

“…The use of the AIS helps to strengthen the safety of life at sea, improve the safety and efficiency of navigation, and protect the marine environment. It has been widely used in marine ship data mining [3], [4], marine ship safety monitoring [5], [6], marine ecological environment supervision [7], and marine search and rescue work [8]. However, given that AIS is a self-reporting system, the crew can close the AIS resulting in the loss of the AIS signal to prevent the surveillance system and its operators from discovering its illegal activities.…”