Abstract
Existing models for assessing ship collision risk involve complex calculations that complicate the simultaneous qualitative and quantitative analysis of the factors affecting ship navigation safety. Therefore, these models often exhibit slow generation of the risk index and evaluation results with reduced accuracy. To resolve these issues, we model the ship collision risk based on the cloud model theory. Specifically, we select “distance of closest point of approach (DCPA)” and “time to closest point of approach (TCPA)” as the main factors affecting the ship collision risk and analyze the data of DCPA, TCPA, and collision risk index (CRI) based on their cloud models. By combining these analyses with a double-condition-single-rule generator, we construct a cloud model for ship collision risk and finally develop a cloud model-based inference engine system to assess ship collision risk. This engine allows us to establish different ship collision risk analysis models according to the scenario encountered by the ship, which can be used to verify the feasibility of the proposed algorithm for ship collision risk modeling. Through comparisons with traditional ship collision risk models, the proposed ship collision risk model is found to be superior owing to its simple implementation, accurate results, and shorter time required to generate the risk model. The model established in this study enables the crew to determine the key objects to be avoided in case of potential collision with multiple ships. At last,analysis and research of cloud model ship collision risk based on global sensitivity and uncertainty are done to reduce the dimension of the risk parameters and show the main factors of unstable collision risk,therefore,the uncertain results in the calculation of the degree of danger are avoided, some reasonable suggestions are proposed for real navigation safety. the maritime pilot can make correct decisions promptly to reduce or avoid the occurrence of collision accidents.
Highlights
Fuzzy analysis, gray theory, comprehensive safety evaluation, and fuzzy comprehensive evaluation have been used extensively for safety assessment of ship navigation [1][2][3]
Considering these merits, we propose a method to assess ship collision risk based on cloud model theory
CONCEPT CLASSIFICATION FOR PARAMETERS USED IN SHIP COLLISION RISK CLOUD MODEL After examining the collision avoidance behavior and the international collision avoidance rules at sea, we classify distance of closest point of approach (DCPA), to closest point of approach (TCPA), and collision risk index (CRI) into {small, relatively small, medium, relatively large, large} via natural language concepts according to the navigation density of the sea, as discussed in the literature [11][12]
Summary
Gray theory, comprehensive safety evaluation, and fuzzy comprehensive evaluation have been used extensively for safety assessment of ship navigation [1][2][3]. These methods cannot combine qualitative and quantitative analyses of the factors affecting ship navigation safety, which reduces the accuracy of the evaluation results. Under the influence of many intricate factors, the cloud model can enable conversion between the qualitative concepts and the quantitative data while simultaneously checking for loopholes in the traditional methods. Because the number of calculations performed on cloud drops is directly proportional to the accuracy of the evaluation results obtained from the cloud model, the cloud model is superior to other methods for use in evaluation Considering these merits, we propose a method to assess ship collision risk based on cloud model theory.
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