Complex network-based identification of critical vessels in offshore wind farm areas

This paper proposes a method for identifying critical vessels within offshore wind farm (OWF) areas based on complex network analysis. The method aims to identify high-risk or influential vessels by treating vessels and the wind farm vertices within these areas as nodes in network. The importance of each node is assessed using topological metrics, including Vertex Strength (VS), Degree Centrality (DC), Clustering Coefficient (CC), and Betweenness Centrality (BC). To obtain a comprehensive ranking of vessel nodes, the entropy weight method and principal component analysis (PCA) are employed. This ranking helps identify the vessels that have the most significant impact on the overall safety of OWF areas. The effectiveness of the proposed method is validated using real AIS data, demonstrating its capability in accurately identifying critical vessels in OWF areas.