Enterprise system algorithm development for intelligent maritime safety systems using Fuzzy Chaotic C-Means logistic mapping

Abstract

Port authorities require interpretable methods to identify sea areas with elevated abnormal ship behaviour risk in intelligent maritime safety systems. This study develops an enterprise system algorithm that integrates fuzzy inference, Fuzzy C-Means, chaos-based logistic mapping, and Marine Geographic Information System visualisation using Automatic Identification System data. The framework constructs grid-level indicators from Course Over Ground, Speed Over Ground, Rate Of Turn, heading, Cross-Track Distance, and drift angle. It produces the Manoeuvring Dynamics Index and Trajectory Deviation Index, then transforms grid scores into chaos-prone risk patterns. The results support hotspot identification, patrol planning, and traffic management.