Authors : Javeria Fiaz; Sami Ud Din; Imran Ullah

Volume/Issue : Volume 11 - 2026, Issue 10 - October

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Google Scholar : https://tinyurl.com/4cmnudzz

DOI : https://doi.org/10.38124/ijisrt/26Oct657

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : The accurate classification of ocean wave phenomena is essential for maritime surveillance, coastal engineering, and environmental monitoring applications. This study addresses the challenge of distinguishing between ship-generated Kelvin wakes and natural plunging breaking waves using machine learning techniques. A synthetic dataset consisting of 800 samples (400 per wave type) is generated under controlled simulation conditions.

Keywords : Maritime Surveillance; Wave Classification; Kelvin Wake Detection; Plunging Wave; Machine Learning; Remote Sensing; SAR Simulation.

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