Serr, JohannaJohannaSerrWermbter, MalwinMalwinWermbterAbdel-Maksoud, MoustafaMoustafaAbdel-Maksoud2026-01-072026-01-072025-0813th International Workshop on Ship and Marine Hydrodynamics, IWSH 2025https://hdl.handle.net/11420/60581Monitoring the surrounding sea state enhances operational safety and energy efficiency of vessels during voyage. The directional sea state can be estimated from measured vessel responses using physics-based models following the Wave-Buoy Analogy (WBA) or data-driven methods like Convolutional Neural Networks (CNNs). This study explores both approaches for parametric sea state estimation using simulation and full-scale measurement data of a barge. The issue of local minimum convergence in the least-squares algorithm is addressed by a modified implementation of the parametric WBA. The modification involves constraining the relative wave direction during optimization, leading to more robustness against outliers in the estimation of the peak period and relative wave direction. The study emphasizes that the methods provide feasible approaches for parametric sea state estimation on the given data, with the WBA requiring knowledge of the vessel-specific conditions and the CNN depending on the representativeness of the training data.enhttps://creativecommons.org/licenses/by/4.0/Sea state estimationwave-buoy analogyfull-scale measurementsresponse amplitude operatorsTechnology::623: Military Engineering and Marine Engineering::623.8: Naval Architecture; ShipbuildingComputer Science, Information and General Works::006: Special computer methods::006.3: Artificial Intelligence::006.32: Neural NetworksConference Paperhttps://doi.org/10.15480/882.1638010.15480/882.16380Conference Paper