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Decision support systems in offshore vessels utilize wave parameters in combination with physics-based vessel models to predict the vessel behavior prior to the initiation and execution of a marine operation. These predictions are, usually, accompanied by significant uncertainties inherent in the estimation of wave statistical parameters, idealized parametric spectra, and system variables. Consequently, the predictions may deviate considerably from the real behavior of the vessel. Therefore, this study uses numerical wave spectra corresponding to a site in the North Sea in conjunction with a hydrodynamic model adapted to measurements to make more accurate intermediate-term response predictions. Considering a weather-restricted marine operation, the intermediate-term predictions involve simulating the responses for any time window within the upcoming 72 hours. The vessel model’s uncertainty is minimized by calibrating the influential parameters utilizing the full-scale response measurements within an optimization framework. The subsequent Roll predictions based on calibrated parameters exhibit better alignment with the measured Roll motions. The application of recursive optimization showed a significant reduction in prediction errors in an actual marine operation.

This thesis was developed underMarineUAS -Innovative Training Network on Autonomous Unmanned Aerial Systems for Marine and Coastal Monitoring. This project has received funding from the European Union's Horizon 2020 research and innovation programme, under the Marie Sklodowska-Curie grant agreement No. 642153.