Wave spectrum estimation conditioned on machine learning-based output using the wave buoy analogy |
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| Affiliation: | 1. Faculty of Civil Engineering, Sahand University of Technology, Tabriz, Iran;2. Department of Civil Engineering, University of Hormozgan, Bandar Abbas, Iran;3. Faculty of Electrical Engineering , Sahand University of Technology, Tabriz, Iran;1. State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, 300350, China;2. Tianjin Key Laboratory of Port and Ocean Engineering, Tianjin University, Tianjin, 300350, China;3. Ocean College, Zhejiang University, Zhoushan, 316021, Zhejiang, China;4. Research Institute of Offshore Energy and Intelligent Construction, Tianjin University of Technology, Tianjin, 300384, China |
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| Abstract: | In this work, a hybrid approach for wave spectrum estimation is proposed. Fundamentally, the approach is based on the wave buoy analogy, processing ship response measurements, via a framework combining machine learning and a physics-based method dependent on available transfer functions. Specifically, a non-parametric (Bayesian) estimate is obtained of the directional wave spectrum conditioned on integral wave parameters established by a convolutional neural network. The developed method is assessed in a case study considering about two years of data obtained from an in-service container ship. The method produces good results, significantly improved when compared to the initial estimate made without constraints. |
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| Keywords: | Wave spectrum estimation Wave buoy analogy Hybrid method Convolutional neural network Transfer functions ERA5 Response prediction |
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