Abstract
Important information for the design and operation of oceanic systems can be obtained by assessing local sea state parameters such as significant height, peak period and incidence direction. Techniques for motion-based inference and their possible drawbacks have been extensively discussed in the literature (their motivation coming from the simplicity of the required instrumentation when compared to traditional measuring systems), and machine learning approaches are now appearing in a few investigations. This paper addresses the estimation problem through supervised learning, using time series with the movement of a moored vessel to train neural networks models so as to estimate the sea state. Such time series are obtained through simulations, that consider a model of a spread-moored FPSO (Floating Production Storage and Offloading) platform with constant draft, out of a set of metocean conditions observed at Brazil’s Campos Basin. A sensitivity analysis for different classes of neural networks was run, based on the significant height estimation, to choose the network architecture with the best results with respect to the mean absolute error metric. That topology was trained and employed in the estimation of the remaining sea state parameter, separately. The outcomes of the proposed models were confronted with other neural networks-based methods and showed up a comparable or slightly better performance in the error metrics. A preliminary discussion of the ability of the approach to deal with some classical issues on motion-based estimation is presented.
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Acknowledgments
Authors acknowledge Petrobras for providing long-term support and motivation to this work. The authors also thank the Center for Artificial Intelligence (C4AI-USP) and the support from the São Paulo Research Foundation (FAPESP grant #2019/07665-4) and from the IBM Corporation. The first and second authors acknowledge the Higher Education Personnel Improvement Coordination (Capes) for the scholarship. The third author was supported in part by Brazilian National Council for Scientific and Technological Development (CNPq) under Grant 312180/2018-7. The last author acknowledges the CNPq for the research grant (310127/2020-3).
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Bisinotto, G.A., Cotrim, L.P., Cozman, F.G., Tannuri, E.A. (2021). Sea State Estimation with Neural Networks Based on the Motion of a Moored FPSO Subjected to Campos Basin Metocean Conditions. In: Britto, A., Valdivia Delgado, K. (eds) Intelligent Systems. BRACIS 2021. Lecture Notes in Computer Science(), vol 13074. Springer, Cham. https://doi.org/10.1007/978-3-030-91699-2_21
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