A Semi-Supervised Machine Learning Model to Forecast Movements of Moored Vessels
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Romano Moreno, Eva; Tomás, Antonio; Díaz Hernández, Gabriel; López Lara, Javier; Molina, Rafael; García-Valdecasas, JavierFecha
2022-08Derechos
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
Publicado en
Journal of Marine Science and Engineering, 2022, 10, 1125
Editorial
MDPI
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Palabras clave
Semi-supervised machine learning
Regression-guided clustering
Inference model
Moored ship motions prediction
Port operability forecast
Resumen/Abstract
ABSTRACT: The good performance of the port activities in terminals is mainly conditioned by the dynamic response of the moored ship system at a berth. An adequate definition of the highly multivariate processes involved in the response of a moored ship at a berth is crucial for an appropriate characterization of port operability. The availability of an efficient forecast system of the movements of moored ships is essential for the planning, performance, and safety of the development of port operations. In this paper, an inference model to predict moored ship motions, based on a semisupervised Machine Learning methodology, is presented. A comparison with different supervised and unsupervised Machine Learning techniques, as well as with existing Deep Learning-based models for predicting moored ship motions, has been performed. The highest performance of the semi-supervised Machine Learning-based model has been obtained. Additionally, the influence of infragravity wave parameters introduced as predictor variables in the model has been analyzed and compared with the typical ocean waves, wind, and sea level as predictor variables. The prediction
model has been developed and validated with an available dataset of measured data from field campaigns in the Outer Port of Punta Langosteira (A Coruña, Spain).
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Excepto si se señala otra cosa, la licencia del ítem se describe como © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).