@article{10902/26358,
year = {2022},
month = {8},
url = {https://hdl.handle.net/10902/26358},
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).},
organization = {This research has been supported by a FPU (Formación de Profesorado Universitario) grant from the Spanish Ministry of Science, Innovation and Universities to the first author (FPU18/03046). This work has been also partially funded under the Algeciras BrainPort 2020 Program (ABP2020) of the Port Authority of Algeciras Bay, within the project Sistema avanzado de predicción de la operatividad buque-infraestructura. Expedient code: 2017-001 CPI.},
publisher = {MDPI},
publisher = {Journal of Marine Science and Engineering, 2022, 10, 1125},
title = {A Semi-Supervised Machine Learning Model to Forecast Movements of Moored Vessels},
author = {Romano Moreno, Eva and Tomás, Antonio and Díaz Hernández, Gabriel and López Lara, Javier and Molina, Rafael and García-Valdecasas, Javier},
}