Papers

@article{carro_artificial_2026,

title = {Artificial intelligence to predict critical events in port operations},

author = {Humberto Carro-Fidalgo and Andrés Figuero-Pérez and José Sande-González-Cela and Enrique Peña-González and Alberto Alvarellos-González},

url = {https://doi.org/10.1007/s00773-026-01109-y},

doi = {10.1007/s00773-026-01109-y},

issn = {1437-8213},

year  = {2026},

date = {2026-02-01},

urldate = {2026-02-01},

journal = {Journal of Marine Science and Technology},

abstract = {Port downtime is one of the most important economic and safety issues. The objective of this paper is the design of a predictive tool based on machine learning, capable of identifying downtimes. We have compared two approaches: one that uses the moored ship motions and follows a more theoretical approach. It is based on the physics of the problem, but has the difficulty of using movements that are obtained in a costly and complex way. The other option directly estimates the probability of downtime from ocean-meteorological data and ignores the moored ship motions. This is a simplification, as it omits movement constraints, but it is also more realistic, as it is very complex to obtain data on the state of moorings or estimated tension. The dataset uses ocean-meteorological forecast data and downtimes recorded during the port operations of 799 ships obtained during 8 years. We applied regression models to obtain the variables related to infragravity wave and moored ship motions. We performed a multiclass random forest classification by adjusting the weighting of the dataset to identify the most promising approach. We evaluated techniques such as random forest or gradient boosting machine, selecting the GBM for better validation performance, with a log-loss of 0.0103. We obtained F2 and F1 scores of 0.971 and 0.941 respectively, with which the model correctly classified 98% of the anchorages and 88% of the berthing disruptions. Error analysis by sea state and by stay record indicates that some of them are due to the subjectivity inherent in the anchoring phenomenon. The tool offers an alternative to the traditional analysis of significant movements through an easily exportable methodology.},

keywords = {Downtimes, Field campaign, machine learning, Port operability},

pubstate = {published},

tppubtype = {article}

}

@article{costas_influence_2022,

title = {The influence of infragravity waves, wind, and basin resonance on vessel movements and related downtime at the Outer Port of Punta Langosteira, Spain},

author = {Raquel Costas and Andrés Figuero-Pérez and José Sande-González-Cela and Enrique Peña-González and Andrés Guerra},

url = {https://www.sciencedirect.com/science/article/pii/S0141118722003017},

doi = {10.1016/j.apor.2022.103370},

issn = {0141-1187},

year  = {2022},

date = {2022-12-01},

urldate = {2026-08-10},

journal = {Applied Ocean Research},

volume = {129},

pages = {103370},

abstract = {The coupling of low-frequency oscillatory modes of a harbor with horizontal motions of moored ships can amplify the latter, causing operational and safety problems. The objectives of this study were to analyze infragravity waves and their effect on moored ship motions; to validate the Wavelet Transform Analysis (WTA) method for studying port-ship resonance in the Outer Port of Punta Langosteira, Spain; and to study the climatic forcing of downtimes. The excited resonant modes of the basin (102 and 132 s) were determined using a numerical model and high-frequency water level measurements at different points of the harbor. Yaw and sway motions of three bulk carriers were analyzed in the frequency-time domain using WTA. The results showed that the oscillation periods of the studied vessel motions are directly related to the water depth to vessel draft ratio. In addition, when the wind speeds exceeds 20 km/h, the ship moves away from the fenders, which lead to a lengthening of these periods. Based on our findings, yaw may induce oscillations of the same period in sway. Additionally, operational thresholds were proposed based on outer and inner waves data that will lead to optimize ship stays, avoiding downtimes, and to advance the safety of loading and unloading operations.},

keywords = {Downtimes, Infragravity waves, Moored vessel motions, Operational thresholds, Vessel resonance, Wavelet transform analysis},

pubstate = {published},

tppubtype = {article}

}