Publicacións

@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_decision-making_2023,

title = {A Decision-Making Tool for Port Operations Based on Downtime Risk and Met-Ocean Conditions including Infragravity Wave Forecast},

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

url = {https://www.mdpi.com/2077-1312/11/3/536},

doi = {10.3390/jmse11030536},

issn = {2077-1312},

year  = {2023},

date = {2023-03-01},

urldate = {2026-08-13},

journal = {Journal of Marine Science and Engineering},

volume = {11},

number = {3},

pages = {536},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {Port downtime leads to economic losses and reductions in safety levels. This problem is generally assessed in terms of uni-variable thresholds, despite its multidimensional nature. The aim of the present study is to develop a downtime probability forecasting tool, based on real problems at the Outer Port of Punta Langosteira (Spain), and including infragravity wave prediction. The combination of measurements from three pressure sensors and a tide gauge, together with machine-learning techniques, made it possible to generate long wave prognostication at different frequencies. A fitting correlation of 0.95 and 0.9 and a root mean squared error (RMSE) of 0.022 m and 0.012 m were achieved for gravity and infragravity waves, respectively. A wave hindcast in the berthing areas, met-ocean forecast data, and information on 15 real operational problems between 2017 and 2022, were all used to build a classification model for downtime probability estimation. The proposed use of this tool addresses the problems that arise when two consecutive sea states have thresholds above 3.97%. This is the limit for guaranteeing the safety of port operations and has a cost of just 0.6 unnecessary interruptions of operations per year. The methodology is easily exportable to other facilities for an adequate assessment of downtime risks.},

keywords = {long waves prediction, machine learning, multidimensional operational threshold, port management, Port operability},

pubstate = {published},

tppubtype = {article}

}