Publicacións

@article{costas_wind_2024,

title = {Wind influence and bathymetric effects in wave overtopping based on a long-term field campaign in the Outer Port of Punta Langosteira (Spain)},

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

url = {https://doi.org/10.1080/20464177.2023.2255330},

doi = {10.1080/20464177.2023.2255330},

issn = {2046-4177},

year  = {2024},

date = {2024-11-01},

urldate = {2026-08-13},

journal = {Journal of Marine Engineering & Technology},

volume = {23},

number = {6},

pages = {383–395},

publisher = {Taylor & Francis},

abstract = {The operability of harbour facilities and the safety of those working within them can be compromised by overtopping. The aim of the present study is to analyse this phenomenon through a long-term field campaign, of 5 years’ duration, in the Outer Port of Punta Langosteira (Spain). Image monitoring techniques were used to identify the events, which allowed to study the influence of climatic variables and the distribution of overtopping. Results show that the place with the highest density of events was located in the central area of the third alignment. This turned out to be the area of sea wave concentration generated by the bathymetry, as shown by the propagation with a numerical model. Furthermore, wind with a magnitude greater than 30 km/h perpendicular to the breakwater increased the number of events per sea state, that is, the probability that the phenomenon will occur. In addition, the volume of 29 overtopping events and the mean discharge of 13 sea states were estimated using measurements from two pressure sensors on the crest of the breakwater. The results of this study allow to design a breakwater access warning system integrated with open access into the Port Authority’s Environmental Control Panel (http://cma.puertocoruna.com).},

keywords = {Field campaign, mean discharge, Overtopping, port management},

pubstate = {published},

tppubtype = {article}

}

@article{alvarellos_deep_2024,

title = {Deep Learning-Based Wave Overtopping Prediction},

author = {Alberto Alvarellos-González and Andrés Figuero-Pérez and Santiago Rodríguez-Yáñez and José Sande-González-Cela and Enrique Peña-González and Paulo Rosa-Santos and Juan R. Rabuñal-Dopico},

url = {https://www.mdpi.com/2076-3417/14/6/2611},

doi = {10.3390/app14062611},

issn = {2076-3417},

year  = {2024},

date = {2024-01-01},

urldate = {2026-04-20},

journal = {Applied Sciences},

volume = {14},

number = {6},

pages = {2611},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {This paper analyses the application of deep learning techniques for predicting wave overtopping events in port environments using sea state and weather forecasts as inputs. The study was conducted in the outer port of Punta Langosteira, A Coruña, Spain. A video-recording infrastructure was installed to monitor overtopping events from 2015 to 2022, identifying 3709 overtopping events. The data collected were merged with actual and predicted data for the sea state and weather conditions during the overtopping events, creating three datasets. We used these datasets to create several machine learning models to predict whether an overtopping event would occur based on sea state and weather conditions. The final models achieved a high accuracy level during the training and testing stages: 0.81, 0.73, and 0.84 average accuracy during training and 0.67, 0.48, and 0.86 average accuracy during testing, respectively. The results of this study have significant implications for port safety and efficiency, as wave overtopping events can cause disruptions and potential damage. Using deep learning techniques for overtopping prediction can help port managers take preventative measures and optimize operations, ultimately improving safety and helping to minimize the economic impact that overtopping events have on the port’s activities.},

keywords = {Deep learning, machine learning, Neural networks, port management, port security, wave overtopping prediction},

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}

}