Deep Learning-Based Wave Overtopping Prediction
![Thumbnail](/dspace/bitstream/handle/2183/36619/Alvarellos_Alberto_2024_Deep_Learning_Based_Wave_Overtopping_Prediction.pdf.jpg?sequence=5&isAllowed=y)
Use this link to cite
http://hdl.handle.net/2183/36619Collections
- GI-RNASA - Artigos [190]
- CITEEC-GEAMA - Artigos [91]
- GI-ISLA-Artigos [17]
Metadata
Show full item recordTitle
Deep Learning-Based Wave Overtopping PredictionAuthor(s)
Date
2024-03-20Citation
Alvarellos, A.; Figuero, A.; Rodríguez-Yáñez, S.; Sande, J.; Peña, E.; Rosa-Santos, P.; Rabuñal, J. Deep Learning-Based Wave Overtopping Prediction. Appl. Sci. 2024, 14(6), 2611. https://doi.org/10.3390/app14062611
Abstract
[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
Machine learning
Neural networks
Deep learning
Wave overtopping prediction
Port management
Port security
Neural networks
Deep learning
Wave overtopping prediction
Port management
Port security
Editor version
Rights
Atribución 4.0 Internacional © 2024 the authors
ISSN
2076-3417