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Article

Determination of Water Depth in Ports Using Satellite Data Based on Machine Learning Algorithms

1
Project Engineering Department, University of Oviedo, 33004 Oviedo, Spain
2
Department of Mechanical Engineering, University of La Rioja, 26004 Logroño, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Sandro Nizetic, Gwanggil Jeon and William Holderbaum
Energies 2021, 14(9), 2486; https://doi.org/10.3390/en14092486
Received: 3 March 2021 / Revised: 20 April 2021 / Accepted: 25 April 2021 / Published: 27 April 2021
One of the fundamental maintenance tasks of ports is the periodic dredging of them. This is necessary to guarantee a minimum draft that will enable ships to access ports safely. The determination of bathymetries is the instrument that determines the need for dredging and permits an analysis of the behavior of the port bottom over time, in order to achieve adequate water depth. Satellite data processing to predict environmental parameters is used increasingly. Based on satellite data and using different machine learning algorithm techniques, this study has sought to estimate the seabed in ports, taking into account the fact that the port areas are strongly anthropized areas. The algorithms that were used were Support Vector Machine (SVM), Random Forest (RF) and the Multi-Adaptive Regression Splines (MARS). The study was carried out in the ports of Candás and Luarca in the Principality of Asturias. In order to validate the results obtained, data was acquired in situ by using a single beam provided. The results show that this type of methodology can be used to estimate coastal bathymetry. However, when deciding which system was best, priority was given to simplicity and robustness. The results of the SVM and RF algorithms outperform those of the MARS. RF performs better in Candás with a mean absolute error (MAE) of 0.27 cm, whereas SVM performs better in Luarca with a mean absolute error of 0.37 cm. It is suggested that this approach is suitable as a simpler and more cost-effective rough resolution alternative, for estimating the depth of turbid water in ports, than single-beam sonar, which is labor-intensive and polluting. View Full-Text
Keywords: environmental monitoring; satellite remote sensing; Sentinel-2; SVM; RF; MARS; polluted water; bathymetry environmental monitoring; satellite remote sensing; Sentinel-2; SVM; RF; MARS; polluted water; bathymetry
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MDPI and ACS Style

Mateo-Pérez, V.; Corral-Bobadilla, M.; Ortega-Fernández, F.; Rodríguez-Montequín, V. Determination of Water Depth in Ports Using Satellite Data Based on Machine Learning Algorithms. Energies 2021, 14, 2486. https://doi.org/10.3390/en14092486

AMA Style

Mateo-Pérez V, Corral-Bobadilla M, Ortega-Fernández F, Rodríguez-Montequín V. Determination of Water Depth in Ports Using Satellite Data Based on Machine Learning Algorithms. Energies. 2021; 14(9):2486. https://doi.org/10.3390/en14092486

Chicago/Turabian Style

Mateo-Pérez, Vanesa, Marina Corral-Bobadilla, Francisco Ortega-Fernández, and Vicente Rodríguez-Montequín. 2021. "Determination of Water Depth in Ports Using Satellite Data Based on Machine Learning Algorithms" Energies 14, no. 9: 2486. https://doi.org/10.3390/en14092486

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