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

Vanesa Mateo-Pérez,Vicente Rodríguez-Montequín,Francisco Ortega-Fernández,Marina Corral-Bobadilla

doi:10.3390/en14092486

Vanesa Mateo-Pérez, Vicente Rodríguez-Montequín + Show 2 more

Open Access

https://doi.org/10.3390/en14092486

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Journal: Energies	Publication Date: Apr 27, 2021
Citations: 10	License type: CC BY 4.0

Affiliation: University of Oviedo, University of La Rioja

Abstract

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.

Highlights

The bathymetry of coastal zones is important for many applications
Three statistical metrics were used to compare the accuracies of the Support Vector Machine (SVM), Random Forest (RF) and Multi-Adaptive Regression Splines (MARS) models
No study previously has compared the use of SVM, RF, and MARS to study bathymetric mapping for the determination of depth in anthropized water areas, including ports that experience contamination and processes of accretion

Summary

Introduction

The bathymetry of coastal zones is important for many applications. These include navigation, infrastructure maintenance, dredging planning, managing the environment, hydrographic applications and coastal engineering sciences [1,2,3,4]. Management of and planning for these areas of endeavor require updated and accurate information. This requires efficient technologies to record these never-ending changes. Most ports need maintenance dredging at some point to improve and facilitate navigation and for the development and maintenance of infrastructures in the marine and fluvial environment [7,8]. In order to minimize unnecessary or excessive dredging and the associated expense [9], it is important to determine and model seafloor levels accurately

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