Integrating Multiple Datasets and Machine Learning Algorithms for Satellite-Based Bathymetry in Seaports

Water depth estimation in seaports is essential for effective port management. This paper presents an empirical approach for water depth determination from satellite imagery through the integration of multiple datasets and machine learning algorithms. The implementation details of the proposed appro...

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Autores principales:	Zhongqiang Wu, Zhihua Mao, Wen Shen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	ensemble learning classifier fusion support vector machine random forest multi-adaptive regression spline neural networks Science Q
Acceso en línea:	https://doaj.org/article/e087065784ae455c84f41c1e81bb50f0
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id	oai:doaj.org-article:e087065784ae455c84f41c1e81bb50f0
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spelling	oai:doaj.org-article:e087065784ae455c84f41c1e81bb50f02021-11-11T18:53:56ZIntegrating Multiple Datasets and Machine Learning Algorithms for Satellite-Based Bathymetry in Seaports10.3390/rs132143282072-4292https://doaj.org/article/e087065784ae455c84f41c1e81bb50f02021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4328https://doaj.org/toc/2072-4292Water depth estimation in seaports is essential for effective port management. This paper presents an empirical approach for water depth determination from satellite imagery through the integration of multiple datasets and machine learning algorithms. The implementation details of the proposed approach are provided and compared against different existing machine learning algorithms with a single training set. For a single training set and a single machine learning method, our analysis shows that the proposed depth estimation method provides a better root-mean-square error (RMSE) and a higher coefficient of determination (R<sup>2</sup>) under turbid water conditions, with overall RMSE and R<sup>2</sup> improvements of 1 cm and 0.7, respectively. The developed method may be employed in monitoring dredging activities, especially in areas with polluted water, mud and/or a high sediment content.Zhongqiang WuZhihua MaoWen ShenMDPI AGarticleensemble learningclassifier fusionsupport vector machinerandom forestmulti-adaptive regression splineneural networksScienceQENRemote Sensing, Vol 13, Iss 4328, p 4328 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ensemble learning classifier fusion support vector machine random forest multi-adaptive regression spline neural networks Science Q
spellingShingle	ensemble learning classifier fusion support vector machine random forest multi-adaptive regression spline neural networks Science Q Zhongqiang Wu Zhihua Mao Wen Shen Integrating Multiple Datasets and Machine Learning Algorithms for Satellite-Based Bathymetry in Seaports
description	Water depth estimation in seaports is essential for effective port management. This paper presents an empirical approach for water depth determination from satellite imagery through the integration of multiple datasets and machine learning algorithms. The implementation details of the proposed approach are provided and compared against different existing machine learning algorithms with a single training set. For a single training set and a single machine learning method, our analysis shows that the proposed depth estimation method provides a better root-mean-square error (RMSE) and a higher coefficient of determination (R<sup>2</sup>) under turbid water conditions, with overall RMSE and R<sup>2</sup> improvements of 1 cm and 0.7, respectively. The developed method may be employed in monitoring dredging activities, especially in areas with polluted water, mud and/or a high sediment content.
format	article
author	Zhongqiang Wu Zhihua Mao Wen Shen
author_facet	Zhongqiang Wu Zhihua Mao Wen Shen
author_sort	Zhongqiang Wu
title	Integrating Multiple Datasets and Machine Learning Algorithms for Satellite-Based Bathymetry in Seaports
title_short	Integrating Multiple Datasets and Machine Learning Algorithms for Satellite-Based Bathymetry in Seaports
title_full	Integrating Multiple Datasets and Machine Learning Algorithms for Satellite-Based Bathymetry in Seaports
title_fullStr	Integrating Multiple Datasets and Machine Learning Algorithms for Satellite-Based Bathymetry in Seaports
title_full_unstemmed	Integrating Multiple Datasets and Machine Learning Algorithms for Satellite-Based Bathymetry in Seaports
title_sort	integrating multiple datasets and machine learning algorithms for satellite-based bathymetry in seaports
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e087065784ae455c84f41c1e81bb50f0
work_keys_str_mv	AT zhongqiangwu integratingmultipledatasetsandmachinelearningalgorithmsforsatellitebasedbathymetryinseaports AT zhihuamao integratingmultipledatasetsandmachinelearningalgorithmsforsatellitebasedbathymetryinseaports AT wenshen integratingmultipledatasetsandmachinelearningalgorithmsforsatellitebasedbathymetryinseaports
_version_	1718431735922819072

Integrating Multiple Datasets and Machine Learning Algorithms for Satellite-Based Bathymetry in Seaports

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