A Novel Method on Hydrographic Survey Technology Selection Based on the Decision Tree Supervised Learning

A Novel Method on Hydrographic Survey Technology Selection Based on the Decision Tree Supervised Learning

Hydrographic survey or seabed mapping plays an important role in achieving better maritime safety, especially in coastal waters. Due to advances in survey technologies, it becomes important to choose well-suited technology for a specific area. Moreover, various technologies have various ranges of eq...

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Main Authors: Ivana Golub Medvešek, Igor Vujović, Joško Šoda, Maja Krčum
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Applied Sciences
Subjects:
supervised learning
decision tree
hydrographic survey
weighted sum model
Online Access:https://www.mdpi.com/2076-3417/11/11/4966
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spelling doaj-5bd194d9548b41c0891e5f72fd48a38f2021-06-01T01:26:21ZengMDPI AGApplied Sciences2076-34172021-05-01114966496610.3390/app11114966A Novel Method on Hydrographic Survey Technology Selection Based on the Decision Tree Supervised LearningIvana Golub Medvešek0Igor Vujović1Joško Šoda2Maja Krčum3Faculty of Maritime Studies, University of Split, Ruđera Boškovića 37, 21000 Split, CroatiaFaculty of Maritime Studies, University of Split, Ruđera Boškovića 37, 21000 Split, CroatiaFaculty of Maritime Studies, University of Split, Ruđera Boškovića 37, 21000 Split, CroatiaFaculty of Maritime Studies, University of Split, Ruđera Boškovića 37, 21000 Split, CroatiaHydrographic survey or seabed mapping plays an important role in achieving better maritime safety, especially in coastal waters. Due to advances in survey technologies, it becomes important to choose well-suited technology for a specific area. Moreover, various technologies have various ranges of equipment and manufacturers, as well as characteristics. Therefore, in this paper, a novel method of a hydrographic survey, i.e., identifying the appropriate technology, has been developed. The method is based on a reduced elimination matrix, decision tree supervised learning, and multicriteria decision methods. The available technologies were: remotely operated underwater vehicle (ROV), unmanned aerial vehicle (UAV), light detection and ranging (LIDAR), autonomous underwater vehicle (AUV), satellite-derived bathymetry (SDB), and multibeam echosounder (MBES), and they are applied as a case study of Kaštela Bay. Results show, considering the specifics of the survey area, that UAV is the best-suited technology to be used for a hydrographic survey. However, some other technologies, such as SDB come close and can be considered an alternative for hydrographic surveys.https://www.mdpi.com/2076-3417/11/11/4966supervised learningdecision treehydrographic surveyweighted sum model
collection DOAJ
language English
format Article
sources DOAJ
author Ivana Golub Medvešek
Igor Vujović
Joško Šoda
Maja Krčum
spellingShingle Ivana Golub Medvešek
Igor Vujović
Joško Šoda
Maja Krčum
A Novel Method on Hydrographic Survey Technology Selection Based on the Decision Tree Supervised Learning
Applied Sciences
supervised learning
decision tree
hydrographic survey
weighted sum model
author_facet Ivana Golub Medvešek
Igor Vujović
Joško Šoda
Maja Krčum
author_sort Ivana Golub Medvešek
title A Novel Method on Hydrographic Survey Technology Selection Based on the Decision Tree Supervised Learning
title_short A Novel Method on Hydrographic Survey Technology Selection Based on the Decision Tree Supervised Learning
title_full A Novel Method on Hydrographic Survey Technology Selection Based on the Decision Tree Supervised Learning
title_fullStr A Novel Method on Hydrographic Survey Technology Selection Based on the Decision Tree Supervised Learning
title_full_unstemmed A Novel Method on Hydrographic Survey Technology Selection Based on the Decision Tree Supervised Learning
title_sort novel method on hydrographic survey technology selection based on the decision tree supervised learning
publisher MDPI AG
series Applied Sciences
issn 2076-3417
publishDate 2021-05-01
description Hydrographic survey or seabed mapping plays an important role in achieving better maritime safety, especially in coastal waters. Due to advances in survey technologies, it becomes important to choose well-suited technology for a specific area. Moreover, various technologies have various ranges of equipment and manufacturers, as well as characteristics. Therefore, in this paper, a novel method of a hydrographic survey, i.e., identifying the appropriate technology, has been developed. The method is based on a reduced elimination matrix, decision tree supervised learning, and multicriteria decision methods. The available technologies were: remotely operated underwater vehicle (ROV), unmanned aerial vehicle (UAV), light detection and ranging (LIDAR), autonomous underwater vehicle (AUV), satellite-derived bathymetry (SDB), and multibeam echosounder (MBES), and they are applied as a case study of Kaštela Bay. Results show, considering the specifics of the survey area, that UAV is the best-suited technology to be used for a hydrographic survey. However, some other technologies, such as SDB come close and can be considered an alternative for hydrographic surveys.
topic supervised learning
decision tree
hydrographic survey
weighted sum model
url https://www.mdpi.com/2076-3417/11/11/4966
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