Assessing Ships’ Environmental Performance Using Machine Learning
Environmental performance of ships is a critical factor in the shipping industry due to evolving climate change and the respective regulations imposed by authorities all over the world. As shipping moves towards digitization, a large amount of ships’ environmental performance-related data, collected...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/6/2544 |
| _version_ | 1797612085955264512 |
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| author | Kyriakos Skarlatos Andreas Fousteris Dimitrios Georgakellos Polychronis Economou Sotirios Bersimis |
| author_facet | Kyriakos Skarlatos Andreas Fousteris Dimitrios Georgakellos Polychronis Economou Sotirios Bersimis |
| author_sort | Kyriakos Skarlatos |
| collection | DOAJ |
| description | Environmental performance of ships is a critical factor in the shipping industry due to evolving climate change and the respective regulations imposed by authorities all over the world. As shipping moves towards digitization, a large amount of ships’ environmental performance-related data, collected during ships’ voyages, provide opportunities to develop and enhance data-driven performance models by using different machine learning algorithms. This paper introduces new indices of ships’ environmental performance using machine learning techniques. The new indices are produced by combining clustering algorithms as well as principal component analysis. Based on the analysis of the data (14 variables with operational and design characteristics), the ships are divided into four clusters based on the new suggested indices. These clusters categorize the ships according to their physical dimensions, operating region, and operational environmental efficiency, offering insight into the distinctive traits of each cluster. |
| first_indexed | 2024-03-11T06:37:26Z |
| format | Article |
| id | doaj.art-070e41935d95413088771240ace5ec12 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T06:37:26Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-070e41935d95413088771240ace5ec122023-11-17T10:47:35ZengMDPI AGEnergies1996-10732023-03-01166254410.3390/en16062544Assessing Ships’ Environmental Performance Using Machine LearningKyriakos Skarlatos0Andreas Fousteris1Dimitrios Georgakellos2Polychronis Economou3Sotirios Bersimis4Department of Business Administration, University of Piraeus, 18534 Piraeus, GreeceDepartment of Business Administration, University of Piraeus, 18534 Piraeus, GreeceDepartment of Business Administration, University of Piraeus, 18534 Piraeus, GreeceDepartment of Civil Engineering, University of Patras, 26504 Patras, GreeceDepartment of Business Administration, University of Piraeus, 18534 Piraeus, GreeceEnvironmental performance of ships is a critical factor in the shipping industry due to evolving climate change and the respective regulations imposed by authorities all over the world. As shipping moves towards digitization, a large amount of ships’ environmental performance-related data, collected during ships’ voyages, provide opportunities to develop and enhance data-driven performance models by using different machine learning algorithms. This paper introduces new indices of ships’ environmental performance using machine learning techniques. The new indices are produced by combining clustering algorithms as well as principal component analysis. Based on the analysis of the data (14 variables with operational and design characteristics), the ships are divided into four clusters based on the new suggested indices. These clusters categorize the ships according to their physical dimensions, operating region, and operational environmental efficiency, offering insight into the distinctive traits of each cluster.https://www.mdpi.com/1996-1073/16/6/2544ship’s environmental performancemachine learning in shippingdata-driven environmental indicesshipping environmental categorization |
| spellingShingle | Kyriakos Skarlatos Andreas Fousteris Dimitrios Georgakellos Polychronis Economou Sotirios Bersimis Assessing Ships’ Environmental Performance Using Machine Learning Energies ship’s environmental performance machine learning in shipping data-driven environmental indices shipping environmental categorization |
| title | Assessing Ships’ Environmental Performance Using Machine Learning |
| title_full | Assessing Ships’ Environmental Performance Using Machine Learning |
| title_fullStr | Assessing Ships’ Environmental Performance Using Machine Learning |
| title_full_unstemmed | Assessing Ships’ Environmental Performance Using Machine Learning |
| title_short | Assessing Ships’ Environmental Performance Using Machine Learning |
| title_sort | assessing ships environmental performance using machine learning |
| topic | ship’s environmental performance machine learning in shipping data-driven environmental indices shipping environmental categorization |
| url | https://www.mdpi.com/1996-1073/16/6/2544 |
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