Machine Learning for Promoting Environmental Sustainability in Ports
Maritime transportation is one of the essential drivers of the global economy as it enables both lower transportation costs and intermodal operations across multiple forms of transportation. Maritime ports are essential interfaces that support cargo handling between sea and hinterland transportation...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2023/2144733 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832551190214213632 |
|---|---|
| author | Meead Mansoursamaei Mahmoud Moradi Rosa G. González-Ramírez Eduardo Lalla-Ruiz |
| author_facet | Meead Mansoursamaei Mahmoud Moradi Rosa G. González-Ramírez Eduardo Lalla-Ruiz |
| author_sort | Meead Mansoursamaei |
| collection | DOAJ |
| description | Maritime transportation is one of the essential drivers of the global economy as it enables both lower transportation costs and intermodal operations across multiple forms of transportation. Maritime ports are essential interfaces that support cargo handling between sea and hinterland transportation. Besides, in this area, environmental protection is becoming extremely important. Global warming, air pollution, and greenhouse gas emissions are all having a detrimental influence on the environment and will most likely continue to do so for future generations. Hence, there is a growing need to promote environmental sustainability in maritime-based transportation. The application of machine learning (ML), as one of the main subdomains of artificial intelligence (AI), can be considered a component within the process of digital transformation to advance green activities in maritime port logistics. Thus, this article presents the results of a systematic literature review of the recent literature on machine learning for promoting environmentally sustainable maritime ports. It collects and analyses the articles whose contributions lie in the interplay between three main dimensions, i.e., machine learning, port-related operations, and environmental sustainability. Throughout a review protocol, this research is constituted on the major focuses of impact, problems, and techniques to discern the current state of the art as well as research directions. The research findings indicate that the articles using polynomial regression models are dominant in the literature, and the recurrent neural network (RNN) and long short-term memory (LSTM) are the most recent approaches. Moreover, in terms of environmental sustainability, emissions and energy consumption are the most studied problems. mAccording to the research gaps observed in the review, two broad directions for future research are identified: (i) altering attention on a greater diversity of machine learning approaches for promoting environmental sustainability in ports and (ii) leveraging new outlooks to perform more green practical works on port-related operations. |
| format | Article |
| id | doaj-art-6624559adc3f480ebd3a6cd98bd93c51 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-6624559adc3f480ebd3a6cd98bd93c512025-02-03T06:04:40ZengWileyJournal of Advanced Transportation2042-31952023-01-01202310.1155/2023/2144733Machine Learning for Promoting Environmental Sustainability in PortsMeead Mansoursamaei0Mahmoud Moradi1Rosa G. González-Ramírez2Eduardo Lalla-Ruiz3University of GuilanUniversity of GuilanFacultad de Ingeniería y Ciencias AplicadasUniversity of TwenteMaritime transportation is one of the essential drivers of the global economy as it enables both lower transportation costs and intermodal operations across multiple forms of transportation. Maritime ports are essential interfaces that support cargo handling between sea and hinterland transportation. Besides, in this area, environmental protection is becoming extremely important. Global warming, air pollution, and greenhouse gas emissions are all having a detrimental influence on the environment and will most likely continue to do so for future generations. Hence, there is a growing need to promote environmental sustainability in maritime-based transportation. The application of machine learning (ML), as one of the main subdomains of artificial intelligence (AI), can be considered a component within the process of digital transformation to advance green activities in maritime port logistics. Thus, this article presents the results of a systematic literature review of the recent literature on machine learning for promoting environmentally sustainable maritime ports. It collects and analyses the articles whose contributions lie in the interplay between three main dimensions, i.e., machine learning, port-related operations, and environmental sustainability. Throughout a review protocol, this research is constituted on the major focuses of impact, problems, and techniques to discern the current state of the art as well as research directions. The research findings indicate that the articles using polynomial regression models are dominant in the literature, and the recurrent neural network (RNN) and long short-term memory (LSTM) are the most recent approaches. Moreover, in terms of environmental sustainability, emissions and energy consumption are the most studied problems. mAccording to the research gaps observed in the review, two broad directions for future research are identified: (i) altering attention on a greater diversity of machine learning approaches for promoting environmental sustainability in ports and (ii) leveraging new outlooks to perform more green practical works on port-related operations.http://dx.doi.org/10.1155/2023/2144733 |
| spellingShingle | Meead Mansoursamaei Mahmoud Moradi Rosa G. González-Ramírez Eduardo Lalla-Ruiz Machine Learning for Promoting Environmental Sustainability in Ports Journal of Advanced Transportation |
| title | Machine Learning for Promoting Environmental Sustainability in Ports |
| title_full | Machine Learning for Promoting Environmental Sustainability in Ports |
| title_fullStr | Machine Learning for Promoting Environmental Sustainability in Ports |
| title_full_unstemmed | Machine Learning for Promoting Environmental Sustainability in Ports |
| title_short | Machine Learning for Promoting Environmental Sustainability in Ports |
| title_sort | machine learning for promoting environmental sustainability in ports |
| url | http://dx.doi.org/10.1155/2023/2144733 |
| work_keys_str_mv | AT meeadmansoursamaei machinelearningforpromotingenvironmentalsustainabilityinports AT mahmoudmoradi machinelearningforpromotingenvironmentalsustainabilityinports AT rosaggonzalezramirez machinelearningforpromotingenvironmentalsustainabilityinports AT eduardolallaruiz machinelearningforpromotingenvironmentalsustainabilityinports |