Machine Learning-Based Models for Accident Prediction at a Korean Container Port
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Jae Hun | - |
dc.contributor.author | Kim, Juyeon | - |
dc.contributor.author | Lee, Gunwoo | - |
dc.contributor.author | Park, June young | - |
dc.date.accessioned | 2022-10-25T06:43:45Z | - |
dc.date.available | 2022-10-25T06:43:45Z | - |
dc.date.created | 2022-10-11 | - |
dc.date.issued | 2021-08 | - |
dc.identifier.issn | 2071-1050 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/111119 | - |
dc.description.abstract | The occurrence of accidents at container ports results in damages and economic losses in the terminal operation. Therefore, it is necessary to accurately predict accidents at container ports. Several machine learning models have been applied to predict accidents at a container port under various time intervals, and the optimal model was selected by comparing the results of different models in terms of their accuracy, precision, recall, and F1 score. The results show that a deep neural network model and gradient boosting model with an interval of 6 h exhibits the highest performance in terms of all the performance metrics. The applied methods can be used in the predicting of accidents at container ports in the future. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI Open Access Publishing | - |
dc.title | Machine Learning-Based Models for Accident Prediction at a Korean Container Port | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Gunwoo | - |
dc.contributor.affiliatedAuthor | Park, June young | - |
dc.identifier.doi | 10.3390/su13169137 | - |
dc.identifier.scopusid | 2-s2.0-85113308674 | - |
dc.identifier.wosid | 000690044400001 | - |
dc.identifier.bibliographicCitation | Sustainability, v.13, no.16, pp.1 - 14 | - |
dc.relation.isPartOf | Sustainability | - |
dc.citation.title | Sustainability | - |
dc.citation.volume | 13 | - |
dc.citation.number | 16 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 14 | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | ssci | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalResearchArea | Environmental Sciences & Ecology | - |
dc.relation.journalWebOfScienceCategory | Green & Sustainable Science & Technology | - |
dc.relation.journalWebOfScienceCategory | Environmental Sciences | - |
dc.relation.journalWebOfScienceCategory | Environmental Studies | - |
dc.subject.keywordPlus | NEURAL-NETWORK | - |
dc.subject.keywordPlus | RISK | - |
dc.subject.keywordAuthor | container port | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | accident prediction model | - |
dc.subject.keywordAuthor | neural network | - |
dc.subject.keywordAuthor | random forestㅣgradient boosting | - |
dc.identifier.url | https://www.mdpi.com/2071-1050/13/16/9137 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
55 Hanyangdeahak-ro, Sangnok-gu, Ansan, Gyeonggi-do, 15588, Korea+82-31-400-4269 sweetbrain@hanyang.ac.kr
COPYRIGHT © 2021 HANYANG UNIVERSITY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.