Data-Driven Analysis of Causes and Risk Assessment of Marine Container Losses: Development of a Predictive Model Using Machine Learning and Statistical Approaches
This study presents a comprehensive, data-driven analysis of the causes and risks associated with container loss during maritime transport, utilizing incident data from 2011 to 2023. By employing advanced statistical analysis, machine-learning techniques, and data preprocessing, the study identifies...
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| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/3/420 |
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| author | Myung-Su Yi Byung-Keun Lee Joo-Shin Park |
| author_facet | Myung-Su Yi Byung-Keun Lee Joo-Shin Park |
| author_sort | Myung-Su Yi |
| collection | DOAJ |
| description | This study presents a comprehensive, data-driven analysis of the causes and risks associated with container loss during maritime transport, utilizing incident data from 2011 to 2023. By employing advanced statistical analysis, machine-learning techniques, and data preprocessing, the study identifies key factors influencing container loss, including vessel size, incident locations, and primary causes. A predictive model based on decision trees was developed to assess the severity of container loss incidents, while K-means clustering was used to classify incident zones. Adverse weather conditions were found to be the predominant cause, accounting for 57.14% of incidents. The study reveals that larger vessels, despite experiencing fewer incidents, face more severe losses, whereas smaller vessels are more prone to frequent but less severe losses. The decision-tree model demonstrated high accuracy in predicting low-risk incidents but showed limitations in moderate- and high-risk scenarios. The findings underscore the importance of understanding the correlation between vessel parameters and incident outcomes to enhance risk management strategies. The study also highlights the potential for improving predictive capabilities by incorporating environmental data. These insights provide a robust framework for ship owners and maritime authorities to anticipate and mitigate risks, emphasizing the need for continuous monitoring and enhanced safety measures in maritime operations. |
| format | Article |
| id | doaj-art-314c2a44b7e84ba68256981341ea286a |
| institution | DOAJ |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-314c2a44b7e84ba68256981341ea286a2025-08-20T02:42:34ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-02-0113342010.3390/jmse13030420Data-Driven Analysis of Causes and Risk Assessment of Marine Container Losses: Development of a Predictive Model Using Machine Learning and Statistical ApproachesMyung-Su Yi0Byung-Keun Lee1Joo-Shin Park2Department of Naval Architecture and Ocean Engineering, Chosun University, Gwangju 61452, Republic of KoreaShip & Offshore Research Institute, Samsung Heavy Industries Co., Ltd., Geoje 53261, Republic of KoreaShip & Offshore Research Institute, Samsung Heavy Industries Co., Ltd., Geoje 53261, Republic of KoreaThis study presents a comprehensive, data-driven analysis of the causes and risks associated with container loss during maritime transport, utilizing incident data from 2011 to 2023. By employing advanced statistical analysis, machine-learning techniques, and data preprocessing, the study identifies key factors influencing container loss, including vessel size, incident locations, and primary causes. A predictive model based on decision trees was developed to assess the severity of container loss incidents, while K-means clustering was used to classify incident zones. Adverse weather conditions were found to be the predominant cause, accounting for 57.14% of incidents. The study reveals that larger vessels, despite experiencing fewer incidents, face more severe losses, whereas smaller vessels are more prone to frequent but less severe losses. The decision-tree model demonstrated high accuracy in predicting low-risk incidents but showed limitations in moderate- and high-risk scenarios. The findings underscore the importance of understanding the correlation between vessel parameters and incident outcomes to enhance risk management strategies. The study also highlights the potential for improving predictive capabilities by incorporating environmental data. These insights provide a robust framework for ship owners and maritime authorities to anticipate and mitigate risks, emphasizing the need for continuous monitoring and enhanced safety measures in maritime operations.https://www.mdpi.com/2077-1312/13/3/420container lossincident datarisk assessmentclustering analysisstatistical analysis |
| spellingShingle | Myung-Su Yi Byung-Keun Lee Joo-Shin Park Data-Driven Analysis of Causes and Risk Assessment of Marine Container Losses: Development of a Predictive Model Using Machine Learning and Statistical Approaches Journal of Marine Science and Engineering container loss incident data risk assessment clustering analysis statistical analysis |
| title | Data-Driven Analysis of Causes and Risk Assessment of Marine Container Losses: Development of a Predictive Model Using Machine Learning and Statistical Approaches |
| title_full | Data-Driven Analysis of Causes and Risk Assessment of Marine Container Losses: Development of a Predictive Model Using Machine Learning and Statistical Approaches |
| title_fullStr | Data-Driven Analysis of Causes and Risk Assessment of Marine Container Losses: Development of a Predictive Model Using Machine Learning and Statistical Approaches |
| title_full_unstemmed | Data-Driven Analysis of Causes and Risk Assessment of Marine Container Losses: Development of a Predictive Model Using Machine Learning and Statistical Approaches |
| title_short | Data-Driven Analysis of Causes and Risk Assessment of Marine Container Losses: Development of a Predictive Model Using Machine Learning and Statistical Approaches |
| title_sort | data driven analysis of causes and risk assessment of marine container losses development of a predictive model using machine learning and statistical approaches |
| topic | container loss incident data risk assessment clustering analysis statistical analysis |
| url | https://www.mdpi.com/2077-1312/13/3/420 |
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