Exploring Key Factors for Long-Term Vessel Incident Risk Prediction
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914991519563776 |
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| author | Chen, Tianyi Wang, Hua Cai, Yutong Liang, Maohan Meng, Qiang |
| author_facet | Chen, Tianyi Wang, Hua Cai, Yutong Liang, Maohan Meng, Qiang |
| contents | Factor analysis acts a pivotal role in enhancing maritime safety. Most previous studies conduct factor analysis within the framework of incident-related label prediction, where the developed models can be categorized into short-term and long-term prediction models. The long-term models offer a more strategic approach, enabling more proactive risk management, compared to the short-term ones. Nevertheless, few studies have devoted to rigorously identifying the key factors for the long-term prediction and undertaking comprehensive factor analysis. Hence, this study aims to delve into the key factors for predicting the incident risk levels in the subsequent year given a specific datestamp. The majority of candidate factors potentially contributing to the incident risk are collected from vessels' historical safety performance data spanning up to five years. An improved embedded feature selection, which integrates Random Forest classifier with a feature filtering process is proposed to identify key risk-contributing factors from the candidate pool. The results demonstrate superior performance of the proposed method in incident prediction and factor interpretability. Comprehensive analysis is conducted upon the key factors, which could help maritime stakeholders formulate management strategies for incident prevenion. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_19804 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Key Factors for Long-Term Vessel Incident Risk Prediction Chen, Tianyi Wang, Hua Cai, Yutong Liang, Maohan Meng, Qiang Machine Learning Factor analysis acts a pivotal role in enhancing maritime safety. Most previous studies conduct factor analysis within the framework of incident-related label prediction, where the developed models can be categorized into short-term and long-term prediction models. The long-term models offer a more strategic approach, enabling more proactive risk management, compared to the short-term ones. Nevertheless, few studies have devoted to rigorously identifying the key factors for the long-term prediction and undertaking comprehensive factor analysis. Hence, this study aims to delve into the key factors for predicting the incident risk levels in the subsequent year given a specific datestamp. The majority of candidate factors potentially contributing to the incident risk are collected from vessels' historical safety performance data spanning up to five years. An improved embedded feature selection, which integrates Random Forest classifier with a feature filtering process is proposed to identify key risk-contributing factors from the candidate pool. The results demonstrate superior performance of the proposed method in incident prediction and factor interpretability. Comprehensive analysis is conducted upon the key factors, which could help maritime stakeholders formulate management strategies for incident prevenion. |
| title | Exploring Key Factors for Long-Term Vessel Incident Risk Prediction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.19804 |