An ensemble method for investigating maritime casualties resulting in pollution occurrence: Data augmentation and feature analysis
•Prediction of maritime casualties resulting in pollution occurrence powered by AI technology.•Employment of VAE based data augmentation to address data imbalance challenge.•Utilization of up-to-date maritime casualty data with various pollution sources.•Analysis of contributing features and their d...
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| Published in: | Reliability engineering & system safety Vol. 251; p. 110391 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.11.2024
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| ISSN: | 0951-8320 |
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| Abstract | •Prediction of maritime casualties resulting in pollution occurrence powered by AI technology.•Employment of VAE based data augmentation to address data imbalance challenge.•Utilization of up-to-date maritime casualty data with various pollution sources.•Analysis of contributing features and their dependences on pollution occurrence.•Discussion of practical insights for precautionary measures and policy development.
Timely prediction of maritime casualties resulting in pollution occurrence remains unsolved in academia, as the significant data imbalance between non-polluting and polluting casualties poses a challenge to prediction efficacy. This study proposes an ensemble method for predicting polluting maritime casualties and exploring the contributing features to pollution. In the data preprocessing phase, key features related to casualties and vessels are extracted and encoded into model variables; in the data augmentation phase, Variational Autoencoder is employed to generate synthetic samples from the minor class, effectively mitigating the impact from data imbalance; and in the pollution indicator classification phase, machine learning models are trained on the balanced dataset to label a casualty as “polluting” or “non-polluting”. A dataset containing 25,414 worldwide maritime casualties from 2013 to 2023 is utilized for method validation. Several state-of-the-art data balancing techniques serve as baselines for comparison with the VAE on the quality of generated synthetic data. The model trained on the VAE dataset achieves the most satisfactory performances, demonstrating the superiority of VAE in augmenting data quantity and diversity. “Casualty cause”, “Vessel age” and “Vessel type” are revealed as the top three contributing features to pollution. Several insights are discussed for precautionary measures and policy development. |
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| AbstractList | •Prediction of maritime casualties resulting in pollution occurrence powered by AI technology.•Employment of VAE based data augmentation to address data imbalance challenge.•Utilization of up-to-date maritime casualty data with various pollution sources.•Analysis of contributing features and their dependences on pollution occurrence.•Discussion of practical insights for precautionary measures and policy development.
Timely prediction of maritime casualties resulting in pollution occurrence remains unsolved in academia, as the significant data imbalance between non-polluting and polluting casualties poses a challenge to prediction efficacy. This study proposes an ensemble method for predicting polluting maritime casualties and exploring the contributing features to pollution. In the data preprocessing phase, key features related to casualties and vessels are extracted and encoded into model variables; in the data augmentation phase, Variational Autoencoder is employed to generate synthetic samples from the minor class, effectively mitigating the impact from data imbalance; and in the pollution indicator classification phase, machine learning models are trained on the balanced dataset to label a casualty as “polluting” or “non-polluting”. A dataset containing 25,414 worldwide maritime casualties from 2013 to 2023 is utilized for method validation. Several state-of-the-art data balancing techniques serve as baselines for comparison with the VAE on the quality of generated synthetic data. The model trained on the VAE dataset achieves the most satisfactory performances, demonstrating the superiority of VAE in augmenting data quantity and diversity. “Casualty cause”, “Vessel age” and “Vessel type” are revealed as the top three contributing features to pollution. Several insights are discussed for precautionary measures and policy development. |
| ArticleNumber | 110391 |
| Author | Yuen, Kum Fai Chen, Tianyi Wong, Yiik Diew Li, Duowei Wang, Nanxi |
| Author_xml | – sequence: 1 givenname: Duowei orcidid: 0000-0002-1940-2435 surname: Li fullname: Li, Duowei organization: School of Civil and Environmental Engineering, Nanyang Technological University, Singapore – sequence: 2 givenname: Yiik Diew orcidid: 0000-0001-7419-5777 surname: Wong fullname: Wong, Yiik Diew organization: School of Civil and Environmental Engineering, Nanyang Technological University, Singapore – sequence: 3 givenname: Tianyi surname: Chen fullname: Chen, Tianyi organization: School of Civil and Environmental Engineering, Nanyang Technological University, Singapore – sequence: 4 givenname: Nanxi surname: Wang fullname: Wang, Nanxi organization: School of Civil and Environmental Engineering, Nanyang Technological University, Singapore – sequence: 5 givenname: Kum Fai orcidid: 0000-0002-9199-6661 surname: Yuen fullname: Yuen, Kum Fai email: kumfai.yuen@ntu.edu.sg organization: School of Civil and Environmental Engineering, Nanyang Technological University, Singapore |
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| Keywords | Maritime casualty Variational autoencoder (VAE) Data augmentation Maritime pollution Pollution prediction Machine learning |
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