| Item type |
デフォルト(1) |
| 公開日 |
2025-06-17 |
| タイトル |
|
|
タイトル |
Development of marine accident probability prediction model for pleasure boats using ship accident database in central part of Seto Inland Sea |
|
言語 |
en |
| 作成者 |
Shintani, Aogi
Taniguchi, Naokazu
Nakayama, Yoshiyuki
Tanaka, Takahiro
Hamada, Kunihiro
|
| アクセス権 |
|
|
アクセス権 |
embargoed access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_f1cf |
| 権利情報 |
|
|
言語 |
en |
|
権利情報 |
© <2025>. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ |
| 権利情報 |
|
|
言語 |
en |
|
権利情報 |
This is not the published version. Please cite only the published version. |
| 権利情報 |
|
|
言語 |
ja |
|
権利情報 |
この論文は出版社版ではありません。引用の際には出版社版をご確認、ご利用ください。 |
| 主題 |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Marine accident |
| 主題 |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Pleasure boat |
| 主題 |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Prediction probability |
| 主題 |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Marine traffic safety |
| 主題 |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
LightGBM |
| 主題 |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
SHAP |
| 内容記述 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Understanding future marine accident risks is a critical challenge for maritime safety. Approximately 50% of marine accidents in Japan involve pleasure boats (PBs), small ships used for marine leisure. Previous studies have primarily analyzed the causes and trends of marine accidents involving larger vessels. Effective maritime safety management requires predicting future accidents and identifying conditions that contribute to these incidents. This study developed a predictive model using Light Gradient Boosting Machine to estimate the monthly probability of PB marine accidents in the central part of the Seto Inland Sea, Japan's highest incidence area. The model utilized 2986 PB marine accidents (2005–2021), sea area characteristics, vessel traffic density, and weather conditions to learn patterns of marine accident occurrence for each approximately 10 nautical mile square. The prediction accuracy indicated that the results generally aligned with the actual incident rates. Additionally, the Shapley Additive exPlanation, an interpretation method for artificial intelligence models, reveals that in areas with a high predicted probability, the number of islands and vessel traffic density were the main crucial factors. The results of this study can effectively aid port authorities and rescue organizations in optimizing the season and area selection for safety activities and rescue vessel deployment. |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
Elsevier |
|
言語 |
en |
| 日付 |
|
|
日付 |
2027-02-05 |
|
日付タイプ |
Available |
| 言語 |
|
|
言語 |
eng |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 出版タイプ |
|
|
出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| 関連情報 |
|
|
関連タイプ |
isVersionOf |
|
|
関連識別子 |
https://doi.org/10.1016/j.oceaneng.2025.120460 |
| 書誌情報 |
en : Ocean Engineering
号 322,
p. 120460,
ページ数 25,
発行日 2025-02-05
|
| 備考 |
|
|
言語 |
en |
|
値 |
The full-text file will be made open to the public on 5 February 2027 in accordance with publisher's 'Terms and Conditions for Self-Archiving' |
OE_322.pdf (2.5 MB)
