文献类型：期刊文献

英文题名：Maritime-Accident-Induced Environmental Pollution and Economic Loss Analysis Using an Interpretable Data-Driven Method

作者：Zhang, Zeguo[1,2];Hu, Qinyou[2,3];Yin, Jianchuan[1]

机构：[1]Guangdong Ocean Univ, Naval Architecture & Shipping Coll, Zhanjiang 524088, Peoples R China;[2]Shanghai Maritime Univ, Key Lab Transport Ind Marine Technol & Control Eng, Shanghai 200210, Peoples R China;[3]Shanghai Maritime Univ, Merchant Marine Coll, Shanghai 201306, Peoples R China

年份：2025

卷号：17

期号：7

外文期刊名：SUSTAINABILITY

收录：SSCI(收录号：WOS:001465749900001)、SCI-EXPANDED(收录号：WOS:001465749900001)、、Scopus(收录号：2-s2.0-105002308156)、WOS

基金：This research was funded by the National Natural Science Foundation of China under Grants 52271361 and 52231014, the Special Projects of Key Areas for Colleges and Universities in Guangdong Province under Grant 2021ZDZX1008, the Natural Science Foundation of Guangdong Province of China under Grant 2023A1515010684, the Educational and Teaching Reform Project of Guangdong Ocean University under Grant PX-972024013, the Technology breakthrough plan project of Zhanjiang under Grant 2023B01024, and the Open Funded Research Project of Key Laboratory of Transport Industry of Marine Technology and Control Engineering, Shanghai Maritime University.

语种：英文

外文关键词：maritime safety; risk factor; interpretable machine learning; environmental and economic-loss analysis

外文摘要：In this study, we developed an interpretable machine learning (ML) framework to predict marine pollution and economic losses from accident risk factors. A triple-feature selection process identified key predictors, followed by a comparative analysis of eight ML models. Random forest outperformed other models in forecasting environmental and property damage. The interpretable model was established based on the SHAP value framework, which revealed that onboard personnel count, vessel dimensions (length), and accident/ship types account for the risk factors with the most severe consequences, with environmental conditions like wind speed and air temperature contributing secondary effects. The methodology enables two critical applications: (1) environmental agencies can proactively assess accident impact through the identified risk triggers, optimizing emergency response planning, and (2) insurance providers gain data-driven risk evaluation metrics to refine premium calculations. By quantifying how human/technical factors, including crew members and vessel specifications, dominate over natural variables in accident effects, this data-driven approach provides actionable insights for maritime safety management and financial risk mitigation, achieving high prediction accuracy while maintaining model transparency through Shapley value explanations.