Prediction of the severity of marine accidents using improved machine learning
提出一种新的研究框架，通过两阶段特征选择方法和六种机器学习模型，准确预测海上事故的严重程度，并发现LightGBM模型表现最佳，可用于安全评估和事故预防。
Although many studies have focused on the occurrence likelihood of marine accidents, few have focused on the analysis of the severity of the consequences, and even fewer on the prediction of the severity. To this end, a new research framework is proposed in this study to accurately predict the severity of marine accidents. First, a novel two-stage feature selection (FS) method was developed to select and rank Risk Influential Factors (RIFs) to improve the accuracy of the Machine Learning (ML) model and interpretability of the FS. Second, a comprehensive evaluation method is proposed to measure the performance of the FS methods based on stability, predictive performance improvement, and statistical tests. Third, six well-established ML models were used and compared to measure the performance of different predictors. The Light Gradient Boosting Machine (LightGBM) was found to have the best predictive performance for the severity prediction of marine accidents and was treated as the benchmark model. Finally, LightGBM was used to predict accident severity based on the RIFs selected by the proposed FS method, and the effect of risk control measures was counterfactually analysed from a quantitative perspective. This innovative study on the use of improved ML approaches can effectively analyse and predict the severity of marine accidents, providing a novel methodology for and triggering a new direction for using Artificial Intelligence (AI) technologies in safety assessment and accident prevention studies. The source code is publicly available at: https://github.com/FengYinLeo/PGI-SDMI.