Lan He, Wang Shutian, Zhang Wenfeng
School of Economics and Management, Dalian Ocean University, Dalian, 116023, China.
Heliyon. 2024 Apr 26;10(9):e30046. doi: 10.1016/j.heliyon.2024.e30046. eCollection 2024 May 15.
Maritime accidents frequently lead to severe property damage and casualties, and an accurate and reliable risk prediction model is necessary to help maritime stakeholders assess the current risk situation. Therefore, the present study proposes a hybrid methodology to develop an explainable prediction model for maritime accident types. Based on the advantages of selective ensemble learning method, this study pioneers to introduce a two-stage model selection method, aiming to enhance the predictive accuracy and stability of the model. Then, SHAP (Shapley Additive Explanations) method is integrated to identify effective mapping associations of seafarers' unsafe acts and their risk factors with the prediction results. The results demonstrate that the model developed achieves good prediction performance with an accuracy of 87.50 % and an F1-score of 84.98 %, which benefits stakeholders in assessing the type of maritime accident in advance, so as to make proactive intervention measures.
海上事故经常导致严重的财产损失和人员伤亡，因此需要一个准确可靠的风险预测模型来帮助海事利益相关者评估当前的风险状况。为此，本研究提出了一种混合方法来开发一个可解释的海上事故类型预测模型。基于选择性集成学习方法的优势，本研究率先引入了两阶段模型选择方法，旨在提高模型的预测准确性和稳定性。然后，集成SHAP（Shapley Additive Explanations）方法来识别海员不安全行为及其风险因素与预测结果之间的有效映射关联。结果表明，所开发的模型具有良好的预测性能，准确率为87.50%，F1分数为84.98%，这有助于利益相关者提前评估海上事故类型，从而采取积极的干预措施。
