Effective maritime accident prediction will benefit both maritime safety management and the insurance industry. Due to the complex non-linearity and non-stationarity nature of maritime accident data, its prediction is still a challenge in the research field. An autoregressive integrated moving average with explanatory variables (ARIMAX) model was proposed to predict maritime accidents accurately, and a multi-factor accident prediction framework was developed. Additionally, the impacts of eight influencing factors on the number of maritime accidents were also investigated, and the predictions from the ARIMAX model were contrasted with those from earlier maritime accident prediction models, as well as autoregressive integrated moving average (ARIMA), back-propagation neural network (BPNN), and support vector regression (SVR). The findings imply that an increase in any one of the eight factors may increase the number of maritime accidents worldwide. The ARIMAX model, which incorporates accident factors, is accurate enough to estimate the number of global maritime accidents and outperforms the ARIMA, BPNN, and SVR models in terms of prediction precision and robustness. The ARIMAX model outperforms earlier marine accident prediction models and has good applicability.

有效的海上事故预测将有利于海上安全管理和保险业。由于海上事故数据具有复杂的非线性和非平稳性，其预测仍然是研究领域的一个挑战。提出了一种具有解释变量的自回归综合移动平均（ARIMAX）模型来准确预测海上事故，并开发了多因素事故预测框架。此外，还研究了八个影响因素对海事事故数量的影响，并将 ARIMAX 模型的预测与早期海事事故预测模型以及自回归积分移动平均（ARIMA）、反向传播模型的预测进行了对比神经网络 (BPNN) 和支持向量回归 (SVR)。调查结果表明，八个因素中任何一个的增加都可能增加全球海上事故的数量。ARIMAX 模型加入了事故因素，能够准确估计全球海上事故的数量，在预测精度和鲁棒性方面优于 ARIMA、BPNN 和 SVR 模型。ARIMAX 模型优于早期的海上事故预测模型，具有良好的适用性。