Society is concerned about maritime accidents since pollution, such as oil spills from ship accidents, adversely affects the marine environment. Operational and strategic pollution preparedness and response risk management are essential activities to mitigate such adverse impacts. Quantitative risk models and decision support systems (DSS) have been proposed to support these risk management activities. However, there currently is a lack of computationally fast and accurate models to estimate oil spill consequences. While resource-intensive simulation models are available to make accurate predictions, these are slow and cannot easily be integrated into quantitative risk models or DSS. Hence, there is a need to develop solutions to accelerate the computational process. A fast and accurate metamodel is developed in this work to predict damage and oil outflow in tanker collision accidents. To achieve this, multiobjective optimization is applied to three metamodeling approaches: Deep Neural Network, Polynomial Regression, and Gradient Boosting Regression Tree. The data used in these learning algorithms are generated using state-of-the-art engineering models for accidental damage and oil outflow dynamics. The multiobjective optimization approach leads to a computationally efficient and accurate model chosen from a set of optimized models. The results demonstrate the metamodel’s robust capacity to provide accurate and computationally efficient estimates of damage extents and volume of oil outflow. This model can be used in maritime risk analysis contexts, particularly in strategic pollution preparedness and response management. The models can also be linked to operational response DSS when fast, and reasonably accurate estimates of spill sizes are critical.

由于船舶事故造成的石油泄漏等污染会对海洋环境产生不利影响，因此社会关注海事事故。操作性和战略性污染准备和响应风险管理是减轻此类不利影响的重要活动。已经提出了定量风险模型和决策支持系统 (DSS) 来支持这些风险管理活动。然而，目前缺乏计算速度快且准确的模型来估计漏油后果。虽然可以使用资源密集型模拟模型来进行准确预测，但这些模型很慢，并且无法轻松集成到定量风险模型或 DSS 中。因此，需要开发解决方案来加速计算过程。在这项工作中开发了一个快速准确的元模型来预测油轮碰撞事故中的损坏和石油流出。为此，将多目标优化应用于三种元建模方法：深度神经网络、多项式回归和梯度提升回归树。这些学习算法中使用的数据是使用最先进的工程模型生成的，用于意外损坏和石油流出动力学。多目标优化方法导致从一组优化模型中选择一个计算高效且准确的模型。结果表明，元模型具有强大的能力，可以提供对损坏程度和石油流出量的准确且计算效率高的估计。该模型可用于海事风险分析环境，特别是在战略性污染准备和响应管理方面。当快速、合理准确地估计泄漏规模至关重要时，这些模型还可以与操作响应 DSS 相关联。