Several coastal regions require operational forecast systems for predicting the transport of pollutants released during marine accidents. In response to this need, surrogate models offer cost-effective solutions. Here, we propose a surrogate modeling method for predicting the residual transport of particle patches in coastal environments. These patches are collections of passive particles equivalent to Eulerian tracers but can be extended to other particulates. By only using relevant forcing, we train a deep learning model (DLM) to predict the displacement (advection) and spread (dispersion) of particle patches after one tidal period. These quantities are then coupled into a simplified Lagrangian model to obtain predictions for larger times. Predictions with our methodology, successfully applied in the Dutch Wadden Sea, are fast. The trained DLM provides predictions in a few seconds, and our simplified Lagrangian model is one to two orders of magnitude faster than a traditional Lagrangian model fed with currents.

中文翻译：

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用于预测沿海环境中粒子斑块传输的高效深度学习代理方法


一些沿海地区需要业务预报系统来预测海上事故期间释放的污染物的运输。为了满足这一需求，代孕模型提供了具有成本效益的解决方案。在这里，我们提出了一种替代建模方法，用于预测沿海环境中粒子斑块的残差传输。这些斑块是相当于欧拉示踪剂的钝化粒子集合，但可以扩展到其他粒子。通过仅使用相关强迫，我们训练了一个深度学习模型 （DLM） 来预测一个潮汐期后粒子斑块的位移（平流）和扩散（分散）。然后将这些量耦合到一个简化的拉格朗日模型中，以获得更大时间的预测。使用我们的方法进行预测，并在荷兰瓦登海成功应用，速度很快。经过训练的 DLM 可以在几秒钟内提供预测，我们简化的拉格朗日模型比传统的电流拉格朗日模型快一到两个数量级。