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有效的深度学习替代方法,用于预测沿海环境中的粒子斑块运输

Jeancarlo M Fajardo-Urbina¹, Yang Liu², Sonja Georgievska²

¹Fluids and Flows group and J.M. Burgers Center for Fluid Dynamics, Department of Applied Physics and Science Education, Eindhoven University of Technology, Eindhoven, The Netherlands.

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|November 15, 2024

概括

我们开发了一个快速,具有成本效益的深度学习模型 (DLM) 来预测沿海水域的污染物运输. 这种替代模型准确地预测了粒子斑块的转移和分散,有助于应对海上事故.

科学领域:

环境科学环境科学
海洋学海洋学海洋学
计算建模计算建模

背景情况:

沿海地区需要可靠的系统来预测海上事故造成的污染物运输.
传统模型通常在实时运营用途上计算昂贵.
替代模型为预测污染物分散提供了一个具有成本效益的替代方案.

研究的目的:

提出和验证一种替代模型方法,用于预测沿海环境中颗粒斑块的残余运输.
开发一个深度学习模型 (DLM) 来预测粒子的偏向和分散.
为了实现长期预测,将DLM与简化的拉格朗日模型集成在一起.

主要方法:

一个深度学习模型 (DLM) 使用相关的强迫数据进行训练,以预测在一个潮时期的粒子补丁位移 (向导) 和扩散 (分散).
DLM的预测被合到一个简化的拉格朗日模型中,用于延长时间的预测.
该方法在荷兰瓦登海被应用和验证.

主要成果:

经过训练的DLM在几秒钟内提供预测,证明了高计算效率.
简化的拉格朗日模型比传统的拉格朗日模型快一到两个数量级.
该方法成功预测了荷兰瓦登海的粒子补丁传输.

结论:

拟议的代用建模方法为沿海地区的运营污染物运输预测提供了快速和经济有效的解决方案.
深度学习模型可以有效地用于预测被动粒子的向导和分散.
与传统方法相比,这种方法显著提高了污染物运输预测的速度.

关键词:

沿海系统沿海系统深度学习替代模型模型荷兰瓦登海荷兰瓦登海拉格朗日向和分散.拉格朗的污染模型.

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