Efficient deep learning surrogate method for predicting the transport of particle patches in coastal environments
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View abstract on PubMed
Summary
This summary is machine-generated.We developed a fast, cost-effective deep learning model (DLM) to predict pollutant transport in coastal waters. This surrogate model accurately forecasts particle patch advection and dispersion, aiding marine accident response.
Area Of Science
- Environmental science
- Oceanography
- Computational modeling
Background
- Coastal regions need reliable systems to forecast pollutant transport from marine accidents.
- Traditional models are often computationally expensive for real-time operational use.
- Surrogate models present a cost-effective alternative for predicting pollutant dispersion.
Purpose Of The Study
- To propose and validate a surrogate modeling method for predicting residual transport of particle patches in coastal environments.
- To develop a deep learning model (DLM) for forecasting particle advection and dispersion.
- To integrate the DLM with a simplified Lagrangian model for long-term predictions.
Main Methods
- A deep learning model (DLM) was trained using relevant forcing data to predict particle patch displacement (advection) and spread (dispersion) over one tidal period.
- The DLM's predictions were coupled into a simplified Lagrangian model for extended time predictions.
- The methodology was applied and validated in the Dutch Wadden Sea.
Main Results
- The trained DLM provides predictions in seconds, demonstrating high computational efficiency.
- The simplified Lagrangian model is one to two orders of magnitude faster than traditional Lagrangian models.
- The method successfully predicted particle patch transport in the Dutch Wadden Sea.
Conclusions
- The proposed surrogate modeling method offers a fast and cost-effective solution for operational pollutant transport forecasting in coastal areas.
- Deep learning models can be effectively used to predict advection and dispersion of passive particles.
- This approach significantly enhances the speed of pollutant transport predictions compared to traditional methods.
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