This repository contains scripts and data related to the manuscript:
Fajardo-Urbina, J.M., Liu, Y., Georgievska, S., Gräwe, U., Clercx, H.J.H., Gerkema, T., & Duran-Matute, M. (2024). Efficient deep learning surrogate method for predicting the transport of particle patches in coastal environments. Available at SSRN: http://dx.doi.org/10.2139/ssrn.4815334
There are in total 5 NetCDF files. They can be used to run the surrogate and optimal prediction experiments and reproduce results from Figure 5 of the manuscript. They are provided inside the folder data.
The environment used for the analysis uses Python v3.8 and can be found in the file environment.yml.
The simplified Lagrangian model (Eq. (4) of the manuscript) is implemented in the notebook located in the folder notebooks. Here, we also add all the necessary instructions to run the surrogate and optimal prediction experiments. The notebook can be run using any of the following instructions:
- Mybinder.org: click on the binder icon to open a jupyter-lab session.
- Google Colab: follow the instructions from the notebook clone_repo_using_google_colab.ipynb
- Clone or download the repository on your PC: install the packages of the file environment.yml.
Jup-lab:
The netCDF files provided in this repository are generated from the following raw data:
- Eulerian data from the GETM/GOTM model, and its set-up is described in:
- Duran-Matute et al. (2014): https://doi.org/10.5194/os-10-611-2014
- Grawe et al. (2016): https://doi.org/10.1002/2016JC011655
- The Lagrangian model Parcels v2.4.2 can be installed from:
- The ConvLSTM model used in our study is built using Pytorch (https://anaconda.org/pytorch/pytorch), and its implementation is described in:
- Liu et al. (2021) https://doi.org/10.1175/MWR-D-20-0113.1