WITOIL-for-iMagine

WITOIL for iMagine is an advanced application utilizing the DEEPaaS API to predict the transport and transformation of actual or hypothetical oil spills in the ocean, powered by the Medslik-II model.

The Medslik-II oil spill model is a community-driven, freely available framework designed to predict the movement and weathering of oil slicks using a Lagrangian representation. It simulates the dispersion of oil spills based on real-world environmental conditions.

Integrating the model with DEEPaaS

In this repository, we have integrated a DeepaaS API into the existing Medslik-II oil spill model. To launch it, first install the package then run deepaas:

Warning: If you are using a virtual environment, make sure you are working with the last version of pip before installing the package. Use pip install --upgrade pip to upgrade pip.

git clone https://github.com/ai4os-hub/WITOIL-for-iMagine
cd WITOIL-for-iMagine
git submodule init
git submodule update --remote --merge
pip install -e ./path/to/submodule/dir
pip install -e .
deepaas-run --listen-ip 0.0.0.0

How To Use

To use WITOIL for iMagine, users must first register and obtain essential datasets from the following sources:

1. Copernicus Marine Environment Monitoring Service (CMEMS): - Users must create an account to access oceanographic data required for simulations. Registration is available here.Once the account is created, the user should store their username and password for future use.

2. European Centre for Medium-Range Weather Forecasts (ECMWF):
   • Users must register and obtain a token to access ERA5 reanalysis data. Registration can be completed here.
   • Users must retrieve their token from their profile here under the "Personal Access Token" section. The last step, which is somewhat hidden, is to accept the terms of use for the dataset.

• The user needs to navigate to the ERA5 single layer page, scroll to the bottom of the page, and accept the terms of usage when they appear.

  

RUNNING WITOIL FOR iMAGINE

Once registrations are complete, users must provide their credentials, spill details, and environmental parameters to run the model. After entering the necessary information, users can execute the simulation by clicking "Run". The outputs include visualizations of oil dispersion, temporal oil concentrations, and current/wind direction vectors.This process facilitates informed decision-making for mitigating the impact of oil spills.

Project structure

├── Jenkinsfile             <- Describes basic Jenkins CI/CD pipeline
├── Dockerfile              <- Steps to build a DEEPaaS API Docker image
├── LICENSE                 <- License file
├── README.md               <- The top-level README for developers using this project.
├── VERSION                 <- Version file indicating the version of the model
│
├── witoil_for_imagine
│   ├── README.md           <- Instructions on how to integrate your model with DEEPaaS.
│   ├── __init__.py         <- Makes <your-model-source> a Python module
│   ├── ...                 <- Other source code files
│   └── config.py           <- Module to define CONSTANTS used across the AI-model python package
│
├── api                     <- API subpackage for the integration with DEEP API
│   ├── __init__.py         <- Makes api a Python module, includes API interface methods
│   ├── config.py           <- API module for loading configuration from environment
│   ├── responses.py        <- API module with parsers for method responses
│   ├── schemas.py          <- API module with definition of method arguments
│   └── utils.py            <- API module with utility functions
│
├── data                    <- Data subpackage for the integration with DEEP API
│
├── docs                    <- A default Sphinx project; see sphinx-doc.org for details
│
├── models                  <- Folder to store your models
│
├── notebooks               <- Jupyter notebooks. Naming convention is a number (for ordering),
│                              the creator's initials (if many user development),
│                              and a short `_` delimited description, e.g.
│                              `1.0-jqp-initial_data_exploration.ipynb`.
│
├── references              <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports                 <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures             <- Generated graphics and figures to be used in reporting
│
├── requirements-dev.txt    <- Requirements file to install development tools
├── requirements-test.txt   <- Requirements file to install testing tools
├── requirements.txt        <- Requirements file to run the API and models
│
├── pyproject.toml          <- Makes project pip installable (pip install -e .)
│
├── tests                   <- Scripts to perform code testing
│   ├── configurations      <- Folder to store the configuration files for DEEPaaS server
│   ├── conftest.py         <- Pytest configuration file (Not to be modified in principle)
│   ├── data                <- Folder to store the data for testing
│   ├── models              <- Folder to store the models for testing
│   ├── test_deepaas.py     <- Test file for DEEPaaS API server requirements (Start, etc.)
│   ├── test_metadata       <- Tests folder for model metadata requirements
│   ├── test_predictions    <- Tests folder for model predictions requirements
│   └── test_training       <- Tests folder for model training requirements
│
└── tox.ini                 <- tox file with settings for running tox; see tox.testrun.org

References

1. De Dominicis M., N. Pinardi, G. Zodiatis, R. Lardner. MEDSLIK-II, a Lagrangian marine surface oil spill model for short-term forecasting - Part 1: Theory. Geosci. Model Dev., 6, 1851-1869, 2013.
2. De Dominicis M., N. Pinardi, G. Zodiatis, R. Archetti MEDSLIK-II, a Lagrangian marine surface oil spill model for short-term forecasting - Part 2: Numerical simulations and validations. Geosci. Model Dev., 6, 1851-1869, 2013.