Situation: Selecting the correct response technology for emergency oil spill response is difficult. This work will rank response technologies (from MCR, CDU, & ISB) considering Arctic environement.
Task:
- Since Oil Spill is rare in Arctic, we needed to build data pipeline to obtain reasonable numbers of incidents
- Build model to classify technologies
Action:
- Based on Monte Carlo Simulation (implemented using distribution of feature variables, and outputs obtained from engineering model), 3100 scenarios is generated
- A #multi-class, multi-label classification system is developed. Bayesian Inference model is implemented using Naive Bayes Classifier. Multi-label: y = [y1, y2, y3] = [MCR, CDU, ISB]. Each label can have multiple classes e.g. [OK, Consider, Go Next Season, Unknown, Not recommended]
Result: Based on oil and environmental conditions in Arctic, our model proposes which technology would be better to respond oil spill. The model has 0.79, 0.93 and 0.93 ROC-AUC score for different technologies.
BIM
├── requirement.txt Dependencies
├── README.md Project README
├── data
│ ├── raw Raw files e.g.
│ ├── processed Cleaned and processed data
├── models
│ └── model_BIMReTA.pkl Trained models
│ ├── ...
├── reports Figures in the paper
│ ├── Fig3 Bar plot
│ ├── Fig5a.png ROC curve
└── src Source files
├── 2.0-bayesian-model.py
├── ...
└── 5.0-nn.py
├── BIMReTA_app.py Streamlit web app
├── Dockerfile
The picture below is an overview of the project's methodology. Further details can be found in this journal paper.
Tanmoy Das, Floris Goerlandt (2022). Bayesian inference modeling to rank response technologies in arctic marine oil spills. Marine Pollution Bulletin, 185, 114203. https://doi.org/10.1016/j.marpolbul.2022.114203