Bayesian inference modeling to rank response technologies in arctic marine oil spills

Marine oil spills have a detrimental effect on aquatic systems. Yet, it is challenging to select appropriate tech-nologies in the Arctic because of limited logistics support, inclement weather conditions, and remoteness, and limited research has been conducted in this direction. This article suggests a method to rank the oil response technologies, including mechanical recovery, chemical dispersant, and in-situ burning, for use in Arctic oil spill risk assessment and preparedness planning. The proposed Preference Learning based Bayesian Inference Modeling offers data-driven ranking of systems by learning a label function and considers factors such as ice covered sea areas, cold weather, and spill volume. A data generation system is developed to produce numerous oil spill scenarios, using a state-of-the-art engineering tool. Results demonstrate that the model, while simple, can effi-ciently and accurately select the best available technique, making it suitable primarily for marine pollution preparedness and response planning in strategic risk assessments.

Automated summary

This article proposes a method for ranking oil response technologies in Arctic oil spill risk assessment and preparedness planning. By considering factors such as ice covered sea areas, cold weather, and spill volume, the proposed model efficiently selects the best available technique using preference learning based Bayesian inference modeling. This model is suitable for strategic risk assessments in marine pollution preparedness and response planning.