Optimal Model-Based Sensor Placement & Adaptive Monitoring Of An Oil Spill

TL;DR

This paper introduces an adaptive, model-based sensor placement method for oil spill monitoring that uses PDE-constrained optimization and real-time data assimilation to improve oil spill mapping and prediction accuracy.

Contribution

It develops a novel adaptive monitoring framework combining PDE-constrained optimization, reduced-order modeling, and Kalman filtering for oil spill detection and prediction.

Findings

Achieves 30% improvement in oil presence mapping and prediction.

Integrates real-time data assimilation with dynamic ocean and oil models.

Demonstrates enhanced oil spill monitoring over standard methods.

Abstract

This paper presents a model based adaptive monitoring method for the estimation of flow tracers, with application to mapping, prediction and observation of oil spills in the immediate aftermath of an incident. Autonomous agents are guided to optimal sensing locations via the solution of a PDE constrained optimisation problem, obtained using the adjoint method. The proposed method employs a dynamic model of the combined ocean and oil dynamics, with states that are updated in real-time using a Kalman filter that fuses agent-based measurements with a reduced-order model of the ocean circulation dynamics. In turn, the updated predictions from the fluid model are used to identify and update the reduced order model, in a process of continuous feedback. The proposed method exhibits a 30% oil presence mapping and prediction improvement compared to standard industrial oil observation sensor guidance and model use.