Model-Based Optimal Adaptive Monitoring of Oil Spills

This article presents a novel model-based adaptive monitoring framework for the estimation of oil spills using mobile sensors. In the first of a four-stage process, simulation of a combined ocean, wind, and oil model provides a state trajectory over a finite time horizon, used in the second stage to solve an adjoint optimization problem for sensing locations. In the third stage, a reduced-order model is identified from the state trajectory, utilized alongside measurements to produce smoothed state estimates in the fourth stage, which update and re-initialize the first-stage simulation. In the second stage, sensors are directed to optimal sensing locations using the solution of a partial differential equation (PDE)-constrained optimization problem. This problem formulation represents a key contributory idea, utilizing the definition of spill uncertainty as a scalar PDE to be minimized subject to sensor, ocean, wind, and oil constraints. Spill uncertainty is a function of uncertainty in 1) the bespoke model of the ocean, wind, and oil spill; 2) the reduced order model identified from sensor data; and 3) the data assimilation method employed to estimate the states of the environment and spill. The uncertainty minimization is spatiotemporally weighted by a function of spill probability and information utility, prioritizing critical measurements. A numerical case study spanning a 2500-km(2) coastal area is presented, with four mobile sensors arriving 12 h after an oil leak. Compared to industry standard ``ladder pathing,'' the proposed method achieves an 80% reduction in oil distribution error and a 62% reduction in sensor distance traveled.

Automated summary

This article presents a novel model-based adaptive monitoring framework for the estimation of oil spills using mobile sensors. The framework utilizes simulation of state trajectory, optimization of sensing locations, and smoothed state estimates to accurately monitor oil spills.