An Efficient Drifters Deployment Strategy to Evaluate Water Current Velocity Fields

TL;DR

This paper proposes a clustering-based deployment strategy for water current drifters, optimizing their initial placement to better capture velocity fields and improve prediction accuracy over traditional random deployment methods.

Contribution

It introduces a novel clustering approach using physical models to determine optimal drifter deployment locations for more efficient water current measurement.

Findings

Significant improvement over random deployment in capturing velocity fields.

Method validated on a dataset spanning over a year.

Deployment strategy enhances the representativeness of water current measurements.

Abstract

Water current prediction is essential for understanding ecosystems, and to shed light on the role of the ocean in the global climate context. Solutions vary from physical modeling, and long-term observations, to short-term measurements. In this paper, we consider a common approach for water current prediction that uses Lagrangian floaters for water current prediction by interpolating the trajectory of the elements to reflect the velocity field. Here, an important aspect that has not been addressed before is where to initially deploy the drifting elements such that the acquired velocity field would efficiently represent the water current. To that end, we use a clustering approach that relies on a physical model of the velocity field. Our method segments the modeled map and determines the deployment locations as those that will lead the floaters to 'visit' the center of the different segments. This way, we validate that the area covered by the floaters will capture the in-homogeneously in the velocity field. Exploration over a dataset of velocity field maps that span over a year demonstrates the applicability of our approach, and shows a considerable improvement over the common approach of uniformly randomly choosing the initial deployment sites. Finally, our implementation code can be found in [1].