An Efficient Drifters Deployment Strategy to Evaluate Water Current Velocity Fields
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Abstract (arXiv)
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].
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 7 of its 52 references that are in the map. First posted on arXiv 10 Jan 2023.
Builds on
7 of 52 references.
References · 7
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- 2017Efficient Convex Optimization with Membership Oraclescited by 23
- 2020On Coresets for Support Vector Machinescited by 19
- 2020Sets Clusteringcited by 19
- 2022Obstacle Aware Sampling for Path Planningcited by 6
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Citing papers per year: 2023: 4; 2024: 0; 2025: 2.
6 papers.
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- 2025BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fieldscited by 1
- 2025BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields30 Sep 2025
- 2025Improving Model Classification by Optimizing the Training Dataset22 Jul 2025
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- 2023AutoCoreset: An Automatic Practical Coreset Construction Framework19 May 2023
- 2023Provable Data Subset Selection For Efficient Neural Network Training9 Mar 2023