# An Efficient Drifters Deployment Strategy to Evaluate Water Current Velocity Fields

arXiv: https://arxiv.org/abs/2301.04216 · HTML: https://mlsanity.com/paper/2301.04216/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 52 references that are in the map.
- First posted on arXiv 10 Jan 2023.

## Builds on (most cited first, 7 of 7)

- [Sketching as a Tool for Numerical Linear Algebra](https://mlsanity.com/paper/1411.4357/) (2014)
- [Hyperparameter Search in Machine Learning](https://mlsanity.com/paper/1502.02127/) (2015)
- [Efficient Convex Optimization with Membership Oracles](https://mlsanity.com/paper/1706.07357/) (2017)
- [On Coresets for Support Vector Machines](https://mlsanity.com/paper/2002.06469/) (2020)
- [Sets Clustering](https://mlsanity.com/paper/2003.04135/) (2020)
- [Obstacle Aware Sampling for Path Planning](https://mlsanity.com/paper/2203.04075/) (2022)
- [Robot Coverage Path Planning for General Surfaces Using Quadratic Differentials](https://mlsanity.com/paper/1701.07549/) (2017)

## Built on by (most cited first, 6 of 6)

- [Provable Data Subset Selection For Efficient Neural Network Training](https://mlsanity.com/paper/2303.05151/) (2023)
- [Dataset Distillation Meets Provable Subset Selection](https://mlsanity.com/paper/2307.08086/) (2023)
- [ORBSLAM3-Enhanced Autonomous Toy Drones: Pioneering Indoor Exploration](https://mlsanity.com/paper/2312.13385/) (2023)
- [AutoCoreset: An Automatic Practical Coreset Construction Framework](https://mlsanity.com/paper/2305.11980/) (2023)
- [Improving Model Classification by Optimizing the Training Dataset](https://mlsanity.com/paper/2507.16729/) (2025)
- [BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields](https://mlsanity.com/paper/2509.26005/) (2025)

## 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].