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].
翻译:水流预测对于理解生态系统至关重要,对于阐明海洋在全球气候背景下的作用至关重要。解决方案从物理建模和长期观测到短期测量,从物理建模、长期观测到短期测量不等。在本文件中,我们考虑对水流预测采取一种共同的方法,即利用拉格朗加的浮标器进行水流预测,通过对元素轨迹进行内插来反映速度场。这里,一个尚未解决的重要方面是,最初在哪里部署漂流元素,以便获得的速度场能够有效地代表水流。为此,我们采用了基于速度场物理模型的集成方法。我们的方法绘制了地图并确定部署地点,作为将引导浮标者前往不同区中心的地方。这样,我们确认浮标覆盖的区域将捕捉速度场中的同系。对一年中速度场地图数据集的探索显示了我们的方法的可适用性,并表明在统一随机选择初始部署地点的共同方法上取得了相当大的改进。最后,我们的执行代码可以在《斯德哥尔摩公约》中找到。