Abstract
Reconstructions of Lagrangian drift, for example for objects lost at sea, are often uncertain due to unresolved physical phenomena within the data. Uncertainty is usually overcome by introducing stochasticity into the drift, but this approach requires specific assumptions for modelling uncertainty. We remove this constraint by presenting a purely data-driven framework for modelling probabilistic drift in flexible environments. Using ocean circulation model simulations, we generate probabilistic trajectories of object location by simulating uncertainty in the initial object position. We train an emulator of probabilistic drift over one day given perfectly known velocities and observe good agreement with numerical simulations. Several loss functions are tested. Then, we strain our framework by training models where the input information is imperfect. On these harder scenarios, we observe reasonable predictions although the effects of data drift become noticeable when evaluating the models against unseen flow scenarios.
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Notes
Due to the limited number of one-day snapshots in a year, the same flow scenarios are used for both training and evaluation for the year 2018. However, trajectories are sampled across different locations (see Section 3.2) which prevents the model from relying on memory.
Although U and V are gridded on curvilinear coordinates, the limited region that we consider makes it reasonable to neglect projection errors and to associate each grid cell to a Cartesian pixel.
If this results in particles to lie outside of the ocean’s domain then we discard them, and hence the actual number of particles may be less than \(N_P\).
The training split for the 2016 dataset is not used in this study.
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Funding
This work has been supported by Ocean Next, Datlas, and ANRT by means of a PhD CIFRE grant attributed to Joseph Jenkins. It has been co-granted by the FEDER MARITTIMO GIAS (Geolocalisation by AI for maritime Security). Funding was received from Chaire Intelligence Artificielle ADSIL ANR-20-CHIA-0014 and ANR-18-CE40-0014 SMILES. The NEMO calculations were performed using GENCI-IDRIS resources, grant A0110101707.
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Conceptualization: Y.O., A.P., J.J., J.S. and H.G.; Methodology: J.J., A.P., Y.O., J.S., and C.U.; Data curation: J.J. and Y.O.; Investigation: J.J.; Software & validation: J.J.; Visualization: J.J. and A.P.; Writing - original draft: J.J. and A.P.; Writing - review & editing: A.P., Y.O. and J.S.; Supervision: A.P., Y.O. and H.G.; Project administration: H.G., J.V., and C.U.; Funding acquisition: H.G., J.V., C.U. and Y.O.
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Jenkins, J., Paiement, A., Ourmières, Y. et al. A DNN Framework for Learning Lagrangian Drift With Uncertainty. Appl Intell 53, 23729–23739 (2023). https://doi.org/10.1007/s10489-023-04625-1
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DOI: https://doi.org/10.1007/s10489-023-04625-1