Predicting particle trajectories in oceanic flows using artificial neural networks

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Predicting ocean transport has many practical applications ranging from search and rescue operations to predicting the spread of oil, debris, and biogeochemical tracers, yet trajectory prediction remains a challenge for existing ocean modeling techniques. General circulation models require high resolution observational data in order to be properly initialized, but these data do not exist for the ocean. Statistical models are difficult to tune with existing data and are often too simple to accurately encapsulate turbulent flows. Here we investigate a data-driven approach to ocean transport prediction wherein the goal is to first learn from available data instead of prescribed laws of physics and then apply this information to new data. More specifically, we explore whether simple artificial neural networks (ANNs) are capable of learning to predict 2D particle trajectories using only previous velocity observations. ANNs are trained in two ways: first, a so-called “one-to-one ANN” uses a particle's most recently observed velocity to predict its velocity six hours later, and second, a “time series ANN” uses the past 24 hours’ worth of velocity observations to predict the next 24 h. We present a proof-of-concept considering particles in a hierarchy of simulated flow regimes ranging from uniform, steady flow to more complex scenarios with interacting scales of motion and then substantiate our approach on trajectories in modeled flows generated by a high-resolution Hybrid Coordinate Ocean Model for a mesoscale eddy in the northern Gulf of Mexico. We also assess ANN sensitivity to the prediction window over which forecasts are made, the number of training particles used, and the size of the network. ANNs successfully predict 24 h trajectories within the temporal bounds of the training data with forecast errors around half those of both rudimentary persistence and classical ARIMA models. Predicting beyond the domain of the training data leads to forecast errors comparable to ARIMA models. Our results suggest that ANNs offer promising potential as a data-driven approach to forecasting material transport in the ocean.

Original languageEnglish (US)
Article number101707
JournalOcean Modelling
StatePublished - Dec 2020


  • Advection
  • Current prediction
  • Current velocity
  • Machine learning
  • Mass transport
  • Particle motion

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Oceanography
  • Geotechnical Engineering and Engineering Geology
  • Atmospheric Science


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