Tuna-AI: tuna biomass estimation with Machine Learning models trained on oceanography and echosounder FAD data

@article{Precioso2021TunaAITB,
  title={Tuna-AI: tuna biomass estimation with Machine Learning models trained on oceanography and echosounder FAD data},
  author={Daniel Precioso and Manuel Navarro-Garc'ia and Kathryn Gavira-O'Neill and Alberto Torres-Barr'an and David Gordo and Victor Gallego-Alcal'a and David G'omez-Ullate},
  journal={bioRxiv},
  year={2021}
}
Echo-sounder data registered by buoys attached to drifting FADs provide a very valuable source of information on populations of tuna and their behaviour. This value increases when these data are supplemented with oceanographic data coming from CMEMS. We use these sources to develop Tuna-AI, a Machine Learning model aimed at predicting tuna biomass under a given buoy, which uses a 3-day window of echo-sounder data to capture the daily spatio-temporal patterns characteristic of tuna schools. As… Expand

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