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

arXiv: https://arxiv.org/abs/2109.06732 · HTML: https://mlsanity.com/paper/2109.06732/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 46 references that are in the map.
- First posted on arXiv 14 Sep 2021.

## Builds on (most cited first, 1 of 1)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)

## Built on by (most cited first, 1 of 1)

- [How do tuna schools associate to dFADs? A study using echo-sounder buoys to identify global patterns](https://mlsanity.com/paper/2207.07049/) (2022)

## Abstract (arXiv)

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 whenthese 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 the supervised signal for training, we employ more than 5000 set events with their corresponding tuna catch reported by the AGAC tuna purse seine fleet.