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

Abstract

The use of dFADs by tuna purse-seine fisheries is widespread across oceans, and the echo-sounder buoys attached to these dFADs provide fishermen with estimates of tuna biomass aggregated to them. This information has potential for gaining insight into tuna behaviour and abundance, but has traditionally been difficult to process and use. The current study combines FAD logbook data, oceanographic data and echo-sounder buoy data to evaluate different Machine Learning models and establish a pipeline, named TUN-AI, for processing echo-sounder buoy data and estimating tuna biomass (in metric tons, t) at various levels of complexity: binary classification, ternary classification and regression. Models were trained and tested on over 5000 sets and over 6000 deployments. Of all the models evaluated, the best performing one uses a 3-day window of echo-sounder data, oceanographic data and position/time derived features. This model is able to estimate if tuna biomass was higher than 10 t or lower than 10 t with an F1-score of 0.925. When directly estimating tuna biomass, the best model (Gradient Boosting) has an error (MAE) of 21.6 t and a relative error (SMAPE) of 29.5%, when evaluated over sets. All models tested improved when enriched with oceanographic and position-derived features, highlighting the importance of these features when using echo-sounder buoy data. Potential applications of this methodology, and future improvements, are discussed.