Tun-AI: What can Machine Learning tell about tuna aggregation dynamics?

M. Navarro García, D. Precioso, K. Gavira O’Neill, A. Torres Barrán, D. Gordo, V. Gallego, D. Gómez-Ullate Oteiza

In this talk we present Tun-AI, a new pipeline for estimating tuna biomass aggregated at drifting Fish Aggregating Devices (dFADs), i.e., floating objects drifting in the sea surface equipped with an echo-sounder and GPS communication devices. For the first time, echo-sounder buoy data are enriched with oceanographic and position-derived features and with real catch information provided by the Spanish tropical tuna purse seine fleet, which are used to train Machine Learning models that solve different tasks relevant to fisheries operations. These improved biomass estimations could be used to enhance knowledge on species distribution or better understanding many tuna aggregation dynamics processes, and we apply them to study the aggregation behavior of tuna schools to the dFADs.

Keywords: Data Science, Machine Learning, Oceanography, Smoothing

Scheduled

RSME-SEIO Invited Session. Mathematical research applied to the sustainability of the fishing industry and maritime transport
June 8, 2022  4:00 PM
A11

Other papers in the same session

Smart Shipping: Marine Shipping Route Optimization using Weather Data

D. Precioso, D. Gómez-Ullate Oteiza, J. Jiménez de la Jara, F. Amor Roldán, V. Gallego Alcalá, A. Torres Barrán, M. Navarro García

Fishing route optimization to enhance the economic and environmental sustainability

I. Granado, L. Hernando, J. A. Fernandes

A parallel iterative method for navigation problems

D. Martín de Diego, S. Ferraro, R. T. Sato Martin de Almagro

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