Machine learning for characterizing tropical tuna aggregations under Drifting Fish Aggregating Devices (DFADs) from commercial echosounder buoys data
Authors:
- Y. BaidaiYBY. Baidai
- L. DagornLDL. Dagorn
- M.J. AmandeMAM.J. Amande
- D. GaertnerDGD. Gaertner
- M. CapelloMCM. Capello
- 1 September 2020
- journal article
- research article
- Published by Elsevier in Fisheries Research
- Vol. 229
Abstract
No abstract availableKeywords
Funding Information
- Institut de Recherche pour le Développement
- Agence Nationale de la Recherche
This publication has 42 references indexed in Scilit:
- Real-time acoustic classification of sperm whale clicks and shipping impulses from deep-sea observatoriesApplied Acoustics, 2010
- Acoustic identification of small pelagic fish species in Chile using support vector machines and neural networksFisheries Research, 2010
- Classification trees for species identification of fish-school echotracesICES Journal of Marine Science, 2009
- Indian Ocean circulation and climate variabilityReviews of Geophysics, 2009
- Effects of temperature on global patterns of tuna and billfish richnessMarine Ecology Progress Series, 2008
- Fish behaviour from fishers’ knowledge: the case study of tropical tuna around drifting fish aggregating devices (DFADs)Canadian Journal of Fisheries and Aquatic Sciences, 2007
- Satellite-linked acoustic receivers to observe behavior of fish in remote areasAquatic Living Resources, 2007
- Behavior of yellowfin (Thunnus albacares) and bigeye (T. obesus) tuna in a network of fish aggregating devices (FADs)Marine Biology, 2006
- Interpreting catch per unit effort data to assess the status of individual stocks and communitiesICES Journal of Marine Science, 2006
- In situ acoustic target strength measurements of tuna associated with a fish aggregating deviceICES Journal of Marine Science, 2000