Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloIdentifying small pelagic Mediterranean fish schools from acoustic and environmental data using optimized artificial neural networks
Anno di pubblicazione2019
FormatoCartaceo
Autore/iAronica, S.; Fontana, I.; Giacalone, G.; Lo Bosco, G.; Rizzo, R.; Mazzola, S.; Basilone, G.; Ferreri, R.; Genovese, S.; Barra, M.; Bonanno, A.
Affiliazioni autoriNatl Res Council Italy; Univ Palermo; Natl Res Council Italy
Autori CNR e affiliazioni
  • RICCARDO RIZZO
  • ANGELO BONANNO
  • SALVATORE ARONICA
  • GIOVANNI GIACALONE
  • IGNAZIO FONTANA
  • MARCO BARRA
  • SALVATORE MAZZOLA
Lingua/e
  • inglese
AbstractThe Common Fisheries Policy of the European Union aims to exploit fish stocks at a level of Maximum Sustainable Yield by 2020 at the latest. At the Mediterranean level, the General Fisheries Commission for the Mediterranean (GFCM) has highlighted the importance of reversing the observed declining trend of fish stocks. In this complex context, it is important to obtain reliable biomass estimates to support scientifically sound advice for sustainable management of marine resources. This paper presents a machine learning methodology for the classification of pelagic species schools from acoustic and environmental data. In particular, the methodology was tuned for the recognition of anchovy, sardine and horse mackerel. These species have a central role in the fishing industry of Mediterranean countries and they are also of considerable importance in the trophic web because they occupy the so-called middle trophic level. The proposed methodology consists of a classifier based on an optimized two layer feed-forward neural network. Morphological, bathymetric, energetic and positional features, extracted from acoustic data, are used as input, together with other environmental data features. The classifier uses an optimal number of neurons in the hidden layer, and a feature selection strategy based on a genetic algorithm. Working on a dataset of 2565 fish schools, the proposed methodology permitted us to identify the these three fish species with an accuracy of around 95%.
Lingua abstractinglese
Altro abstract-
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Pagine da149
Pagine a161
Pagine totali13
RivistaEcological informatics (Print)
Attiva dal 2006
Editore: Elsevier - Amsterdam
Paese di pubblicazione: Paesi Bassi
Lingua: inglese
ISSN: 1574-9541
Titolo chiave: Ecological informatics (Print)
Titolo proprio: Ecological informatics. (Print)
Numero volume della rivista50
Fascicolo della rivista-
DOI10.1016/j.ecoinf.2018.12.007
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:WOS:000461401800016)
  • Scopus (Codice:2-s2.0-85060879443)
Parole chiaveAcoustic survey, Fish school, Neural networks, Classification
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Strutture CNR
  • ICAR — Istituto di calcolo e reti ad alte prestazioni
  • IAS — Istituto per lo studio degli impatti Antropici e Sostenibilità in ambiente marino
Moduli/Attività/Sottoprogetti CNR
  • INT.P02.007.001 : Analisi intelligente dei dati per la bioinformatica
Progetti Europei-
Allegati
Identifying small pelagic Mediterranean fish schools from acoustic and environmental data using optimized artificial neural networks (documento privato )
Tipo documento: application/pdf