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Abstract

Probabilistic School Classification of Multiple Species in Acoustic Echograms Based on Machine Learning  †

Fundación AZTI, 48395 Sukarrieta, Spain
*
Author to whom correspondence should be addressed.
Presented at the IX Iberian Congress of Ichthyology, Porto, Portugal, 20–23 June 2022.
Presenting author (Oral communication).
Biol. Life Sci. Forum 2022, 13(1), 44; https://doi.org/10.3390/blsf2022013044
Published: 7 June 2022
(This article belongs to the Proceedings of The IX Iberian Congress of Ichthyology)

Abstract

:
Multifrequency trawl-acoustic surveys are used worldwide for continuous monitoring of pelagic ecosystems. Acoustic backscattering energy partitioning in different species is typically done by visual scrutiny of the echograms with the aid of trawl species composition, which may be subjective and time-consuming. Alternatively, machine learning techniques may provide well-stablished, objective, and reproducible methods for automatic school classification in acoustic echograms. The pelagic ecosystem is a diverse one, where many species co-occur in space and time, being mixed catches very common during scientific surveys. However, most of the school classification models are built using single species composition trawls due to difficulties to assign a class to each school in multispecific trawls. The present study has the aim of developing and comparing different probabilistic multivariate models to identify pelagic species in mixed scenarios based on trawl catch proportions. In addition to the standard predictors, a novel variable, collective mean TS per nautical mile measured on the periphery of the schools, has shown to play an important role in species discrimination. The methods were applied on data from seven consecutive years of an acoustic survey in the Bay of Biscay. Preliminary results yielded classification performances near 90% in classifying 5 different pelagic species.

Author Contributions

Conceptualization, A.L., G.B. and M.L.; methodology, A.L. and G.B.; formal analysis, A.L. and G.B.; writing—original draft preparation, A.L.: writing—review and editing, A.L., G.B. and M.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Pre-doctoral grant from the Department of Education of Basque Government, grant number PRE_2020_1_0129.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data underlying this article will be shared on reasonable request to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.
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Share and Cite

MDPI and ACS Style

Lekanda, A.; Boyra, G.; Louzao, M. Probabilistic School Classification of Multiple Species in Acoustic Echograms Based on Machine Learning . Biol. Life Sci. Forum 2022, 13, 44. https://doi.org/10.3390/blsf2022013044

AMA Style

Lekanda A, Boyra G, Louzao M. Probabilistic School Classification of Multiple Species in Acoustic Echograms Based on Machine Learning . Biology and Life Sciences Forum. 2022; 13(1):44. https://doi.org/10.3390/blsf2022013044

Chicago/Turabian Style

Lekanda, Aitor, Guillermo Boyra, and Maite Louzao. 2022. "Probabilistic School Classification of Multiple Species in Acoustic Echograms Based on Machine Learning " Biology and Life Sciences Forum 13, no. 1: 44. https://doi.org/10.3390/blsf2022013044

APA Style

Lekanda, A., Boyra, G., & Louzao, M. (2022). Probabilistic School Classification of Multiple Species in Acoustic Echograms Based on Machine Learning . Biology and Life Sciences Forum, 13(1), 44. https://doi.org/10.3390/blsf2022013044

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