Growth parameters with traditional and artificial neural networks methods of big-scale sand smelt (Atherina boyeri Risso, 1810)

Semra Benzer; Recep Benzer

doi:10.12714/egejfas.40.2.02

TR EN

Gümüş balığının (Atherina boyeri Risso, 1810) geleneksel ve yapay sinir ağları yöntemleriyle büyüme parametreleri

Abstract

Bu çalışmada İznik Gölü'nde yakalanan gümüş balığının (Atherina boyeri Risso, 1810) büyüme parametreleri geleneksel (boy ağırlık ilişkileri (LWR), von Bertalanffy (VB) ve kondisyon faktörü (CF)) ve modern yaklaşımlarla (yapay sinir ağları) incelenmiştir. Av sezonunda bölge balıkçılarından Nisan 2018-Nisan 2019 tarihleri arasında toplam 635 adet (%44,84 dişi ve %55,16 erkek) birey toplanmıştır. Tüm populasyon için ortalama çatal boy (FL) 67,31 mm (40,10 - 97,77 mm), ortalama ağırlık (W) 2,57 g (0,53 - 7,50g) ve ortalama CF 0,790 (0,170-1,520) olarak bulunmuştur. LWR dişi popülasyon için W=0,00001437 L2,8602, erkek popülasyon için W=0,00001570 L2,8266 ve tüm popülasyon için W=0,00001328 L2,8717 olarak bulunmuştur. Von Bertalanffy dişi, erkek ve tüm popülasyonda sırasıyla L_t=136,218 [1-e^(-0,240(t+0,51)) ], L_t=155,042 [1-e^(-0,185(t+0,73)) ] ve L_t=146,916 [1-e^(-0,205(t+0,64)) ] olarak tespit edilmiştir. Yapay sinir ağları (ANN) ile yapılan hesaplamalarda eğitim, doğrulama ve test verilerindeki MSE (Ortalama Kare Hata) ve R (korelasyon katsayısı) değerleri sırasıyla 4.52559e-5 9.09347e-1, 4.86111e-5 9.00931e-1 ve 3.391999e-5 9.43465e-1 olarak bulunmuştur. YSA’ların büyüme tahmininin değerlendirilmesinde bir alternatif olabileceği belirlenmiştir.

Keywords

Growth parameters with traditional and artificial neural networks methods of big-scale sand smelt (Atherina boyeri Risso, 1810)

Abstract

In this study, the growth parameters of big-scale sand smelt (Atherina boyeri Risso, 1810) in Iznik Lake has been determined with traditional (length weight relationships (LWRs), von Bertalanffy (VB), condition factor (CF)) and modern approaches (Artificial Neural Networks - ANNs). A total of 635 specimens (44.84% female and 55.16% male) were collected from the local fisherman during the hunting season between April 2018 to April 2019. Mean fork length (FL) (mm, min-max), mean W (g, min-max) and mean CF (value, min-max) were estimated as 67.31 mm (40.10 - 97.77 mm), 2.57 g (0.53 - 7.50 g), and 0.790 (0.170-1.520) for all individuals. The length weight relationships were determined W=0.00001437 L^2.8602 for female, W=0.00001570 L^2.8266 for male and W=0.00001328 L^2.8717 for all individuals. The von Bertalanffy equations were determined L_t=136.218 [1-e^(-0.240(t+0.51)) ] for female, L_t=155.042 [1-e^(-0.185(t+0.73)) ] for male, and L_t=146.916 [1-e^(-0.205(t+0.64)) ] for all individuals. The values in training (MSE (Mean Squared Error) 4.52559e-5, R (correlation coefficients) 9.09347e-1), verification (MSE 4.86111e-5, R2 9.00931e-1) and test data (MSE 3.391999e-5, R 9.43465e-1) were found in calculations made with ANNs. It was determined that ANNs could be an alternative for evaluating growth estimation.

Keywords

Supporting Institution

Gazi University Project Resources Center Fund

Project Number

BAP Project Number: 04/2018-05

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Details

Primary Language

English

Subjects

Limnology, Fish Biology

Journal Section

Research Article

Authors

Semra Benzer ^*
0000-0002-8548-8994
Türkiye

Recep Benzer
0000-0002-5339-0554
Türkiye

Early Pub Date

June 10, 2023

Publication Date

June 15, 2023

Submission Date

December 2, 2022

Acceptance Date

March 30, 2023

Published in Issue

Year 2023 Volume: 40 Number: 2

DOI

https://doi.org/10.12714/egejfas.40.2.02

IZ

https://izlik.org/JA55LC85XG

Cite

RIS / Bibtex

APA

Benzer, S., & Benzer, R. (2023). Growth parameters with traditional and artificial neural networks methods of big-scale sand smelt (Atherina boyeri Risso, 1810). Ege Journal of Fisheries and Aquatic Sciences, 40(2), 96-102. https://doi.org/10.12714/egejfas.40.2.02

AMA

1.Benzer S, Benzer R. Growth parameters with traditional and artificial neural networks methods of big-scale sand smelt (Atherina boyeri Risso, 1810). EgeJFAS. 2023;40(2):96-102. doi:10.12714/egejfas.40.2.02

Chicago

Benzer, Semra, and Recep Benzer. 2023. “Growth Parameters With Traditional and Artificial Neural Networks Methods of Big-Scale Sand Smelt (Atherina Boyeri Risso, 1810)”. Ege Journal of Fisheries and Aquatic Sciences 40 (2): 96-102. https://doi.org/10.12714/egejfas.40.2.02.

EndNote

Benzer S, Benzer R (June 1, 2023) Growth parameters with traditional and artificial neural networks methods of big-scale sand smelt (Atherina boyeri Risso, 1810). Ege Journal of Fisheries and Aquatic Sciences 40 2 96–102.

IEEE

[1]S. Benzer and R. Benzer, “Growth parameters with traditional and artificial neural networks methods of big-scale sand smelt (Atherina boyeri Risso, 1810)”, EgeJFAS, vol. 40, no. 2, pp. 96–102, June 2023, doi: 10.12714/egejfas.40.2.02.

ISNAD

Benzer, Semra - Benzer, Recep. “Growth Parameters With Traditional and Artificial Neural Networks Methods of Big-Scale Sand Smelt (Atherina Boyeri Risso, 1810)”. Ege Journal of Fisheries and Aquatic Sciences 40/2 (June 1, 2023): 96-102. https://doi.org/10.12714/egejfas.40.2.02.

JAMA

1.Benzer S, Benzer R. Growth parameters with traditional and artificial neural networks methods of big-scale sand smelt (Atherina boyeri Risso, 1810). EgeJFAS. 2023;40:96–102.

MLA

Benzer, Semra, and Recep Benzer. “Growth Parameters With Traditional and Artificial Neural Networks Methods of Big-Scale Sand Smelt (Atherina Boyeri Risso, 1810)”. Ege Journal of Fisheries and Aquatic Sciences, vol. 40, no. 2, June 2023, pp. 96-102, doi:10.12714/egejfas.40.2.02.

Vancouver

1.Semra Benzer, Recep Benzer. Growth parameters with traditional and artificial neural networks methods of big-scale sand smelt (Atherina boyeri Risso, 1810). EgeJFAS. 2023 Jun. 1;40(2):96-102. doi:10.12714/egejfas.40.2.02

Cited By

Comparison between traditional models and artificial neural networks as estimators of the growth of the Tigris scraper Capoeta umbla (Teleostei: Cyprinidae) in the Munzur River, Turkey

Revista Científica de la Facultad de Ciencias Veterinarias

https://doi.org/10.52973/rcfcv-e35513