Araştırma Makalesi

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

Cilt: 40 Sayı: 2 15 Haziran 2023
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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

Destekleyen Kurum

Gazi University Project Resources Center Fund

Proje Numarası

BAP Project Number: 04/2018-05

Kaynakça

  1. Bagenal, T.B., & Tesch, F.W. (1978). Age and growth. In T. Bagenal (Ed.), Methods for Assessment of Fish Production in Freshwaters (pp 101-136). Oxford: Blackwell Science Publications.
  2. Bartulovic, V., Glamuzina, B., Conides, A., Gavriloviç, A., & Dulçiç, J. (2006). Maturation, reproduction, and recruitment of the sand smelt, Atherina boyeri Risso, 1810 (Pisces: Atherinidae) in the estuary of Mala Neretva River (Southeastern Adriatic, Croatia). Acta Adriatica, 47(1), 5-11.
  3. Benzer, S. (2016). Growth Characteristics of Atherina boyeri Risso, 1880 in Mogan Lake. International Conference on Biological Sciences, Konya, Türkiye.
  4. Benzer, S. (2020). Artificial Neural Networks Approach to Growth Properties Atherina boyeri Risso 1810 in Yamula Dam Lake Turkey. Fresenius Environmental Bulletin, 29(2), 1145-1152.
  5. Benzer, S., & Benzer, R. (2016). Evaluation of growth in pike (Esox lucius L., 1758) using traditional methods and artificial neural networks. Applied Ecology and Environmental Research, 14(2), 543-554. https://doi.org/10.15666/aeer/1402_543554
  6. Benzer, S., & Benzer, R. (2017). Comparative growth models of big scale sand smelt Atherina boyeri Risso 1810 sampled from Hirfanlı Dam Lake Kırsehir Ankara Turkey. Computational Ecology and Software, 7(2), 82-90. https://doi.org/10.0000/issn-2220-721x-compuecol-2017-v7-0007
  7. Benzer, S., & Benzer, R. (2019). Alternative growth models in fisheries: Artificial Neural Networks. Journal of Fisheries, 7(3), 719-725.
  8. Benzer, S., & Benzer, R. (2020a). Growth Properties of Pseudorasbora parva in Süreyyabey Reservoir: Traditional and Artificial Intelligent Methods. Thalassas, 36(1), 149-156. https://doi.org/10.1007/s41208-020-00192-1

Ayrıntılar

Birincil Dil

İngilizce

Konular

Limnoloji , Balık Biyolojisi

Bölüm

Araştırma Makalesi

Erken Görünüm Tarihi

10 Haziran 2023

Yayımlanma Tarihi

15 Haziran 2023

Gönderilme Tarihi

2 Aralık 2022

Kabul Tarihi

30 Mart 2023

Yayımlandığı Sayı

Yıl 2023 Cilt: 40 Sayı: 2

Kaynak Göster

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

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