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Computational Ecology and Software, 2017, 7(2): 82-90
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Article

Comparative growth models of big-scale sand smelt (Atherina boyeri Risso, 1810) sampled from Hirfanll Dam Lake, Klrsehir, Ankara, Turkey

S. Benzer1, R. Benzer2
1Gazi University, Ankara, Turkey
2National Defense University, Ankara, Turkey

Received 17 March 2017;Accepted 25 April 2017;Published 1 June 2017
IAEES

Abstract
In this current publication the growth characteristics of big-scale sand smelt data were compared for population dynamics within artificial neural networks and length-weight relationships models. This study aims to describe the optimal decision of the growth model of big-scale sand smelt by artificial neural networks and length-weight relationships models at Hirfanll Dam Lake, Klrsehir, Turkey. There were a total of 1449 samples collected from Hirfanll Dam Lake between May 2015 and May 2016. Both model results were compared with each other and the results were also evaluated with MAPE (mean absolute percentage error), MSE (mean squared error) and r2 (coefficient correlation) data as a performance criterion. The results of the current study show that artificial neural networks is a superior estimation tool compared to length-weight relationships models of big-scale sand smelt in Hirfanll Dam Lake.

Keywords growth model;length-weight relationships;artificial neural networks;big-scale sand smelt;Hirfanll Dam Lake.


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