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

In this study, the growth parameters of big-scale sand smelt (Atherina boyeri Risso, 1810) in İznik 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 635 specimens (44.84% female 55.16% male) were collected from local fisherman during hunting season between April 2018 to 2019. Mean fork length (FL) (mm, min-max), mean W (g, min-max) CF (value, estimated as 67.31 mm (40.10 97.77 mm), 2.57g (0.53 7.50 g), 0.790 (0.170-1.520) for all individuals. The length-weight W=0.00001437L2.8602 female, W=0.00001570L2.8266 male W=0.00001328L2.8717 equations Lt=136.218 [1-e(-0.240(t+0.51))] Lt=155.042 [1-e(-0.185(t+0.73))] male, Lt=146.916 [1-e(-0.205(t+0.64))] values training (MSE (Mean Squared Error) 4.52559e-5, R (correlation coefficients) 9.09347e-1), verification 4.86111e-5, 9.00931e-1) test data 3.391999e-5, 9.43465e-1) found calculations made ANNs. It was that ANNs could be an alternative evaluating estimation.