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

S. Benzer, R. Benzer
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引用次数: 1

Abstract

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 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), 2.57g (0.53 - 7.50 g), and 0.790 (0.170-1.520) for all individuals. The length-weight relationships were determined W=0.00001437L2.8602 for female, W=0.00001570L2.8266 for male and W=0.00001328L2.8717 for all individuals. The von Bertalanffy equations were determined Lt=136.218 [1-e(-0.240(t+0.51))] for female, Lt=155.042 [1-e(-0.185(t+0.73))] for male, and Lt=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, R 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.
大型砂冶炼厂的传统和人工神经网络生长参数分析方法(Atherina boyeri Risso, 1810)
本研究采用传统的长度权重关系(LWRs)、von Bertalanffy (VB)、条件因子(CF)和现代方法(人工神经网络- ANNs)确定了İznik湖大型沙熔体(Atherina boyeri Risso, 1810)的生长参数。在2018年4月至2019年4月的狩猎季节,从当地渔民那里采集了635只标本,其中雌性44.84%,雄性55.16%。所有个体的平均叉长(FL) (mm, min-max)、平均W (g, min-max)和平均CF (value, min-max)分别为67.31 mm (40.10 ~ 97.77 mm)、2.57g (0.53 ~ 7.50 g)和0.790(0.170 ~ 1.520)。女性的长度-权重关系W=0.00001437L2.8602,男性的长度-权重关系W=0.00001570L2.8266,所有个体的长度-权重关系W=0.00001328L2.8717。雌性的von Bertalanffy方程为Lt=136.218 [1-e(-0.240(t+0.51))],雄性为Lt=155.042 [1-e(-0.185(t+0.73))],所有个体的Lt=146.916 [1-e(-0.205(t+0.64))]。用人工神经网络计算得到训练数据(均方误差MSE 4.52559e-5,相关系数R 9.09347e-1)、验证数据(MSE 4.86111e-5,相关系数R 9.00931e-1)和测试数据(MSE 3.391999e-5,相关系数R 9.43465e-1)的值。确定人工神经网络可以作为评估生长估计的替代方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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