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- MakaleAçık Erişim2019Scopus SJRQ3Artificial Neural Networks (a new statistical approach) method in length-weight relationships of Alburnus Mossulensis in Murat River (Palu-Elaziğ) Turkey
Ozcan, E.I.
Artificial Neural Networks (ANNs) is a reliable and alternative method used specially in the growth characteristics of some fish species. The purpose of this work was to compare with length-weight relationships and Artificial Neural Networks, the length-weight relation of 415 (232 female and 183 male) Alburnus mossulensis captured from Murat River between January 2018 and December 2018. The total length and the weight of the fish changed between 7.2-19.9 cm (12.81±0.095) and 3.24-40.6 g (13.51±0.30) respectively. The length-weight relationships were determined for females, males and all individuals as W=0.0097L2.812 (R2=0.95), W=0.0168L2.599 (R2=0.95) and W=0.0130L2.701 (R2=0.95), respectively. The growth type of A. mossulensis was negative allometric growth for female, male and all individuals. Regression analysis showed that length has high significantly correlation with weight (R = 0.97, R2 = 0.95, F1,413 = 79550.3, P <0.001) and 95% remain in weight was due to length remain. t-test results was used for the importance condition of the regression coefficients (t=89.193, P<0.01). Length-length relations were highly significant with R2 values greater than 0.97. The condition factor values were ranged between 0.430 and 0.975 for all individuals. According to the comparison of the results obtained with MAPE (%), ANNs provided better results than the LWRs. With this study, it can be seen that ANNs can be used as an alternative useful method for population parameters prediction.
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İncele - MakaleYalnız künye2018Scopus SJRQ3ARTIFICIAL NEURAL NETWORKS AS NEW ALTERNATIVE METHOD TO ESTIMATING SOME POPULATION PARAMETERS OF TIGRIS LOACH (OXYNOEMACHEILUS TIGRIS (HECKEL, 1843)) IN THE KARASU RIVER, TURKEY
Ozcan, Ebru Ifakat; Serdar, Osman
In this study, a new method (Artificial Neural Networks) to estimating some population parameters of Tigris loach (Oxynoemacheilus tigris (Heckel, 1843)) collected between 2014 and 2015 in 14 different stations from Karasu River (East Anatolia, Turkey). The total length and weight of the species ranged from 62 to105 mm and from 1.7 to 10.9 g for females and 65 to 100 mm and from 2.4 to 9.8 g for males, respectively. The new maximum length for O. tigris was found to be 105 mm in this study. The length-weight relation of O. tigris was carried out with artificial neural networks (ANNs) and length-weight relationships (LWRs). The results obtained were compared. Length-weight relationships of O. tigris were found as W=0.0049*L-3.29 , R-2 =0.95 for all individuals. According to these values, the growth type of this species was positive allometric (b>3) for all individuals. Regression analysis showed that fish length has high significant correlation with weight (R=0.97, R-2 =0.95, F-1,F-185=31930.3, P<0.001). According to the comparison of the results obtained with MAPE (%), ANNs provide better results than the LWRs. The results of this study could give useful insight that ANNs can be evaluated as an alternative for length-weight relation estimation. The condition factor values (min-max) were observed as 0.680-1.239 for females and 0.699-1.074 for males. No studies have been done before on this species population parameters.
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