Comparison between traditional models and artificial neural networks as estimators of the growth of the Tigris scraper Capoeta umbla (Teleostei: Cyprinidae) in the Munzur River, Turkey

In this study, a comparison of traditional growth methods (length-weight relationships and von Bertalanffy growth function) with artificial neural networks in growth models was carried out in the growth of 783 specimens of Capoeta umbla from the Munzur River, Turkey from September 2019 to May 2021. The length-weight relationships of C. umbla W = 0.0085L3.013 R2=0.943 was determined for all individuals. The ages of the specimens were from 0 to 11 years old. The von Bertalanffy growth function was Lt = 46.15 [1-e-0.139 (t + 2.57)] and Wt = 856.32 [1-e-0.139 (t + 2.57)]3.013 for all individuals. Ф' value was 2.471 all individuals. The training stopped and the best validation performance was fixed at 8.1473 × 10-5 at epoch 42. The validation checks were reached as 6, at epoch 48 and the gradient = 5.6566 × 10-5, at epoch 48. The target output R value was 0.98584 for training, 0.98969 for validation, 0.98757 for testing and 0.9868 for all. The calculated MAPE values were 0.140 and 0.578 for artificial neural networks, 1.168 and 2.726 for length–weight relationships, 5.721 and 4.013 for von Bertalanffy growth function, respectively. The calculated SSE values for length and weight were 0.0128 and 30.864 for artificial neural networks, 1.3985 and 350.786 for length–weight relationships. The results of the present show that artificial neural networks can be superior estimators than length–weight relationships and von Bertalanffy growth function. Therefore, artificial neural networks models are an effective tool to describe body weight and length in fish.