New Perspectives for Predicting Growth Properties of Crayfish (Astacus leptodactylus Eschscholtz, 1823) in Uluabat Lake

This study aims to compare the growth estimation result of the two methods which are Length - Weight Relations and Artificial Neural Networks from Uluabat Lake regarding narrow-clawed crayfish (Astacus leptodactylus Eschscholtz 1823) between 2015 and 2016. The relationships between total length (TL) and total weight (TW); carapace length (CL) for Astacus leptodactylus caught from Uluabat Lake were presented with tradional method of length-weight relation (LWR) Equation and Artificial Neural Networks (ANNs) method. The growth estimation of 540 crayfish was carried out with both methods and the obtained results were compared. Then, the estimated values found via both examined methods. Coefficient correlation (r(2)), sum square error (SSE), mean absolute percentage error performance criteria (MAPE) were used for comparison of artificial neural network and linear regression models goodness of fit. The results of the current study show that ANNs can be a superior estimation tool compared to LWR equation. Thus, as an outcome of the present study, ANNs can be considered as a finer method especially in the growth estimation of the species in biological systems. Another outcome of this study is that crayfish of Uluabat Lake well accommodates itself to the ecologic features of the environment and so its growth features are similar to the values of other water systems