Agricultural Research,Education and Extension OrganizationIranian Journal of Fisheries Sciences1562-291619220200301Estimation of body weight of Sparus aurata with artificial neural network (MLP) and M5P (nonlinear regression)–LR algorithms54155011701010.22092/ijfs.2018.117010ENLevent SangünUniversity of ÇukurovaO.İ GüneyPelin ÖzalpNuri BaşustaJournal Article20170224In this study, morphometric features such as total length, standard length, and fork length obtained from a total of 321 Sparus aurata species, including 164 females and 157 males, captured between 2012-2013 from İskenderun Bay were used as input value, while weight was used as an output value. The Artificial Neural Network (MLP-Multi Layer Perceptron) as well as the M5P algorithm and Linear Regression (LR) algorithm from version 3.7.11 of the WEKA Program were applied. When coefficients of correlation were assessed, the MLP algorithm for males, females and the total were calculated as 0.9686, 0.9605 and 0.9663, respectively; the M5P algorithm for males, females and the total were calculated as 0.9722, 0.9596 and 0.9735, respectively; and the LR Model for males, females and the total were calculated as 0.9777, 0.9498 and 0.9473, respectively. With respect to the Mean Absolute Error (MAE) calculations, the MLP algorithm MAE values for males, females and the total were calculated as 2.94, 2.57 and 2.7074, respectively; the M5P algorithm MAE values for males, females and the total were calculated as 2.400, 2.641 and 2.157, respectively; and the LR Model MAE values for males, females and the total were calculated as 3.217, 2.811 and 3.11, respectively. It can also be concluded from the study that, in order to predict ANN interactions Nonlinear Regression model is more effective and access a better performance than the conventional models.<br /> https://jifro.areeo.ac.ir/article_117010_b8ea2571d6bb42e9bae8697b28409129.pdf