Artificial neural networks and genetic algorithm approach to determine length-weight, length frequency relationships of Lessepsian crab, Charybdis (Goniohellenus) longicollis, Leene, 1938 in the Iskenderun Bay, Turkey

Begüm ÇIĞŞAR, Deniz ÜNAL, Canan TÜRELİ

Abstract

Mathematical models are created to have information about growth of living beings. Biometric models such as length-frequency and weight-length are also distributions that give information about growth of livings. In this study, the growth of Charybdis (Goniohellenus) longicollis Leene, 1938 is given by von Bertalanffy growth curve, which is a continuous growth model, using length frequency (Carapace Width) data. The TropFishR package program in the R program is used to estimate the von Bertalanffy growth curve. Electronic Length Frequency Analysis of Response Surface Analysis and Genetic Algorithm methods included in this package program are applied, and both methods are compared according to the Rn max value. As a result, the Rn max values of the estimated von Bertalanffy parameters for both methods are found to be equal.

In addition to classical linear regression method, Artificial Neural Networks method is used to estimate the weight-length relationship of the species. The Artificial Neural Networks (ANNs) method is presented by two models. In the first ANNs model, CW was used to estimate the weight. In the second model, age was added, which was estimated in the first part of this study, to the first ANNs model.

Mean Squared Error,  and Mean Absolute Percentage Error criteria are taken into account when comparing the three models used in weight estimation. It is seen that the Artificial Neural Networks model with the age variable added to the weight estimation has the best performance.

Keywords

Artificial Neural Networks, Genetic Algorithm, Growth, Length-weight relationship

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