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Artificial Neural Networks approach in morphometric analysis of crayfish (Astacus leptodactylus) in Hirfanlı Dam Lake

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Abstract

This study aims to compare the growth estimation of narrow-clawed crayfish (Astacus leptodactylus Eschscholtz, 1823) obtained from two methods which are length-weight relations and Artificial Neural Networks (ANNs) from Hirfanlı Dam Lake in 2013 and 2014. The growth estimation of 325 crayfish was carried out with both methods and the obtained results were compared. Then, the estimated values found via both methods were examined. Correlation coefficient (r), 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 compared to linear regression models, ANNs is a superior estimation tool. Thus, as an outcome of the present study, ANNs can be considered as a more efficient method especially in the growth estimation of the species in biological systems. Another outcome of this study is that crayfish of Hirfanlı Dam 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.

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Authors and Affiliations

  1. Gazi Faculty of Education, Gazi University, 06500, Teknikokullar, Ankara, Turkey

    Semra Benzer

  2. Department of Computer Engineering, National Defense University, 06654, Ankara, Turkey

    Recep Benzer

  3. Institute of Natural and Applied Sciences, Gazi University, 06500, Teknikokullar, Ankara, Turkey

    Aysel Çağlan Günal

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  1. Semra Benzer
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  2. Recep Benzer
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Benzer, S., Benzer, R. & Günal, A.Ç. Artificial Neural Networks approach in morphometric analysis of crayfish (Astacus leptodactylus) in Hirfanlı Dam Lake. Biologia 72, 527–535 (2017). https://doi.org/10.1515/biolog-2017-0052

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