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Artificial neural networks approach in length-weight relation of crayfish (Astacus leptodactylus Eschscholtz, 1823) in E?irdir Lake, Isparta, Turkey
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作者 Semra Benzer Recep Benzer aysel caglan günal 《Journal of Coastal Life Medicine》 CAS 2017年第8期330-335,共6页
Objective: To analyze the growth prediction results of crayfish (Astacus leptodactylus Eschscholtz 1823) with two methods of length-weight relation (LWR) and artificial neural networks (ANNs). Methods: We examine the ... Objective: To analyze the growth prediction results of crayfish (Astacus leptodactylus Eschscholtz 1823) with two methods of length-weight relation (LWR) and artificial neural networks (ANNs). Methods: We examine the relationships between total length and total weight;carapace length and total weight;carapace length and total length for Astacus leptodactylus caught from E?irdir Lake between 2013 and 2014. Length weight relation is used as a traditional method and artificial neural networks as a new approach. Results: The research is based on a sample of 222 crayfish (34% (75 individual) female, 66%(147 individual) male)The outcomes of the research can be summarized as follows: average total length is 128.40 mm for female and 135.50 mm for male;average total weight is 59.79 g for female and 82.95 g for male crayfish. LWR equation was found to be W = 0.05425196 L2.73 for females, W = 0.05272102 L2.81 for males, and W = 0.03589889 L2.94 for the entire sample, regardless of gender. The results acquired from ANNs and LWR are analyzed to those obtained by the growth rate of crayfish caught from E?irdir Lake. Conclutions: LWR and ANNs mean absolute percentage error results were examined. ANNs provide better results than the LWR. ANNs can be considered as an alternative for growth estimation. 展开更多
关键词 Relationship Neural networks Forecasting CRAYFISH
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