Developing an Artificial Neural Network (ANN) to Estimate Growth Model of Narrow-Clawed Crayfish (Pontastacus leptodactylus) in Yenice Reservoir (Çanakkale, Turkiye)

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dc.contributor.authorKale, Semih
dc.contributor.authorBerber, Selcuk
dc.date.accessioned2025-05-29T02:57:20Z
dc.date.available2025-05-29T02:57:20Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractThis study aims to develop an artificial neural network (ANN) to estimate the growth model of the narrow-clawed crayfish (Pontastacus leptodactylus). A total of 546 (255 males and 291 females) narrow-clawed crayfish individuals were collected monthly between July 2007 and June 2008 by using fyke nets (34 mm mesh size) from Yenice Reservoir, & Ccedil;anakkale, T & uuml;rkiye. Total length (TL) and total weight (TW) were measured, and the relationship between TL and TW was modeled using both the traditional length-weight relationship (LWR) and ANN approaches. The performance of both models was evaluated, and the ANN developed in this study yielded superior results when compared to the traditional LWR method. The R-value was found 0.95077. This value indicates that the model developed using ANN provides better results than traditional growth forecasting models. The present study demonstrates that ANNs can be used as a novel and effective approach to estimating the growth of narrow-clawed crayfish. The ANN approach can provide useful information for sustainable and successful fisheries management.
dc.description.sponsorshipCanakkale Onsekiz Mart University the Scientific Research Coordination Unit [FHD-2020-3273]
dc.description.sponsorshipThis study was financially supported by Canakkale Onsekiz Mart University the Scientific Research Coordination Unit (Project number: FHD-2020-3273) . An earlier version of this paper was presented at the 6th International Eurasian Conference on Biological and Chemical Sciences in Ankara, Tuerkiye, October 2023. The authors would like to thank their colleagues for their support during the fieldwork.
dc.identifier.doi10.4314/mejs.v17i1.6
dc.identifier.endpage98
dc.identifier.issn2073-073X
dc.identifier.issue1
dc.identifier.scopusqualityN/A
dc.identifier.startpage82
dc.identifier.urihttps://doi.org/10.4314/mejs.v17i1.6
dc.identifier.urihttps://hdl.handle.net/20.500.12428/30010
dc.identifier.volume17
dc.identifier.wosWOS:001425110000006
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherMekelle Univ, Coll Natural & Computational Sciences
dc.relation.ispartofMomona Ethiopian Journal of Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250529
dc.subjectArtificial neural networks
dc.subjectCrayfish
dc.subjectLength-weight relationship
dc.titleDeveloping an Artificial Neural Network (ANN) to Estimate Growth Model of Narrow-Clawed Crayfish (Pontastacus leptodactylus) in Yenice Reservoir (Çanakkale, Turkiye)
dc.typeArticle

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