Application of Artificial Neural Networks (ANNs) for Weight Predictions of Blue Crabs (Callinectes sapidus RATHBUN, 1896) Using Predictor Variables
An evaluation of the performance of artificial networks (ANNs) to estimate the weights of blue crab (Callinectes sapidus) catches in Yumurtalık Cove (Iskenderun Bay) that uses measured predictor variables is presented, including carapace width (CW), sex (male, female and female with eggs), and sampling month. Blue crabs (n=410) were collected each month between 15 September 1996 and 15 May 1998. Sex, CW, and sampling month were used and specified in the input layer of the network. The weights of the blue crabs were utilized in the output layer of the network. A multi-layer perception architecture model was used and was calibrated with the Levenberg Marguardt (LM) algorithm. Finally, the values were determined by the ANN model using the actual data. The mean square error (MSE) was measured as 3.3, and the best results had a correlation coefficient (R) of 0.93. We compared the predictive capacity of the general linear model (GLM) versus the Artificial Neural Network model (ANN) for the estimation of the weights of blue crabs from independent field data. The results indicated the higher performance capacity of the ANN to predict weights compared to the GLM (R=0.97 vs. R=0.95, raw variable) when evaluated against independent field data.
- How to Cite
TURELI BILEN, C., KOKCU, P., & IBRIKCI, T. (2011). Application of Artificial Neural Networks (ANNs) for Weight Predictions of Blue Crabs (Callinectes sapidus RATHBUN, 1896) Using Predictor Variables. Mediterranean Marine Science, 12(2), 439–446. https://doi.org/10.12681/mms.43
- Vol. 12 No. 2 (2011)
- Research Article
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution Non-Commercial License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g. post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (preferably in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Download data is not yet available.