Performance of artificial neural networks and traditional methods in determining selected growth parameters of Alburnus sellal Heckel, 1843

In this study, predictions were made on the growth performance of Heckel, 1843 from the Munzur River using back propagation artificial neural networks and ANN algorithms. Statistical growth models used in fish biology and results obtained from artificial neural networks were compared. A total of 239...

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Bibliographic Details
Published in:Oceanological and hydrobiological studies 2024-06, Vol.53 (1), p.153-163
Main Author: Ozcan, Ebru Ifakat
Format: Article
Language:English
Subjects:
Age groups
Alburnus sellal
Algorithms
Artificial neural networks
Back propagation networks
Biological effects
Chronology
Condition factor
Effectiveness
Error analysis
Fish
Growth models
growth parameters
Length-weight relationships
MAPE (%)
Munzur River
Neural networks
Parameters
Performance indices
Performance prediction
Population (statistical)
Population studies
Statistical analysis
Statistical models
Sustainable fisheries
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Summary:In this study, predictions were made on the growth performance of Heckel, 1843 from the Munzur River using back propagation artificial neural networks and ANN algorithms. Statistical growth models used in fish biology and results obtained from artificial neural networks were compared. A total of 239 samples were used in this comparison. It was determined that the population is distributed across age groups 0–VII. The relationship between length and weight was calculated as W = 0.0046L for all individuals. The von Bertalanffy growth parameters were calculated for all individuals: L = 21.93 [1 – e ]; W = 102.29 [1 – e . The growth performance index (Ф’) value was 1.880 for all individuals. The condition factor varied between 0.479 and 1.115 for females and between 0.533 and 1.076 for males. The Mean Absolute Percent Error (MAPE) statistic was used, which is a widely used method to measure the accuracy of the predictions made. It was determined that ANNs MAPE (%) values were better than MAPE values calculated for the length–weight relationship and von Bertalanffy growth function models for . This study shows that ANNs can be used as an alternative useful method for predicting population parameters. ANN models are therefore an effective tool to describe fish growth parameters. They have been found to be a useful predictive tool. The developed models can be used to predict future sustainable fish management.
ISSN:1730-413X
1897-3191
DOI:10.26881/oahs-2024.2.06