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 Alburnus sellal 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...
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Sciendo
2024-06-01
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| Series: | Oceanological and Hydrobiological Studies |
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| Online Access: | https://doi.org/10.26881/oahs-2024.2.06 |
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| author | Ozcan Ebru Ifakat |
| author_facet | Ozcan Ebru Ifakat |
| author_sort | Ozcan Ebru Ifakat |
| collection | DOAJ |
| description | In this study, predictions were made on the growth performance of Alburnus sellal 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 = 0.0046L3.198 for all individuals. The von Bertalanffy growth parameters were calculated for all individuals: Lt = 21.93 [1 – e–0.158 (t + 2.11)]; Wt = 102.29 [1 – e–0.158 (t + 2.11)]3.198. 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 A. sellal. 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. |
| format | Article |
| id | doaj-art-6a4363fef1fc4ee4861a1e612140e739 |
| institution | Kabale University |
| issn | 1897-3191 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Oceanological and Hydrobiological Studies |
| spelling | doaj-art-6a4363fef1fc4ee4861a1e612140e7392025-01-20T11:10:24ZengSciendoOceanological and Hydrobiological Studies1897-31912024-06-0153215316310.26881/oahs-2024.2.06Performance of artificial neural networks and traditional methods in determining selected growth parameters of Alburnus sellal Heckel, 1843Ozcan Ebru Ifakat0Munzur University Faculty of Fisheries, 62100 Tunceli, TurkiyeIn this study, predictions were made on the growth performance of Alburnus sellal 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 = 0.0046L3.198 for all individuals. The von Bertalanffy growth parameters were calculated for all individuals: Lt = 21.93 [1 – e–0.158 (t + 2.11)]; Wt = 102.29 [1 – e–0.158 (t + 2.11)]3.198. 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 A. sellal. 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.https://doi.org/10.26881/oahs-2024.2.06alburnus sellalartificial neural networksgrowth parametersmape (%)munzur river |
| spellingShingle | Ozcan Ebru Ifakat Performance of artificial neural networks and traditional methods in determining selected growth parameters of Alburnus sellal Heckel, 1843 Oceanological and Hydrobiological Studies alburnus sellal artificial neural networks growth parameters mape (%) munzur river |
| title | Performance of artificial neural networks and traditional methods in determining selected growth parameters of Alburnus sellal Heckel, 1843 |
| title_full | Performance of artificial neural networks and traditional methods in determining selected growth parameters of Alburnus sellal Heckel, 1843 |
| title_fullStr | Performance of artificial neural networks and traditional methods in determining selected growth parameters of Alburnus sellal Heckel, 1843 |
| title_full_unstemmed | Performance of artificial neural networks and traditional methods in determining selected growth parameters of Alburnus sellal Heckel, 1843 |
| title_short | Performance of artificial neural networks and traditional methods in determining selected growth parameters of Alburnus sellal Heckel, 1843 |
| title_sort | performance of artificial neural networks and traditional methods in determining selected growth parameters of alburnus sellal heckel 1843 |
| topic | alburnus sellal artificial neural networks growth parameters mape (%) munzur river |
| url | https://doi.org/10.26881/oahs-2024.2.06 |
| work_keys_str_mv | AT ozcanebruifakat performanceofartificialneuralnetworksandtraditionalmethodsindeterminingselectedgrowthparametersofalburnussellalheckel1843 |