Application of machine learning to growth model in fisheries
Traditional growth models, such as length-weight relationships (LWRs) and the von Bertalanffy (VB) growth function, have been widely used in fishery science. Their limitations in capturing nonlinear patterns necessitate alternative approaches. Machine learning (ML) techniques have recently gained a...
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BdFISH
2025-05-01
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| Series: | Journal of Fisheries |
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| Online Access: | https://journal.bdfish.org/index.php/fisheries/article/view/722 |
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| author | Semra Benzer Recep Benzer Ali Gül |
| author_facet | Semra Benzer Recep Benzer Ali Gül |
| author_sort | Semra Benzer |
| collection | DOAJ |
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Traditional growth models, such as length-weight relationships (LWRs) and the von Bertalanffy (VB) growth function, have been widely used in fishery science. Their limitations in capturing nonlinear patterns necessitate alternative approaches. Machine learning (ML) techniques have recently gained attention as a powerful tool for enhancing predictive accuracy in biological studies. In this study, the growth parameter of Eastern mosquitofish, Gambusia holbrooki (135 females: 21–58.78 mm and 0.152–3.424 g; 59 males: 19.25–43.20 mm; 0.108–1.075 g), was determined with traditional LWRs, VB, and machine learning algorithms. The LWRs growth equations of female and male individuals were W=0.00002102 L2.8849 and W=0.00003064 L2.8212, respectively. The VB equations were determined Lt=80.990 [1–e–0.990(t+0.208)] for female and Lt=64.172 [1-e–0.610(t+0.271)] for male. In general, the performance of both methods (VB vs. ML) in predicting lengths, as measured by Mean Absolute Percentage Error (MAPE), was satisfactory, with the VB model demonstrating slightly superior performance (2.734). In addition, the ML algorithm gives better results in length data prediction with multilayer perceptron and in weight data prediction with Sequential Minimum Optimization (SMO) algorithm when ML algorithms are examined. The diverse ML algorithms positively impacted the investigations addressing growth-related issues in fisheries.
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| format | Article |
| id | doaj-art-1d8d05ab3bc84f8a95d6c6fb16f386f3 |
| institution | OA Journals |
| issn | 2311-729X 2311-3111 |
| language | English |
| publishDate | 2025-05-01 |
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| series | Journal of Fisheries |
| spelling | doaj-art-1d8d05ab3bc84f8a95d6c6fb16f386f32025-08-20T02:15:06ZengBdFISHJournal of Fisheries2311-729X2311-31112025-05-0113210.17017/j.fish.722Application of machine learning to growth model in fisheriesSemra Benzer0Recep Benzer1Ali Gül2Gazi Faculty of Education, Gazi University, Teknikokullar, Ankara 06500, TurkeySchool of Administrative and Social Sciences, Department of Management Information System, Ankara Medipol University, Ankara 06050, TurkeyEducation Faculty, Biology Education, Gazi University, Ankara 06050, Turkey Traditional growth models, such as length-weight relationships (LWRs) and the von Bertalanffy (VB) growth function, have been widely used in fishery science. Their limitations in capturing nonlinear patterns necessitate alternative approaches. Machine learning (ML) techniques have recently gained attention as a powerful tool for enhancing predictive accuracy in biological studies. In this study, the growth parameter of Eastern mosquitofish, Gambusia holbrooki (135 females: 21–58.78 mm and 0.152–3.424 g; 59 males: 19.25–43.20 mm; 0.108–1.075 g), was determined with traditional LWRs, VB, and machine learning algorithms. The LWRs growth equations of female and male individuals were W=0.00002102 L2.8849 and W=0.00003064 L2.8212, respectively. The VB equations were determined Lt=80.990 [1–e–0.990(t+0.208)] for female and Lt=64.172 [1-e–0.610(t+0.271)] for male. In general, the performance of both methods (VB vs. ML) in predicting lengths, as measured by Mean Absolute Percentage Error (MAPE), was satisfactory, with the VB model demonstrating slightly superior performance (2.734). In addition, the ML algorithm gives better results in length data prediction with multilayer perceptron and in weight data prediction with Sequential Minimum Optimization (SMO) algorithm when ML algorithms are examined. The diverse ML algorithms positively impacted the investigations addressing growth-related issues in fisheries. https://journal.bdfish.org/index.php/fisheries/article/view/722artificial neural networksEastern mosquitofishgrowth parameterslength-weight relationshipsvon Bertalanffy |
| spellingShingle | Semra Benzer Recep Benzer Ali Gül Application of machine learning to growth model in fisheries Journal of Fisheries artificial neural networks Eastern mosquitofish growth parameters length-weight relationships von Bertalanffy |
| title | Application of machine learning to growth model in fisheries |
| title_full | Application of machine learning to growth model in fisheries |
| title_fullStr | Application of machine learning to growth model in fisheries |
| title_full_unstemmed | Application of machine learning to growth model in fisheries |
| title_short | Application of machine learning to growth model in fisheries |
| title_sort | application of machine learning to growth model in fisheries |
| topic | artificial neural networks Eastern mosquitofish growth parameters length-weight relationships von Bertalanffy |
| url | https://journal.bdfish.org/index.php/fisheries/article/view/722 |
| work_keys_str_mv | AT semrabenzer applicationofmachinelearningtogrowthmodelinfisheries AT recepbenzer applicationofmachinelearningtogrowthmodelinfisheries AT aligul applicationofmachinelearningtogrowthmodelinfisheries |