[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-290788-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-290788-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","application-of-machine-learning-to-growth-model-in-fisheries-original-article","Application of machine learning to growth model in fisheries - Original Article","","Traditional growth models used in fishery science, including length-weight relationships (LWRs) and the von Bertalanffy (VB) growth function, can struggle to represent nonlinear patterns in biological data. This study applies machine learning to improve predictive accuracy by determining growth parameters of Eastern mosquitofish (Gambusia holbrooki) using traditional LWRs, VB, and multiple ML algorithms. VB showed slightly better length prediction (MAPE 2.734), while ML performed strongly with multilayer perceptron for length and SMO for weight prediction. ",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/application-of-machine-learning-to-growth-model-in-fisheries-original-article/290788/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/application-of-machine-learning-to-growth-model-in-fisheries-original-article/290788.png","ImageObject",300,407,{"name":42,"@type":43},"River Wang","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-17","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",8,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Which growth models are compared in this study?","Question",{"text":63,"@type":64},"The study compares traditional length-weight relationships (LWRs) and the von Bertalanffy (VB) growth function with machine learning algorithms.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How were the growth parameters of Eastern mosquitofish determined?",{"text":68,"@type":64},"Growth parameters were determined using traditional LWRs and VB equations, and also by applying machine learning algorithms to the same growth-related dataset.",{"name":70,"@type":61,"acceptedAnswer":71},"What is the reported prediction performance outcome between VB and ML?",{"text":72,"@type":64},"Prediction performance for length was satisfactory for both approaches, with the VB model showing slightly superior length prediction (MAPE 2.734). The ML algorithms delivered better results for specific prediction tasks, including multilayer perceptron for length and Sequential Minimum Optimization (SMO) for weight.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},290788,1789642575,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":112,"slug":113},30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":55,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":80,"read_time":144,"preview_page_count":4},1099514067438,"https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542","Journal of Fisheries  \nVolume 13 Issue 2 August 2025 Peer Reviewed | Open Access  \neISSN 2311-3111 pISSN 2311-729X  \n DOI: [https://doi.org/10.17017/j.fish.722](https://doi.org/10.17017/j.fish.722)   Original Article   \nApplication of machine learning to growth model in fisheries  \nSemra Benzer1 • Recep Benzer2 • Ali Gül3  \n1 Gazi Faculty of Education, Gazi University, Teknikokullar, Ankara 06500, Turkey  \n2 School of Administrative and Social Sciences, Department of Management Information System, Ankara Medipol University, Ankara 06050, Turkey  \n3 Education Faculty, Biology Education, Gazi University, Ankara 06050, Turkey  \nCorrespondence  \nSemra Benzer; Gazi Faculty of Education, Gazi University, Teknikokullar, Ankara 06500, Turkey  \n [sbenzer@gazi.edu.tr](sbenzer@gazi.edu.tr) and [sbenzer@gmail.com](sbenzer@gmail.com)  \nManuscript history  \nReceived 12 July 2024 | Accepted 17 March 2025 | Published online 10 May 2025  \nCitation  \nBenzer S, Benzer R, Gül A (2025) Application of machine learning to growth model in fisheries. Journal of Fisheries 13 (2):  \n132202. DOI: [10.17017/j.fish.722](10.17017/j.fish.722)  \n\n| Abstract |  |\n| --- | --- |\n| 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 L 2.8849 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 model and ML algorithms) 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.\u003Cbr>Keywords: artificial neural networks; Eastern mosquitofish; growth parameters; length-weight relationships; von Bertalanffy |  |\n| 1 | INTRODUCTION\u003Cbr>Growth is an important factor influencing the timing of | studies (direct observation, tagging and marking, and back-calculation of lengths or weights) . Length-weight |\n| sexual maturation, reproduction, production, and fishing | relationships (LWR) emerge as an important tool in the |\n| arrangements (Beaudouin et al. 2008) . Growth is unique | science and management of fisheries (Xiong et al. 2015) . |\n| to each fish in this process (Xiong et al. 2015) . The growth | Although traditional growth models have been wide- |\n| of a fish population is related to stock abundance, food | ly used to estimate fish length-weight relationships, their |\n| source, area, temperature, growing season, and other | limitations in capturing nonlinear growth patterns remain |\n| environmental factors in natural waters (Tesch 1968) . | challenging. Despite the growing interest in machine |\n| Length-weight data is an important data bank in popula- | learning (ML) techniques, a systematic comparison be- |\n| tion calculations in the backward calculation of length or | tween traditional and ML-based growth models in fisher- |\n| weights from the methods used in freshwater growth | ies remains scarce. Traditional growth models, such as the |\n\n[","cbCaiiOYcb52xqI4","https://ap.wps.com/l/cbCaiiOYcb52xqI4","pdf",2030588,11,"English","# Introduction\n## Growth in fisheries and relevance of LWR\n## Limitations of traditional models\n## Rationale for comparing ML with traditional approaches\n# Materials and Methods\n## Data source and study species background\n## Modeling strategies (LWR, VB, and ML)","[{\"question\":\"Which growth models are compared in this study?\",\"answer\":\"The study compares traditional length-weight relationships (LWRs) and the von Bertalanffy (VB) growth function with machine learning algorithms.\"},{\"question\":\"How were the growth parameters of Eastern mosquitofish determined?\",\"answer\":\"Growth parameters were determined using traditional LWRs and VB equations, and also by applying machine learning algorithms to the same growth-related dataset.\"},{\"question\":\"What is the reported prediction performance outcome between VB and ML?\",\"answer\":\"Prediction performance for length was satisfactory for both approaches, with the VB model showing slightly superior length prediction (MAPE 2.734). The ML algorithms delivered better results for specific prediction tasks, including multilayer perceptron for length and Sequential Minimum Optimization (SMO) for weight.\"}]","Application of machine learning to growth model in fisheries - Original Article | PDF",28]