Application of machine learning to growth model in fisheries

Abstract

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 L^2.8212, respectively. The VB equations were determined L_t=80.990 [1–e^{–0.990(t+0.208)}] for female and L_t=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.