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<p>Sustainable fishing and aquaculture production require not only data collection but also its proper application. Another factor influencing the study’s direction is dataset size. Regardless of dataset size, the most critical aspect of data use is feature selection. This study focuses on estimating sustainable fishing and aquaculture production by selecting relevant features and appropriate algorithms for both sexes and all individuals in the crayfish population living in the Atikisar Reservoir. For this purpose, the Mean method was used for data preprocessing, Pearson correlation analysis for feature selection, and Multiple Linear Regression (MLR), Support Vector Machine Regression (SVMR), Gradient Enhancement Regression (GBR), Sampled Sum (Bootstrap Combination, Bagging) Regression (BAR), Random Forest Regression (RFR), and k-Nearest Neighbors Regression (k-NNR) algorithms were used for prediction and stock management. Additionally, the data obtained from the population were evaluated within the framework of the classical length-weight relationship. The MLR, GBR, and RFR algorithms showed the highest coefficients of determination (R² = 0.98) for female individuals; the MLR, GBR, BAR, RFR, and k-NNR algorithms had the highest R² values (0.96) for male individuals; and the MLR, GBR, RFR, and k-NNR algorithms achieved the best coefficients of determination (R² = 0.98) for the entire population. Analyzing crayfish populations using the GBR algorithm will enable us to make predictions with the highest accuracy level and to manage populations, after processing the data with the Mean method from data preprocessing methods, and performing feature selection with Pearson correlation analysis. Regression analyses and machine learning algorithms, applied to overall and sexual length and weight of crayfish caught in the Atikhisar Reservoir, revealed positive allometric growth in the TL-TW and meat yield relationship for females, males, and the entire population.</p>

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A sample model for applying feature selection and machine learning techniques to estimate and manage crayfish populations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月22日

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  • Yasemin Gültepe,
  • Nejdet Gültepe

摘要

Sustainable fishing and aquaculture production require not only data collection but also its proper application. Another factor influencing the study’s direction is dataset size. Regardless of dataset size, the most critical aspect of data use is feature selection. This study focuses on estimating sustainable fishing and aquaculture production by selecting relevant features and appropriate algorithms for both sexes and all individuals in the crayfish population living in the Atikisar Reservoir. For this purpose, the Mean method was used for data preprocessing, Pearson correlation analysis for feature selection, and Multiple Linear Regression (MLR), Support Vector Machine Regression (SVMR), Gradient Enhancement Regression (GBR), Sampled Sum (Bootstrap Combination, Bagging) Regression (BAR), Random Forest Regression (RFR), and k-Nearest Neighbors Regression (k-NNR) algorithms were used for prediction and stock management. Additionally, the data obtained from the population were evaluated within the framework of the classical length-weight relationship. The MLR, GBR, and RFR algorithms showed the highest coefficients of determination (R² = 0.98) for female individuals; the MLR, GBR, BAR, RFR, and k-NNR algorithms had the highest R² values (0.96) for male individuals; and the MLR, GBR, RFR, and k-NNR algorithms achieved the best coefficients of determination (R² = 0.98) for the entire population. Analyzing crayfish populations using the GBR algorithm will enable us to make predictions with the highest accuracy level and to manage populations, after processing the data with the Mean method from data preprocessing methods, and performing feature selection with Pearson correlation analysis. Regression analyses and machine learning algorithms, applied to overall and sexual length and weight of crayfish caught in the Atikhisar Reservoir, revealed positive allometric growth in the TL-TW and meat yield relationship for females, males, and the entire population.