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March 25, 2026Scientific ReportsOpen Access

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

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Authors

YGYasemin GültepeNGNejdet Gültepe

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Overview

Evaluates sustainable crayfish populations using feature selection and machine learning techniques, highlighting implications for aquaculture.

Key Points

  • The research aims to estimate and manage crayfish populations using machine learning and feature selection techniques.
  • Data preprocessing using the Mean method.
  • Feature selection conducted through Pearson correlation analysis.
  • Predictions made using multiple regression and machine learning algorithms (MLR, SVMR, GBR, BAR, RFR, k-NNR).
  • Length-weight relationships analyzed for crayfish population sustainability.
  • MLR, GBR, and RFR algorithms had R² = 0.98 for female crayfish.
  • MLR, GBR, BAR, RFR, and k-NNR algorithms achieved R² = 0.96 for male crayfish.
  • MLR, GBR, RFR, and k-NNR showed R² = 0.98 for the entire crayfish population.
  • Positive allometric growth was revealed for the length-weight and meat yield relationship.

Cite This Study

Gültepe et al. (2026) studied this question.

synapsesocial.com/papers/69c37aa8b34aaaeb1a67c926https://doi.org/10.1038/s41598-026-45535-9
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