What question did this study set out to answer?

The research aims to estimate and manage crayfish populations using machine learning and feature selection techniques.

March 25, 2026Scientific Reports0 citationsOpen Access

Modeling Feature Selection and Machine Learning for Crayfish Population Management

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

View Full Paper

Ask AI

Bookmark

Authors

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

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.

Gültepe et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark

March 25, 2026Scientific Reports0 citationsOpen Access

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

View Full Paper

Ask AI

Bookmark

Authors

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

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.

Gültepe et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark