Mathematical Modeling and Machine Learning for Predicting Shade-Seeking Behavior in Cows Under Heat Stress
Journal:
arXiv
Published Date:
Jan 9, 2025
Abstract
In this paper we develop a mathematical model combined with machine learning
techniques to predict shade-seeking behavior in cows exposed to heat stress.
The approach integrates advanced mathematical features, such as time-averaged
thermal indices and accumulated heat stress metrics, obtained by mathematical
analysis of data from a farm in Titaguas (Valencia, Spain), collected during
the summer of 2023. Two predictive models, Random Forests and Neural Networks,
are compared for accuracy, robustness, and interpretability. The Random Forest
model is highlighted for its balance between precision and explainability,
achieving an RMSE of $14.97$. The methodology also employs $5-$fold
cross-validation to ensure robustness under real-world conditions. This work
not only advances the mathematical modeling of animal behavior but also
provides useful insights for mitigating heat stress in livestock through
data-driven tools.
