When AI Meets Wildlife: Predicting Animal Migration from Habitat Cues

Elephant migration is essential for preserving biodiversity, but accurately predicting their movement patterns is challenging due to the influence of environmental, human, and ecological factors. This research introduces a machine learning-based approach to predict elephant migration routes between Bandipur National Park and Wayanad Wildlife Sanctuary. The study uses 34 months of historical data, including variables such as temperature, humidity, air quality, vegetation health, and water availability. The dataset underwent thorough preprocessing, including outlier handling, feature selection, and data balancing using SMOTE. Several machine learning models were tested, with Logistic Regression yielding the best results—achieving 94% accuracy—surpassing models like Random Forests, Decision Trees, Naive Bayes, Support Vector Machines, and Neural Networks. The analysis identified important environmental factors, such as seasonal water presence and temperature changes, as key triggers for migration. Additionally, hyperparameter tuning helped refine the models further. The findings show that predictive modeling can aid in wildlife conservation, minimize conflicts between humans and elephants, and inform environmental policy. Future developments will focus on integrating real-time tracking and expanding the range of ecological indicators to improve the model’s effectiveness in changing conditions.