When AI Meets Wildlife: Predicting Animal Migration from Habitat Cues
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This preprint studied elephant migration prediction between Bandipur National Park and Wayanad Wildlife Sanctuary using a machine learning approach trained on 34 months of historical environmental and habitat-related data (temperature, humidity, air quality index, vegetation index, and water availability index). After outlier removal, feature selection, and data balancing with SMOTE, multiple algorithms were evaluated, with logistic regression achieving the best reported accuracy (94%) and identifying seasonal water availability and temperature variation as key environmental triggers. The authors describe the work as a preprint and note future steps to add real-time tracking and additional ecological factors, implying limitations in current modeling scope and robustness to dynamic conditions. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
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Abstract
Abstract 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.
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This study develops a machine learning model to forecast elephant migration between Bandipur National Park and Wayanad Wildlife Sanctuary by analyzing 34 months of historical data incorporating features like temperature, humidity, air quality index, vegetation index, and water availability index. After extensive data preprocessing, including outlier removal, feature selection, and data balancing using SMOTE, multiple machine learning algorithms were evaluated. Logistic Regression achieved the highest performance, with an accuracy of 94%, outperforming Decision Trees, Random Forests, Support Vector Machines, Naive Bayes, and Neural Networks. Exploratory data analysis revealed key environmental triggers influencing migration, such as seasonal water availability and temperature variations. Hyperparameter tuning further optimized model performance. The results demonstrate that predictive analytics can enhance conservation strategies, reduce human-elephant conflict, and support policy-making for habitat protection. Future work aims to incorporate real-time tracking and additional ecological factors to further improve model robustness and applicability in dynamic environments. Animal Science Artificial Intelligence and Machine Learning Elephant Migration Machine Learning Predictive Modeling Wildlife Conservation Environmental Factors Bandipur National Park Wayanad Wildlife Sanctuary Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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