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Decoding Wildlife Habitat Shifts in a Changing Environment Through Machine Learning

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  • Vishal Shah

Abstract

Climate change affects wildlife habitats. Data analytics and machine learning have been used in this study, which focused on different ecosystems in the state of Virginia. In this paper, we propose a holistic approach that integrates climate data from the Open-Meteo API and wildlife observations from the iNaturalist API during the years 2020-2023 using real-time data collection, machine learning models, and interactive visualization techniques. Our approach uses ensembling machine learning methods such as XGBoost, Random Forest, and Gradient Boosting classifiers with 85% accuracy using only climate variables as predictors of wildlife presence. Among them, very strong correlations between temperature patterns and observations of wildlife were found (r = 0.72, p

Suggested Citation

  • Vishal Shah, 2025. "Decoding Wildlife Habitat Shifts in a Changing Environment Through Machine Learning," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(1), pages 176-184, February.
    • Handle: RePEc:jbh:ijsrcs:v11:y2025:i1:id:667
    DOI: 10.32628/CSEIT25111224
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25111224
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