AI in Biodiversity Conservation: Predicting Species Habitats and Monitoring Threatened Ecosystems
Abstract
Biodiversity is under threat from habitat loss, climate change, and human activities. This study explores the use of AI to predict species distributions and monitor ecosystem changes, enabling conservationists to prioritize and protect vulnerable areas. Our model analyzes environmental factors, species data, and satellite imagery to predict suitable habitats and detect shifts in ecosystem health over time. Case studies with endangered species demonstrate the model’s effectiveness in identifying critical habitats and informing conservation strategies, underscoring AI’s potential as a powerful tool in biodiversity preservation and sustainable ecosystem management.
References
Balantrapu, S. S. (2021). The Impact of Machine Learning on Incident Response Strategies. International Journal of Management Education for Sustainable Development, 4(4), 1-17.
Balantrapu, S. S. (2019). Adversarial Machine Learning: Security Threats and Mitigations. International Journal of Sustainable Development in Computing Science, 1(3), 1-18.
Pillai, S. E. V. S., Polimetla, K., Avacharmal, R., & Perumal, A. P. (2022). Mental health in the tech industry: Insights from surveys and NLP analysis. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING (JRTCSE), 10(2), 22-33.
Deekshith, A. (2022). Cross-Disciplinary Approaches: The Role of Data Science in Developing AI-Driven Solutions for Business Intelligence. International Machine learning journal and Computer Engineering, 5(5).
Deekshith, A. (2021). Data Engineering for AI: Optimizing Data Quality and Accessibility for Machine Learning Models. International Journal of Management Education for Sustainable Development, 4(4), 1-33.
Deekshith, A. (2023). Scalable Machine Learning: Techniques for Managing Data Volume and Velocity in AI Applications. International Scientific Journal for Research, 5(5).
Deekshith, A. (2020). AI-Enhanced Data Science: Techniques for Improved Data Visualization and Interpretation. International Journal of Creative Research In Computer Technology and Design, 2(2).
Deekshith, A. (2019). Integrating AI and Data Engineering: Building Robust Pipelines for Real-Time Data Analytics. International Journal of Sustainable Development in Computing Science, 1(3), 1-35.
Pillai, S. E. V. S., Polimetla, K., Avacharmal, R., Perumal, A. P., & Gopal, S. K. (2023). Beyond the Bin: Machine Learning-Driven Waste Management for a Sustainable Future. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING (JRTCSE), 11(1), 16-27.
Boppiniti, S. T. (2023). Data Ethics in AI: Addressing Challenges in Machine Learning and Data Governance for Responsible Data Science. International Scientific Journal for Research, 5(5).
Boppiniti, S. T. (2020). Big Data Meets Machine Learning: Strategies for Efficient Data Processing and Analysis in Large Datasets. International Journal of Creative Research In Computer Technology and Design, 2(2).
Boppiniti, S. T. (2021). Real-Time Data Analytics with AI: Leveraging Stream Processing for Dynamic Decision Support. International Journal of Management Education for Sustainable Development, 4(4).
Boppiniti, S. T. (2019). Machine Learning for Predictive Analytics: Enhancing Data-Driven Decision-Making Across Industries. International Journal of Sustainable Development in Computing Science, 1(3).
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
