Analysis and Prediction of Bird Migration Using RFA and GBR

Abstract

The project "Analysis and Prediction of Bird Migration Using RFA and GBR" utilizes an extensive eBird dataset with approximately 100,000 observations to analyze and forecast bird migration patterns. The primary goals involved data collection, preprocessing, and feature extraction, followed by the progression of predictive models Gradient Boosting in use and Random Forest algorithms. Data preprocessing encompassed cleaning, normalization, and encoding to ensure the dataset’s quality and relevance. Through rigorous training and validation, our models demonstrated high accuracy and precision. The resulting analysis uncovered notable migration patterns and seasonal trends that correspond to known bird behaviors. These findings highlight the capabilities of artificial intelligence in deepening our knowledge of bird migration and contributing to effective conservation efforts. It also highlights the value of incorporating additional environmental data to further refine predictive models, offering a robust framework for analyzing and predicting migration patterns and other ecological phenomena. Furthermore, the project demonstrated the critical role of machine learning in environmental science, demonstrating how sophisticated algorithms can uncover complex ecological connections previously challenging to identify. The findings from This project could aid policymakers and conservationists in developing targeted conservation efforts, ensuring better protection of migratory bird species amidst changing climatic conditions. Future work could expand on this foundation by integrating real-time data streams and leveraging more sophisticated ensemble methods to enhance the migration forecasts.