A Machine Learning based approach for Bird Migration Detection and feature analysis with SHAP

Bird migration is vital for preserving ecological balance and biodiversity. Tracking migration patterns is very important to understand ecosystem health and predicting environmental changes. The use of machine learning methods helps analyze and predict the migration patterns of birds. In-depth evaluations of Machine Learning methods used in bird migration detection are provided in this research, which also discusses different models for assessing bird movement data, including Convolutional Neural Networks, K-nearest neighbors, Support Vector Machines, Random Forests, and Decision Trees.