Wildlife Bird Migration Forecasting and Analysis: A Hybrid AI-Embedded Paradigm for Real-Time Ecological Surveillance

Migratory birds are crucial health indicators of the environment, but monitoring their patterns has grown more challenging with fast-paced environmental changes. This thesis suggests an intelligent, low-cost system combining embedded electronics and machine learning to provide real-time bird migration analysis. The design revolves around the LPC2148 ARM7 microcontroller with GPS, temperature, and accelerometer sensors integrated into it. Designed on Proteus simulation, the circuit provides energy-aware and scalable field deployment. The natural migration pattern is simulated using Fibonacci and sigmoidbased models to identify biologically plausible behaviour. Sophisticated AI algorithms such as LSTM for sequence forecasting, Random Forest for behaviour classification, and Autoencoders for anomaly detection were used on synthetic and open-source Movebank datasets. Outcomes display high accuracy, minimal energy expenditure, and real-world usability. This research is a promising contribution to smart wildlife telemetry and conservation technology