AI-GIS Enabled Multimodal Framework for Wildlife Surveillance, Migration Shift Forecasting, and Poaching Risk Mapping

In the 21st century, the rapid deforestation and illegal wildlife poaching have disrupted ecological balance, necessitating the development of an AI-powered framework for real-time wildlife monitoring and conservation. The "Ecoverse" system integrates advanced AIML techniques like DBSCAN clustering and Graph Neural Networks to analyze and forecast animal migration patterns. It also features real-time animal accident prevention using Twilio-driven mobile alerts with 95% accuracy and less than 2 seconds of response time through Convolutional Neural Networks. The system supports continuous poacher monitoring using a webcam-integrated YOLOv8 model. Facebook's Prophet model is used for time-series forecasting of endangered species in India. GIS-driven forest cover mapping and deforestation rate analysis are employed, combining Recurrent Neural Networks with U-Net-based segmentation masking for future forest projections. The system accurately identifies specific species of flora and fauna based on location and terrain, providing higher precision and reduced abstraction. It also performs comprehensive GIS analysis and visualization of historical and current forest and wildlife data, identifying forest fire types, severity levels, and fire probability in specific locations. The system plays a critical role in preventing wildlife poaching, tracking flora and fauna in real-time, sending timely alerts, and contributing to wildlife and biodiversity protection.