A machine learning approach for detecting human–wildlife conflict can combine environmental data (e.g., land use, vegetation cover), animal movement (GPS), and human activity (e.g., settlements, agriculture). You can use hybrid models, such as combining deep learning for spatiotemporal pattern detection with fuzzy logic or decision trees for risk-level interpretation. Techniques like LSTM, CNN-LSTM, or spatial GNNs are well-suited for tracking behavior and predicting conflict hotspots.

In my paper, “Advanced Crop Recommendation System”, I built a hybrid AI model integrating deep learning with fuzzy logic for agricultural risk prediction. This framework can be adapted to conflict detection by tuning the model inputs to wildlife corridors, crop damage history, and proximity to human settlements—offering real-time alerts and mitigation planning.

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Article ADVANCED CROP RECOMMENDATION SYSTEM: LEVERAGING DEEP LEARNIN...