Predicting Wildlife Poaching Activities Using AI

Illegal wildlife poaching continues to be a major threat to endangered species, biodiversity, and ecosystem balance. Traditional patrol methods are often reactive and resource-intensive. Predicting where poaching incidents are likely to occur enables proactive interventions. By analyzing environmental factors, ranger reports, animal movement patterns, and historical poaching data, AI can significantly strengthen conservation efforts and protect vulnerable species from extinction.

Using machine learning classification models trained on poaching incident data, environmental conditions, ranger patrol patterns, and animal movement data, it's possible to predict high-risk zones for future poaching. Random Forests, Gradient Boosting, and Neural Networks can model complex spatial and temporal patterns. These predictions allow conservation agencies to allocate patrols optimally, safeguard wildlife, and prevent illegal activities before they occur.

Historical poaching records, ranger patrol data, environmental conditions (forest density, water availability), animal population data, and human activity indicators are collected. Machine learning models are trained to predict the probability of poaching incidents based on these features. Predictive heatmaps showing high-risk areas are generated, enabling smarter patrol deployment, conservation strategies, and community engagement for anti-poaching efforts.

• Collect datasets from conservation agencies, ranger patrol records, animal tracking systems, and satellite imagery data sources.
• Preprocess and engineer features: proximity to roads, vegetation cover, ranger patrol density, seasonal variations, and animal migration paths.
• Train classification models like Random Forest, XGBoost, or Neural Networks to predict poaching risk levels spatially and temporally.
• Evaluate model performance using accuracy, precision, recall, ROC-AUC, and apply threshold tuning for optimal sensitivity to poaching risks.
• Deploy dashboards visualizing poaching risk maps and provide actionable insights for patrolling and anti-poaching operations.

• SMART Conservation Software
• Global Forest Watch
• EarthRanger Conservation Data Systems