This research paper, titled "Predictive Modeling of Human-Wildlife Coexistence Zones," explores using Python-based machine learning to proactively manage human-wildlife interactions. It focuses on distinguishing between areas of "coexistence"—where humans and wildlife share space with minimal negative impact—and "conflict zones" characterized by frequent negative interactions like crop damage or safety threats. Key Components of the Research: Methodology: The study utilizes machine learning algorithms such as Random Forests and Gradient Boosting Machines to generate spatial probability maps of interaction outcomes. Green Space Influence: A central finding is that high-quality green spaces, specifically those with high forest density and continuous habitat corridors, are strong predictors of successful coexistence. Predictors of Conflict: High human population density, proximity to major roads, and agricultural land expansion were identified as primary drivers of wildlife conflict. Objective: The goal is to shift conservation from reactive mitigation to anticipatory planning, providing tools for evidence-based land-use zoning and green infrastructure investment. The research concludes that robust natural habitats and effective connectivity infrastructure are essential for facilitating wildlife persistence while minimizing negative encounters with human populations
Yathaarth Goel (Fri,) studied this question.