noorahmednn2nn2-create/Flood-Resilient-Digital-Twin

A Global Climate Digital Twin framework integrating Machine Learning (Random Forest) and Blockchain to optimize flood aid equity. Comparing advanced models from Germany and China to implement solutions in the GBM Basin, Libya, South Sudan, and Vietnam Global Scope This research analyzes flood management across diverse economic landscapes: • Case Studies (High Vulnerability): GBM Basin, Libya, and South Sudan. • Benchmarks (Advanced Tech): Germany and China. • Scaling Potential: Mekong Delta, Vietnam. 🛠 Technical Implementation • Machine Learning: Utilizing Random Forest Regressor to predict flood impact and prioritize aid distribution based on real-time data. • Transparency: A Blockchain-based tracking system to ensure aid reaches victims without intermediaries or corruption. • Policy Innovation: Implementing "Plan B" amphibious housing and carbon/tech taxes to fund climate resilience. 🎓 Academic Context Developed and submitted for the Harvard VISION 2026 conference. This project aims to translate complex scientific data into actionable humanitarian policy.