Authors - Saraswati Patil, Mustafa Limdiyawala, M.S. Dawngliana Fanai, Meghaj Kharwadkar, Shivshankar Mahajan Abstract - Natural disasters such as earthquakes, floods, and tsunamis pose severe threats to human lives, infrastructure, and economies. Effective prediction and response strategies are vital for minimizing their impact. This paper introduces an AI-driven Disaster Prediction and Relief Dashboard, an integrated platform leveraging machine learning and geospatial mapping to forecast natural disasters and optimize relief operations. Using Random Forest and Gradient Boosting algorithms trained on historical data, the system predicts the likelihood, magnitude, and severity of disasters. Geospatial visualization highlights high-risk zones and delivers real-time situational awareness for authorities. Additionally, the platform streamlines relief management by dynamically allocating resources based on predicted disaster severity and location. By integrating predictive analytics with operational planning, the system enhances preparedness and responsiveness, contributing to more resilient disaster management.
