AI-Based Disaster Relief Demand Prediction System

Abstract: Natural disasters like floods, tsunamis, cyclones, earthquakes, droughts, and wildfires have a drastic impact on a country like India, where the occurrence of disasters is frequent. We have made progress to some extent in predicting disasters in advance and planning mitigation strategies by notifying authorities and the public to be prepared. However, during and after a disaster, people may lose their lives not because there is no help available, but because the help does not reach the right place at the right time. Relief in terms of food, water, rescue personnel, medical teams, and shelters is meant to reach the affected people. This research proposes a system to predict accurate relief demand using past disaster records, live disaster data, and geographical information. Machine learning models are used, where a Random Forest classifier is applied to classify the type of disaster from live news data, followed by five XGBoost predictors for different types of relief resources, and K-Nearest Neighbors for geographical similarity matching to predict relief for newly impacted areas. A web-based dashboard with analytics and map visualization is developed to support decision-making and provide better insights for efficient resource allocation. The XGBoost regression models for relief prediction achieved high predictive performance, with an overall R² score of approximately 0.93 across all resource categories.

Keywords: Disaster Management, Disaster Response System, Machine Learning, Resource Demand Prediction, XGBoost, Random Forest, K-Nearest Neighbors, Geographical Analysis.