Forecasting Demand for Emergency Material Classification Based on Casualty Population

Accurately forecasting emergency material demand during the initial stages of disaster response is challenging due to communication disruptions and data scarcity. This study proposes a hybrid model integrating regression analysis and intelligent analysis to estimate casualties and predict emergency supply needs indirectly. A case study of five earthquake-affected villages validates the model, using building collapse rates and population data to calculate casualties and determine the demand for essential supplies, including food, water, medicine, and tents. The findings demonstrate that the proposed approach effectively addresses the “black box” condition by utilizing correction factors for population density, disaster preparedness, and emergency response capacity, providing a structured framework for rapid and accurate demand forecasting in disaster scenarios.