Work

Forecasting Demand for Emergency Material Classification Based on Casualty Population

Year: 2025

Type: article

Abstract: 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.

Source: Systems

DOI: 10.3390/systems13060478

Authors: Jianliang Yang, Kun Zhang, Hanping Hou, Na Li

Institutions: Beijing University of Chemical Technology, Beijing Jiaotong University

Language: English

Topic: Facility Location and Emergency Management

Subfield: Organizational Behavior and Human Resource Management

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: National Natural Science Foundation of China, Ministry of Education of the People's Republic of China