Forecasting demand for emergency relief materials resulting from flooding

Abstract

Floods are among the most frequent and destructive natural disasters, causing substantial economic losses and casualties while posing challenges to emergency response in civil and disaster mitigation engineering. However, flood-related data often exhibit characteristics of "small sample size and insufficient information", limiting the effectiveness of traditional forecasting methods. This study proposes an improved GM(1,1) model based on grey system theory for dynamically predicting affected populations, integrated with inventory management to estimate emergency material demand. The model is validated using the June 2024 flood in Jiangxi Province, China. Results showed that: (1) The improved GM(1,1) model demonstrated high prediction accuracy, with the precision test Mean squared Error Ratio (MSER, c = 0.27) and Small Error Probability (SEP, p = 1) both meeting Grade I standards, making it suitable for forecasting affected populations; (2) Case analysis showed that the improved GM(1,1) model outperformed the traditional GM(1,1) model in terms of MSER (c = 0.22<0.31) and SEP (p = 1.0>0.92), with predicted values closer to actual values; (3) The dynamic demand model estimated material requirements from June 24 to July 6, including 15.48 million liters of drinking water, 14,700 tons of food, 8,700 tons of medicine, 2.9 million tents, 10.95 million quilts, and 29,000 signal stations. This study improves dynamic flood response by enabling emergency relief demand prediction and supporting deployments in civil engineering and disaster prevention, including resettlement planning, levee inspections, and identification of key transport nodes.