Demand prediction of emergency materials using case-based reasoning extended by the Dempster-Shafer theory

In recent years, the frequent occurrence of natural hazards has caused huge economic and human losses, as well as seriously impacting the sustainable development of society. The effective management of emergency responses to natural hazards has become an important research topic worldwide. The demand prediction of emergency materials is the premise and basis for the optimal allocation of emergency resources, which is of great significance in improving the efficiency of disaster-related emergency responses. Using case-based reasoning (CBR) and the Dempster-Shafer theory, we investigated methods of predicting emergency materials demand. First, to address the problems of missing feature values, feature heterogeneity and inter-correlations among features of CBR, we proposed a case retrieval strategy based on Dempster-Shafer theory that not only lays a theoretical foundation for subsequent research, but also improves the case retrieval strategy used in CBR. Second, inspired by the 4R principle in CBR, we proposed a scenario-matching method for natural hazard, which uses historical cases in the absence of effective decision data for natural hazard-related loss predictions. Third, assuming that the impact of natural hazards will change with time, we further constructed a dynamic prediction model of emergency material demand based on the prediction results of natural hazard losses. In this paper, typhoon and earthquake disasters are used as case studies to demonstrate the application of the proposed materials demand prediction model, and the effectiveness of the method is demonstrated through empirical analysis.

Automated summary

This paper investigates methods of predicting emergency materials demand using case-based reasoning (CBR) and the Dempster-Shafer theory. It proposes an improved case retrieval strategy and a scenario-matching method for natural hazards, as well as a dynamic prediction model. The effectiveness of the proposed methods is demonstrated through empirical analysis using typhoon and earthquake disasters as case studies.