A Deep Belief Network and Case Reasoning Based Decision Model for Emergency Rescue

Abstract

The frequent occurrence of major public emergencies in China has caused significant human and economic losses. To carry out successful rescue operations in such emergencies, decisions need to be made as efficiently as possible. Using earthquakes as an example of a public emergency, this paper combines the Deep Belief Network (DBN) and Case-Based Reasoning (CBR) models to improve the case representation and case retrieval steps in the decision-making process, then designs and constructs a decision-making model. The validity of the model is then verified by an example. The results of this study can be applied to maximize the efficiency of emergency rescue decisions.

How to Improve the Cooperation Mechanism of Emergency Rescue and Optimize the Cooperation Strategy in China: A Tripartite Evolutionary Game Model

“…At present, there are still outstanding problems in China's emergency rescue system, such as the imperfect cooperation mechanism between emergency rescue subjects, the mismatch between support strategies and rescue operations, and the "disorderly" participation of social emergency organizations. The practice of emergency rescues shows that it is an important basis to improve the overall efficiency of government emergency management to clarify the key tasks and strategies of emergency rescue subjects, improve the cooperative relationship between emergency rescue subjects, and strengthen the planning and adaptive evolution mechanism of emergency rescues [7,8]. However, existing studies mostly focus on the formation and characteristics of the government's emergency cooperation mode in an emergency response, and there are few dynamic evaluations and targeted studies of cooperation strategies from the perspective of emergency rescues.…”