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

Dan Chang, Rui Fan, Zitong Sun

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.

Keywords


deep belief network, case-based reasoning, decision support, emergency rescue, earthquake

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References


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DOI: https://doi.org/10.15837/ijccc.2020.3.3836



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