Hybrid similarity measure for case retrieval in CBR and its application to emergency response towards gas explosion

Case retrieval is a primary step in case-based reasoning (CBR). It is important to measure the similarity between each historical case and the target case during the case retrieval process. In recent years, some methods for similarity measure with multiple formats of attribute values can be found in the practical CBR applications, but the in-depth study is still lacking. The objective of this paper is to develop a new method for hybrid similarity measure with five formats of attribute values: crisp symbols, crisp numbers, interval numbers, fuzzy linguistic variables and random variables. First, for each format of the attribute values, the calculation formula to measure the attribute similarity is presented. Then, the method for measuring hybrid similarity between each historical case and the target case is given by aggregating attribute similarities using the simple additive weighting method, and the proper historical case(s) can be retrieved according to the obtained hybrid similarities afterwards. Finally, a case study in the field of emergency response towards gas explosion is introduced to illustrate the use of the proposed method.