Comprehensive security risk factor identification for small reservoirs with heterogeneous data based on grey relational analysis model

Abstract

Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems. The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete. The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences. To this end, this paper introduces a new grey relational model to analyze heterogeneous data. In this study, a set of security risk factors for small reservoirs was first constructed based on theoretical analysis, and heterogeneous data of these factors were recorded as sequences. The sequences were regarded as random variables, and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors. Then, a new grey relational analysis model for heterogeneous data was constructed, and a comprehensive security risk factor identification method was developed. A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.