Volume 12 Issue 4
Dec.  2019
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Article Navigation >  Water Science and Engineering  > 2019  >  12(4): 330-338
Jing-chun Feng, Hua-ai Huang, Yao Yin, Ke Zhang. 2019: Comprehensive security risk factor identification for small reservoirs with heterogeneous data based on grey relational analysis model. Water Science and Engineering, 12(4): 330-338. doi: 10.1016/j.wse.2019.12.009
Citation: Jing-chun Feng, Hua-ai Huang, Yao Yin, Ke Zhang. 2019: Comprehensive security risk factor identification for small reservoirs with heterogeneous data based on grey relational analysis model. Water Science and Engineering, 12(4): 330-338. doi: 10.1016/j.wse.2019.12.009
shu

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

doi: 10.1016/j.wse.2019.12.009

  • a Business School, Hohai University, Nanjing 211100, China

  • b Institute of Project Management, Hohai University, Nanjing 211100, China

  • c Jiangsu Provincial Collaborative Innovation Center of World Water Valley and Water Ecological Civilization, Nanjing 211100, China

  • d Guangxi Flood Control and Drought Relief Headquarters, Nanning 530023, China

Funds:  This work was supported by the National Nature Science Foundation of China (Grant No. 71401052), the National Social Science Foundation of China (Grant No. 17BGL156), and the Key Project of the National Social Science Foundation of China (Grant No.14AZD024).
More Information
  • Corresponding author: Ke Zhang
  • Received Date: 2019-06-07
  • Rev Recd Date: 2019-10-17
    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.

     

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