Evaluation of regional agricultural drought vulnerability based on unbiased generalized grey relational closeness degree
Grey Systems: Theory and Application
ISSN: 2043-9377
Article publication date: 24 March 2022
Issue publication date: 16 November 2022
Abstract
Purpose
The purpose of this study is to propose an unbiased generalized grey relational closeness evaluation model to improve the accuracy of regional agricultural drought vulnerability decision-making results, as well as to provide theoretical support for reducing agricultural drought risk and losses.
Design/methodology/approach
The index weight is calculated using a rough set and deviation minimization criterion, and the relational degree between the research object and the double reference sequence is thoroughly investigated using the generalized grey relational closeness degree. Because different index rankings can correspond to different closeness degrees, the Monte Carlo method was used to calculate an unbiased estimate of the generalized grey relational closeness degree, which was used as a decision basis.
Findings
Agricultural drought vulnerability in Henan Province in 2019 was clearly spatially differentiated. The vulnerability to agricultural drought in the southern and eastern regions was generally higher than that in other regions. The evaluation results of this model are highly stable and reliable compared to those of the traditional generalized grey relational evaluation model.
Practical implications
This study proposes an evaluation model based on an unbiased generalized grey relational closeness degree, which is important to supplement the grey relational analysis method system and plays a positive role in promoting the quantitative evaluation of regional agricultural drought vulnerability.
Originality/value
The Monte Carlo method is used to calculate the unbiased estimation of the generalized grey relational closeness degree, which solves the problem of the replacement dependence of the traditional generalized grey relational degree and the one-sidedness of the evaluation results, and provides a new research idea for the evaluation of regional agricultural drought vulnerability under cross-sectional informatics.
Keywords
Acknowledgements
This work was funded by National Natural Science Foundation of China (51979106), Scientific and Technological Plan Fund Project of Henan Province (182102310014), Key Research Project Plan of Henan Universities (18A630030), and Doctoral Innovation Fund Project of North China University of Water Resources and Electric Power. These financial supports are gratefully acknowledged.
Citation
Zhang, D. and Luo, D. (2022), "Evaluation of regional agricultural drought vulnerability based on unbiased generalized grey relational closeness degree", Grey Systems: Theory and Application, Vol. 12 No. 4, pp. 839-856. https://doi.org/10.1108/GS-12-2021-0187
Publisher
:Emerald Publishing Limited
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