Abstract
Owing to the difference of the sequences’ orders and the surface structure in the current panel grey relational models, research results will not be unique. In addition, individual measurement of indicators and objects and the subjectivity of combined weight would significantly weaken the effective information of panel data and reduce the reliability and accuracy of research results. Therefore, we propose the concept and calculation method of dispersion of panel data, establish the grey relational model based on dispersion of panel data (DPGRA), and prove that DPGRA exhibits the effective properties of uniqueness, symmetry, and normality. To demonstrate its applicability, the proposed DPGRA model is used to research on storm-tide disaster losses in China’s coastal areas. Comparing research results of three models, which are DPGRA, Euclidean distance grey relational model, and grey grid relational model, it was shown that DPGRA is more effective, feasible, and stable. It is indicated that DPGRA can entirely utilize the effective information of panel data; what’s more, it can not only handle the non-uniqueness of the grey relational model’s results but also improve the reliability and accuracy of research results. The research results are of great significance for coastal areas to focus on monitoring storm–tide disasters hazards, strengthen the protection measures of natural disasters, and improve the ability of disaster prevention and reduction.
Highlights
This paper describes the theoretical basis for panel data data correlation analysis and demonstrates the potential for non-unique results from conventional correlation analysis and demonstrates the potential for non-unique results from conventional Grey Relational AnalysisGrey relational analysis (GRA)
We proposed a GRA model based on the dispersion of panel data
The DPGRA model was developed based on the dispersion of panel data
Summary
Grey relational analysis (GRA) is an important branch of grey system theory (GST) that is used to determine the relational degree among factors according to similarities in their geometry. Based on a grey absolute relational model, Liu et al developed a GRA model using two different perspectives of similarity and proximity [36]. Used panel data in three-dimensional space to develop a multidimensional relational degree, which extended the matrix-based grey absolute relational degree [38]. Liu et al described the geometrical features of panel data in three-dimensional space using a grid method and developed grid relational coefficients; according to arithmetic averages, a grey grid relational model was subsequently established [41]. To summarize, existing GRA models have been developed based on geometric features, slope, area, distance, angle et al, and gradually extended to three-dimensional space, which is more authentic and comprehensive for reflecting the relational degree of research objects. GRA models have been widely applied in numerous fields related to economy [45,46,47], management [48,49], disaster risk [50], society [51,52], industry [53,54], physics [55,56], chemistry [57,58], transportation [59], ecology [60], geology [61] and aeronautics and astronautics [62]
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