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Research on Storm-Tide Disaster Losses in China Using a New Grey Relational Analysis Model with the Dispersion of Panel Data

Author

Listed:
  • Kedong Yin

    (School of Economics, Ocean University of China, Qingdao 266100, China
    Ocean Development Research Institute, Major Research Base of Humanities and Social Sciences of Ministry of Education, Ocean University of China, Qingdao 266100, China)

  • Ya Zhang

    (School of Economics, Ocean University of China, Qingdao 266100, China)

  • Xuemei Li

    (School of Economics, Ocean University of China, Qingdao 266100, China
    Ocean Development Research Institute, Major Research Base of Humanities and Social Sciences of Ministry of Education, Ocean University of China, Qingdao 266100, China)

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.

Suggested Citation

  • Kedong Yin & Ya Zhang & Xuemei Li, 2017. "Research on Storm-Tide Disaster Losses in China Using a New Grey Relational Analysis Model with the Dispersion of Panel Data," IJERPH, MDPI, vol. 14(11), pages 1-18, November.
    • Handle: RePEc:gam:jijerp:v:14:y:2017:i:11:p:1330-:d:117273
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    References listed on IDEAS

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    Cited by:

    1. Ning Zhang & Zaiwu Gong & Kedong Yin & Yuhong Wang, 2018. "Special Issue “Decision Models in Green Growth and Sustainable Development”," IJERPH, MDPI, vol. 15(6), pages 1-8, May.
    2. Xue Jin & Xiaoxia Shi & Jintian Gao & Tongbin Xu & Kedong Yin, 2018. "Evaluation of Loss Due to Storm Surge Disasters in China Based on Econometric Model Groups," IJERPH, MDPI, vol. 15(4), pages 1-19, March.
    3. Lihong Wang & Kedong Yin & Yun Cao & Xuemei Li, 2018. "A New Grey Relational Analysis Model Based on the Characteristic of Inscribed Core (IC-GRA) and Its Application on Seven-Pilot Carbon Trading Markets of China," IJERPH, MDPI, vol. 16(1), pages 1-16, December.
    4. Kedong Yin & Pengyu Wang & Xuemei Li, 2017. "The Multi-Attribute Group Decision-Making Method Based on Interval Grey Trapezoid Fuzzy Linguistic Variables," IJERPH, MDPI, vol. 14(12), pages 1-16, December.
    5. Kedong Yin & Benshuo Yang & Xuemei Li, 2018. "Multiple Attribute Group Decision-Making Methods Based on Trapezoidal Fuzzy Two-Dimensional Linguistic Partitioned Bonferroni Mean Aggregation Operators," IJERPH, MDPI, vol. 15(2), pages 1-23, January.

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