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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
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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5707969/ https://www.ncbi.nlm.nih.gov/pubmed/29104262 http://dx.doi.org/10.3390/ijerph14111330 |
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author | Yin, Kedong Zhang, Ya Li, Xuemei |
author_facet | Yin, Kedong Zhang, Ya Li, Xuemei |
author_sort | Yin, Kedong |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5707969 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-57079692017-12-05 Research on Storm-Tide Disaster Losses in China Using a New Grey Relational Analysis Model with the Dispersion of Panel Data Yin, Kedong Zhang, Ya Li, Xuemei Int J Environ Res Public Health Article 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. MDPI 2017-11-01 2017-11 /pmc/articles/PMC5707969/ /pubmed/29104262 http://dx.doi.org/10.3390/ijerph14111330 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yin, Kedong Zhang, Ya Li, Xuemei Research on Storm-Tide Disaster Losses in China Using a New Grey Relational Analysis Model with the Dispersion of Panel Data |
title | Research on Storm-Tide Disaster Losses in China Using a New Grey Relational Analysis Model with the Dispersion of Panel Data |
title_full | Research on Storm-Tide Disaster Losses in China Using a New Grey Relational Analysis Model with the Dispersion of Panel Data |
title_fullStr | Research on Storm-Tide Disaster Losses in China Using a New Grey Relational Analysis Model with the Dispersion of Panel Data |
title_full_unstemmed | Research on Storm-Tide Disaster Losses in China Using a New Grey Relational Analysis Model with the Dispersion of Panel Data |
title_short | Research on Storm-Tide Disaster Losses in China Using a New Grey Relational Analysis Model with the Dispersion of Panel Data |
title_sort | research on storm-tide disaster losses in china using a new grey relational analysis model with the dispersion of panel data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5707969/ https://www.ncbi.nlm.nih.gov/pubmed/29104262 http://dx.doi.org/10.3390/ijerph14111330 |
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