Research on Vulnerability Assessment of Disaster Bearing Bodies Based on AHP-Cloud Model

This study selects 16 vulnerability indicators of meteorological disaster-bearing bodies in Yining County based on socio-economic data to construct a vulnerability assessment system for meteorological disaster-bearing bodies, including population vulnerability, economic vulnerability, and social vulnerability. The AHP-Cloud model is utilized to assign weights to each indicator, and the vulnerability and spatial-temporal distribution characteristics of the population-economy-society disaster-bearing bodies in Yi Ning County are evaluated and analyzed. The results show that (1) Demographic, economic, and social factors significantly affect the vulnerability of meteorological disaster-bearing bodies in Yi Ning County. High aging, young age, and population growth exacerbate vulnerability, while the economy, road network, and construction land affect social vulnerability. (2) The population vulnerability in Yi Ning County is influenced by topography, with low vulnerability in the north and high vulnerability in the south. The southern plains are highly vulnerable due to their high population density. From 2005 to 2020, population growth and aging exacerbated population vulnerability, and economic and social vulnerability exhibited similar spatial-temporal differences as population vulnerability. (3) Overall, the vulnerability of meteorological disaster-bearing bodies in Yi ning County is mainly dominated by population and economy. Towns and villages in the south have high vulnerability, and the overall trend is increasing. These conclusions can provide scientific evidence for relevant departments in Yi Ning County to reduce the vulnerability of meteorological disaster-bearing bodies and improve disaster prevention and mitigation capabilities.

Authors

Publication Details

Journal
Academic Journal of Science and Technology
Published
2024-11-29
DOI
https://doi.org/10.54097/3753em19
Primary Topic
Disaster Management and Resilience
Type
article
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article

Research on Vulnerability Assessment of Disaster Bearing Bodies Based on AHP-Cloud Model

Xiangyu Yuan
Academic Journal of Science and Technology
Disaster Management and Resilience
article

Research on Vulnerability Assessment of Disaster Bearing Bodies Based on AHP-Cloud Model

Xiangyu Yuan
article en

Abstract

This study selects 16 vulnerability indicators of meteorological disaster-bearing bodies in Yining County based on socio-economic data to construct a vulnerability assessment system for meteorological disaster-bearing bodies, including population vulnerability, economic vulnerability, and social vulnerability. The AHP-Cloud model is utilized to assign weights to each indicator, and the vulnerability and spatial-temporal distribution characteristics of the population-economy-society disaster-bearing bodies in Yi Ning County are evaluated and analyzed. The results show that (1) Demographic, economic, and social factors significantly affect the vulnerability of meteorological disaster-bearing bodies in Yi Ning County. High aging, young age, and population growth exacerbate vulnerability, while the economy, road network, and construction land affect social vulnerability. (2) The population vulnerability in Yi Ning County is influenced by topography, with low vulnerability in the north and high vulnerability in the south. The southern plains are highly vulnerable due to their high population density. From 2005 to 2020, population growth and aging exacerbated population vulnerability, and economic and social vulnerability exhibited similar spatial-temporal differences as population vulnerability. (3) Overall, the vulnerability of meteorological disaster-bearing bodies in Yi ning County is mainly dominated by population and economy. Towns and villages in the south have high vulnerability, and the overall trend is increasing. These conclusions can provide scientific evidence for relevant departments in Yi Ning County to reduce the vulnerability of meteorological disaster-bearing bodies and improve disaster prevention and mitigation capabilities.

Academic Journal of Science and TechnologyVol. 13(2)
Climate action
Openalex Percentile: Top 65%
Disaster Management and Resilience
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