Suyue Han , Bin Liu , Jun Shu , Zuli He , Xinyu Xia , Ke Pan , Hourui Ren
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引用次数: 0
Abstract
Strong geological disasters have caused persistent losses in society, economy, and ecological environments. Given the unique geographical settings of the stricken areas, their resilience is prone to damage or even loss. Comprehensive risk assessment of natural disasters is the core content and important foundation for building regional resilience. Therefore, conducting dynamic characteristics analysis of resilience in mountainous disaster areas impacted by strong earthquake geological disasters is vital for ensuring the region's high-quality and sustainable development. This article takes the 51 stricken areas of Wenchuan earthquake as the research object. To this end, social, economic and ecological environmental data from 2008 to 2020 was hereby collected. Initially, a regional resilience assessment system based on "socio-economic-ecological environment" was established, considering the long-term and spatial heterogeneity of geological disasters. Secondly, the regional resilience assessment model was constructed using Spectral clustering-genetic algorithm-improved entropy weight method. Following that, the dynamic characteristics of regional resilience were quantitatively analyzed from two aspects, including change velocity state and change rate trend. Finally, based on the regional resilience characteristics, differentiated resilience enhancement strategies were proposed. Collectively, the results revealed that: (1) From a geological disaster standpoint, the risk in post-earthquake disaster areas exhibited a strikingly rapid decline, with the spatial distribution of geological disaster risk being notably higher in the central areas and diminishing towards the peripheries. (2) Overall, the regional resilience of the 51 stricken areas showed a "V-shaped" trend, with a significant upturn since 2012. (3) From the perspective of dynamic characteristics, more counties (cities) presented an upward trend. (4) The 51 stricken areas were hereby divided into the "benchmarking type", the "declination type", the "backward type", and the "potential type". In conclusion, the current study enhances the technical framework for evaluating regional resilience and provides technical support for the construction of resilient cities.
期刊介绍：
Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory.
The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation.
This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods.
Computational science typically unifies three distinct elements:
• Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous);
• Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems;
• Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).
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