Evaluating perception of community resilience to typhoon disasters in China based on grey relational TOPSIS model

Peng, Yi; Zheng, Rongrong; Tian, Ye; Cheng, Liang; You, Jinhong

doi:10.1016/j.ijdrr.2022.103468

Cited by 23 publications

(6 citation statements)

References 40 publications

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“…The lag time of baseflow in the wet season was significantly smaller than that in the dry season. This phenomenon happens as a result of the Zhejiang Province, which is located in a humid region, receiving more than 70% of the total annual precipitation during the rainy season, most of which was heavy and brief (Liu et al., 2023; Peng et al., 2023; Yu et al., 2023). The baseflow lag time significantly decreased as a result of the runoff that developed quickly during the rainfall event and gathered in the gullies (Cartwright & Morgenstern, 2015).…”

Section: Resultsmentioning

confidence: 99%

Using Baseflow Ensembles for Hydrologic Hysteresis Characterization in Humid Basins of Southeastern China

Chen,

Huang,

et al. 2024

Water Resources Research

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Baseflow plays a vital role in protecting the environment and ensuring a stable water supply for farming. There are still gaps in the current understanding of baseflow convergence rates in the humid region due to the abundance of rainfall and the high‐water table. Therefore, this study focused on the evolution and hysteresis characteristics of baseflow in humid basins of southeastern China. The baseflow ensemble simulation (BES) method was established to improve the reliability and applicability of baseflow simulation. We suggest a way of differentiating the wet and dry seasons based on the multi‐year average monthly baseflow index (BFI) to determine the intra‐annual distribution of water effectively and simply. The hydrological hysteresis effect of baseflow on precipitation is revealed by characterizing baseflow response to precipitation under precipitation events during wet and dry seasons. A methodology for assessing the performance of baseflow simulation was proposed from observations of streamflow and precipitation. We found that the BES method performed better in baseflow simulation than other single separation methods. Using the BES method, the lag time of baseflow to precipitation during the wet and dry seasons was found to be 3.09 and 4.04 days after utilizing the BFI to divide the hydrological situation into wet and dry seasons. Additionally, precipitation had nearly twice as much intensity influence on baseflow during the dry season compared to the wet season. These findings have significant ramifications for the use, management, and planning of water resources in humid areas of China.

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Section: Resultsmentioning

confidence: 99%

Using Baseflow Ensembles for Hydrologic Hysteresis Characterization in Humid Basins of Southeastern China

Chen,

Huang,

et al. 2024

Water Resources Research

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“…Gray correlation analysis is a method to reflect the strength, size, and order of each factor by determining the size of the gray correlation value of the reference data column and several comparison data columns ( Peng et al, 2023 ). Based on the AISAS behavior model, this paper sets the data of purchase behavior as the reference sequence

, and sets the data series of Attention

, Interest

, Search

and Sharing

experience as the comparison sequence.…”

Section: Methodsmentioning

confidence: 99%

The influencing mechanism of scenic spot online attention and tourists’ purchase behavior: an AISAS model based investigation

Zhao,

Kong,

Yang

et al. 2024

Front. Psychol.

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IntroductionIn the era of the Internet, online digital traces have become a new way to study the online attention of scenic spots and tourists’ purchase behavior. The public’s information search on major search platforms is a series of manifestations of potential tourists’ attention and interest in scenic spots, but there are few studies on how attention, interest and information search affect potential tourists to generate real purchase behavior.MethodThis paper selects four dimensions of short video platform, travel website, search engine and social media to comprehensively measure the online attention of high-quality scenic spots in Yunnan Province, and then establishes a gray association analytic hierarchy process based on the relevant variables of the AISAS model to empirically analyze the primary and secondary factors affecting tourists’ purchase behavior.Results(1) From the perspective of the online attention of scenic spots on different platforms, the intensity of the public’s scenic spots online attention on the four types of media platforms is in the order of travel websites, search engines, short videos and social media (2) From the perspective of spatial distribution characteristics, the online attention of high-quality scenic spots in Yunnan Province is unevenly distributed, that is, there is a big difference between the attention of higher star scenic spots and their star rating and popularity, while the attention of low-star scenic spots is not much different from their star rating and popularity (3) From the perspective of spatial agglomeration characteristics, the comprehensive online attention of high-quality scenic spots in Yunnan Province presents the spatial agglomeration characteristics of “the multi-core linkage of high-density in the east and north, and sub-high-density in the south” (4) The factors influencing the purchase behavior of potential tourists are sharing experience, attracting attention, generating interest and searching information.DiscussionBy exploring the formation mechanism of high-quality scenic spots online attention in Yunnan Province and the mechanism of its spatial differentiation, this study not only enriches the logical chain of “tourism information source → potential tourists → demand driven → tourism information search → travel preference → destination selection → purchase decision → travel experience → real tourists → feelings after traveling → focus on feedback → tourism information source,” but also broadens the application scenarios and application boundaries of travel preference theory and AISAS behavior model to a certain extent.

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“…Scientificity means that the method choice must conform to theoretical logic, and feasibility means that the model must refer to an actual situation [54]. At present, scholars have mainly used gray correlation models [55], principal component analysis [56], etc., to evaluate community resilience, which means they use more objective quantitative indicators to measure community resilience. This method undoubtedly has its advantages but ignoring the humanistic factors of the community, as a community of residents, and relying solely on cold, objective data measurement has its inherent disadvantages [57].…”

Section: Model Constructionmentioning

confidence: 99%

Digital and Culture: Towards More Resilient Urban Community Governance

Xiang,

Heng,

Zhai

et al. 2024

Land

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Urban communities are characterized by significant population size, high density, and strong mobility. While we might enjoy the dividends of rapid modernization, there are nonetheless variable and frequent public crises that occur. Modernization’s problems are gradually emerging, and the traditional risk prevention logic that relies on administrative “rigidity” has begun to be widely challenged. Traditional urban communities depend on institutional, structural, and spatial aspects to improve community resilience. Because big data has become popular, attention has begun to be paid to digital empowerment and community resilience. However, the emergence of problems such as “digital paradox” and “digital ethics” in the digital realm itself has prompted calls for cultural resilience to continue to rise. Therefore, urgently needed resolutions are required to questions regarding the resilience of traditional communities, the construction of digital and cultural resilience, the relationship between digital and cultural resilience, and the manner in which cultural resilience is coordinated to solve the problem of digital resilience. A quantitative analysis of 350 questionnaires from five urban communities found that these communities’ institutional, spatial, and structural dimensions are the driving factors for improving resilience. In contrast, the cultural and digital dimensions are constraints. Therefore, the question of how to coordinate the cultural and digital factors represented by traditional and modern societies in order to compensate for the shortcomings in resilience construction is that which future urban communities must consider. The authors of this study believe that digital empowerment is needed to open up the “first mile” of resilient communities, that cultural empowerment is required to break down the “blocks in the middle” of resilient communities, and that digital and cultural coupling is needed to link the “last mile” of resilient communities. One must use culture to compensate for the shortcomings of digital resilience and digital to pay for the failures of cultural resilience before one can move towards more resilient urban community governance.

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Evaluating perception of community resilience to typhoon disasters in China based on grey relational TOPSIS model

Cited by 23 publications

References 40 publications

Using Baseflow Ensembles for Hydrologic Hysteresis Characterization in Humid Basins of Southeastern China

Using Baseflow Ensembles for Hydrologic Hysteresis Characterization in Humid Basins of Southeastern China

The influencing mechanism of scenic spot online attention and tourists’ purchase behavior: an AISAS model based investigation

Digital and Culture: Towards More Resilient Urban Community Governance

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