2023
DOI: 10.1016/j.ecolind.2023.111278
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Spatial vulnerability assessment of rural settlements in hilly areas using BP neural network algorithm

Yang Liu,
Bo Shu,
Yang Chen
et al.
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Cited by 4 publications
(4 citation statements)
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“…Additionally, there are many active evaluation systems and methods that have been practically tested and developed on national and regional scales in response to specific environmental, cultural, and economic developments. Examples include the New Zealand Landscape Character Assessment System used for landscape classification and identification [184], the Visual Quality Assessment Tool for evaluating the diversity of rural landscapes [185], and the Vulnerability Assessment System for rural settlements in hilly areas [186].…”
Section: Improving the Evaluation System For Features And Characteris...mentioning
confidence: 99%
“…Additionally, there are many active evaluation systems and methods that have been practically tested and developed on national and regional scales in response to specific environmental, cultural, and economic developments. Examples include the New Zealand Landscape Character Assessment System used for landscape classification and identification [184], the Visual Quality Assessment Tool for evaluating the diversity of rural landscapes [185], and the Vulnerability Assessment System for rural settlements in hilly areas [186].…”
Section: Improving the Evaluation System For Features And Characteris...mentioning
confidence: 99%
“…In the agricultural system, considering that agricultural production is more susceptible to disturbances caused by climate change, the intensity of extreme rainfall [15] was selected to characterize waterlogging stress on agricultural production, and the proportion of cultivated land [44] was selected to characterize the pressure of farmland encroachment. In the urban system, the hidden danger of geological collapse, the hidden danger of landslides, and the hidden danger of mudflows [45] were selected to characterize the pressures on urban construction caused by mountain disasters, and nighttime light intensity [46] and the proportion of construction land [16] were selected to characterize the intensity and scale of socioeconomic activities, increasing the urban vulnerability. The vulnerability quantification consists of three components, namely, exposure, sensitivity, and adaptive capacity [37,38].…”
Section: Exposure Indexmentioning
confidence: 99%
“…We constructed a spatial vulnerability assessment framework for mountain cities, including a vulnerability index system, a GA-BP neural network assessment model, and GWR analysis. In previous papers on the vulnerability of mountainous regions, researchers have assessed the vulnerability from the perspectives of geology and hazards [45,64], socioeconomics [65], ecological sensitivity, and ecosystem services [53] by constructing vulnerability indicator systems. These indicators are mostly selected from a single perspective, and holistic studies of humans and the nature are lacking.…”
Section: Applicability Of the Assessment Frameworkmentioning
confidence: 99%
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