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	Ecological risk assessment of regions along the roadside of the Qinghai-Tibet highway and railway based on an artificial neural network
	Chen H. ; Jinsong L. S. ; Cao Y. ; Shuangcheng L. C. ; Ouyang H.
	2007
关键词	ecological risk assessment artificial neural network MLP (multilayer perceptron) model natural factors artificial factors alpine gradients root biomass cherry-point stressor massachusetts plateau design area
英文摘要	A concept model of regional risk was constructed for the characteristics of ecosystems alongside the Qinghai-Tibet highway and railway based on the MLP (Multilayer perceptron) model. Seven indices such as snow hazard, drought hazard, and landslide were selected in order to evaluate the integrated ecological risk of the ecosystems along the study area. Results show that the Qaidam montane desert zone had the greatest average risk value (4.26), followed by the Golog-Nagqu high-cold scrub meadow zone (2.80) and the East Qinghai and Qilian montane steppe zone (2.73) among the ecosystems within the six natural zones within the study region. As far as land cover types are concerned, the top three ecological risk values appear in the needle-leaved forest (4.31), desert (4.12), and land without vegetation (3.62), which are higher than those in the other seven types in the study site. Although the risk values are influenced by natural factors and human activities, they are more strongly controlled by natural factors. According to the ecological risk characteristics, the ecosystems within the study area are subdivided into four subregions, including the Qaidam. basin region (high risk), the Xidatan to Damxung region (moderate risk), and the Eastern Qinghai-Qilian (slight risk) and Southern Xizang (Tibet) region (slighter risk).
出处	Human and Ecological Risk Assessment
卷	13
期	4
页	900-913
收录类别	SCI
语种	英语
ISSN号	1080-7039
内容类型	SCI/SSCI论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/24260]
专题	地理科学与资源研究所_历年回溯文献
推荐引用方式 GB/T 7714	Chen H.,Jinsong L. S.,Cao Y.,et al. Ecological risk assessment of regions along the roadside of the Qinghai-Tibet highway and railway based on an artificial neural network. 2007.

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