NDLI: Improved back-propagation neural network in ecological vulnerability assessment of Zhalong wetland

Content Provider	IEEE Xplore Digital Library
Author	Bo Fu Zhen-hong Xie Zhong-li Wang
Copyright Year	2010
Description	Author affiliation: School of Surveying and Prospecting Engineering, Jilin Institute of Architecture and Civil Engineering, Changchun, China (Bo Fu; Zhen-hong Xie; Zhong-li Wang)
Abstract	Combining GIS and RS technique in the study of ecological vulnerability assessment in Zhalong Wet Ecosystem, an improved Back-Propagation neural network was established to model and evaluate ecological vulnerability based on Press-State-Response model. The research educed that: the regions of moderate or severe vulnerability accounted for 18% while the regions of potential or minute vulnerability accounted for 63%; the potential fragility areas and the slight fragility areas with better health were all located in the internal marish while the moderate fragility areas and the serious fragility with poor health were mainly paddy fields; the vulnerability of the central area in the Reserve was not so obvious as the marginal area. The holistic ecological environment of Zhalong wetland was not optimistic. By modeling improved BP neural network and calculating the ecological vulnerability index EVI of each grid cell of Zhalong wetland, a reference of scientific significance was provided for the ecological environment protection and management of Zhalong wetland from the quantitative point of view.
Starting Page	1700
Ending Page	1704
File Size	1268092
Page Count	5
File Format	PDF
ISBN	9781424459582
e-ISBN	9781424459612
DOI	10.1109/ICNC.2010.5584561
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training ecological vulnerability Zhalong wet ecosystem Back-Propagation neural network Biological system modeling Press-State-Response model Ecosystems Humans Artificial neural networks Indexes Mathematical model Geographic Information System
Content Type	Text
Resource Type	Article

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