NDLI: Study on town ecological carrying capacity based on BP neural network

Content Provider	IEEE Xplore Digital Library
Author	Aimei Li Haizhen Yang Rong Kang
Copyright Year	2010
Description	Author affiliation: College of Environmental Science and Engineering, Tongji University, State Key Laboratory of Pollution Control and Resource Reuse, Shanghai, China (Aimei Li; Haizhen Yang; Rong Kang)
Abstract	Artificial neural network is a frontier theory of complex non-linear science and artificial intelligence science. It was widely applied in ecological ecosystem research. However its application in regional ecological carrying capacity (ECC) field is still rare. Based on the basic principle and method of neural network, as well as the theory of ECC, the BP neural network model of town ECC was established, with a view to predict the evolution law of regional ECC in the future. Then take Yiwu city as study area, its ECC points in the past and future were calculated and predicted by using these methods, respectively. According to the assessment results and the realities of study region, the accuracy of the method was further analyzed. Results showed Yiwu's ECC scores were between 60 point and 80 point from 2000 to 2007, however it increased a little from 2008 to 2020. The neural network model designed could better predict the regional ECC, and it was an effective method to study ECC. using BP neural network
Starting Page	1551
Ending Page	1554
File Size	203697
Page Count	4
File Format	PDF
ISBN	9781424476169
e-ISBN	9781424476183
DOI	10.1109/ICISE.2010.5689859
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-04
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Industries Biological system modeling Town Artificial neural networks Ecological carrying capacity Assessment Cities and towns Error correction codes Neural network Indexes Water resources
Content Type	Text
Resource Type	Article

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