Multi-dimensional analysis of urban shrinkage problem in Liaoning Province based on multi-index system, grey correlation analysis and BP neural network with particle swarm optimization
Abstract
The rapid development of urbanization in modern China is accompanied by the increasingly serious problem of urban shrinkage. To provide an effective analytical model for the urban shrinkage problem, this paper takes Liaoning Province, which is one of the typical provinces with a serious urban shrinkage issue in China, as an example. Based on the data from 30 cities in Liaoning Province in recent years, this paper constructs a multi-index system for shrinking cities to evaluate and classify the shrinkage degree of 30 cities. The grey relation analysis model is also used to quantitatively analyze the influence of various factors on the shrinking city population, while the back-propagation neural network algorithm model optimized with particle swarm optimization is also applied to predict the development trend of shrinking cities. The results present the shrinking properties of 30 cities and correlations between different city indicators, as well as the predictive development trend of the shrinking city.
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Index Terms
- Multi-dimensional analysis of urban shrinkage problem in Liaoning Province based on multi-index system, grey correlation analysis and BP neural network with particle swarm optimization
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Published In
March 2023
598 pages
ISBN:9781450399449
DOI:10.1145/3590003
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 May 2023
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- Research-article
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- Refereed limited
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- Natural Science Foundation of China
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CACML 2023
CACML 2023: 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning
March 17 - 19, 2023
Shanghai, China
Acceptance Rates
CACML '23 Paper Acceptance Rate 93 of 241 submissions, 39%;
Overall Acceptance Rate 93 of 241 submissions, 39%
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