Cargando…
The Analysis of the Urban Sprawl Measurement System of the Yangtze River Economic Belt, Based on Deep Learning and Neural Network Algorithm
In the context of rapid urbanization, the spread of cities in the Yangtze River Economic Belt is intensifying, which has an impact on the green and sustainable development of these cities. It is necessary to establish an accurate urban sprawl measurement system. First, the regulation theory of urban...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7345229/ https://www.ncbi.nlm.nih.gov/pubmed/32545504 http://dx.doi.org/10.3390/ijerph17124194 |
_version_ | 1783556132182163456 |
---|---|
author | Huang, Huafang Wu, Xiaomao Cheng, Xianfu |
author_facet | Huang, Huafang Wu, Xiaomao Cheng, Xianfu |
author_sort | Huang, Huafang |
collection | PubMed |
description | In the context of rapid urbanization, the spread of cities in the Yangtze River Economic Belt is intensifying, which has an impact on the green and sustainable development of these cities. It is necessary to establish an accurate urban sprawl measurement system. First, the regulation theory of urban sprawl is explained. According to the actual development situation of cities in the Yangtze River Economic Belt, smart growth theory is selected as the basic regulation method of urban sprawl. Second, the back propagation neural network (BPNN) algorithm under deep supervised learning is applied to construct a smart evaluation model of land use growth. Finally, based on the actual development of cities in the Yangtze River Economic Belt, the quantitative growth measurement method is selected to construct a measurement system of urban sprawl in the Yangtze River Economic Belt, and the empirical analysis is carried out. The training results show that the proposed BPNN smart growth evaluation model, based on deep supervised learning, has good evaluation accuracy, and the error is within the preset range. The analysis of the quantitative growth-based measurement system in the increase of urban construction land shows that the increase in urban construction land area of the Yangtze River Economic Belt from 2014 to 2019 was 78.67 km(2). Meanwhile, the increases in urban construction land area in different years are different. The empirical results show that the population composition of the Yangtze River Economic Belt and the urban construction area between 2005 and 2019 show a trend of increasing annually; at the same time, urban sprawl development shows a staged characteristic. It is of great significance to apply deep learning fusion neural network algorithm in the construction of the urban sprawl measurement system, which provides a quantitative basis for the in-depth analysis and discussion of urban sprawl. |
format | Online Article Text |
id | pubmed-7345229 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-73452292020-07-09 The Analysis of the Urban Sprawl Measurement System of the Yangtze River Economic Belt, Based on Deep Learning and Neural Network Algorithm Huang, Huafang Wu, Xiaomao Cheng, Xianfu Int J Environ Res Public Health Article In the context of rapid urbanization, the spread of cities in the Yangtze River Economic Belt is intensifying, which has an impact on the green and sustainable development of these cities. It is necessary to establish an accurate urban sprawl measurement system. First, the regulation theory of urban sprawl is explained. According to the actual development situation of cities in the Yangtze River Economic Belt, smart growth theory is selected as the basic regulation method of urban sprawl. Second, the back propagation neural network (BPNN) algorithm under deep supervised learning is applied to construct a smart evaluation model of land use growth. Finally, based on the actual development of cities in the Yangtze River Economic Belt, the quantitative growth measurement method is selected to construct a measurement system of urban sprawl in the Yangtze River Economic Belt, and the empirical analysis is carried out. The training results show that the proposed BPNN smart growth evaluation model, based on deep supervised learning, has good evaluation accuracy, and the error is within the preset range. The analysis of the quantitative growth-based measurement system in the increase of urban construction land shows that the increase in urban construction land area of the Yangtze River Economic Belt from 2014 to 2019 was 78.67 km(2). Meanwhile, the increases in urban construction land area in different years are different. The empirical results show that the population composition of the Yangtze River Economic Belt and the urban construction area between 2005 and 2019 show a trend of increasing annually; at the same time, urban sprawl development shows a staged characteristic. It is of great significance to apply deep learning fusion neural network algorithm in the construction of the urban sprawl measurement system, which provides a quantitative basis for the in-depth analysis and discussion of urban sprawl. MDPI 2020-06-12 2020-06 /pmc/articles/PMC7345229/ /pubmed/32545504 http://dx.doi.org/10.3390/ijerph17124194 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Huang, Huafang Wu, Xiaomao Cheng, Xianfu The Analysis of the Urban Sprawl Measurement System of the Yangtze River Economic Belt, Based on Deep Learning and Neural Network Algorithm |
title | The Analysis of the Urban Sprawl Measurement System of the Yangtze River Economic Belt, Based on Deep Learning and Neural Network Algorithm |
title_full | The Analysis of the Urban Sprawl Measurement System of the Yangtze River Economic Belt, Based on Deep Learning and Neural Network Algorithm |
title_fullStr | The Analysis of the Urban Sprawl Measurement System of the Yangtze River Economic Belt, Based on Deep Learning and Neural Network Algorithm |
title_full_unstemmed | The Analysis of the Urban Sprawl Measurement System of the Yangtze River Economic Belt, Based on Deep Learning and Neural Network Algorithm |
title_short | The Analysis of the Urban Sprawl Measurement System of the Yangtze River Economic Belt, Based on Deep Learning and Neural Network Algorithm |
title_sort | analysis of the urban sprawl measurement system of the yangtze river economic belt, based on deep learning and neural network algorithm |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7345229/ https://www.ncbi.nlm.nih.gov/pubmed/32545504 http://dx.doi.org/10.3390/ijerph17124194 |
work_keys_str_mv | AT huanghuafang theanalysisoftheurbansprawlmeasurementsystemoftheyangtzerivereconomicbeltbasedondeeplearningandneuralnetworkalgorithm AT wuxiaomao theanalysisoftheurbansprawlmeasurementsystemoftheyangtzerivereconomicbeltbasedondeeplearningandneuralnetworkalgorithm AT chengxianfu theanalysisoftheurbansprawlmeasurementsystemoftheyangtzerivereconomicbeltbasedondeeplearningandneuralnetworkalgorithm AT huanghuafang analysisoftheurbansprawlmeasurementsystemoftheyangtzerivereconomicbeltbasedondeeplearningandneuralnetworkalgorithm AT wuxiaomao analysisoftheurbansprawlmeasurementsystemoftheyangtzerivereconomicbeltbasedondeeplearningandneuralnetworkalgorithm AT chengxianfu analysisoftheurbansprawlmeasurementsystemoftheyangtzerivereconomicbeltbasedondeeplearningandneuralnetworkalgorithm |