The Analysis of the Urban Sprawl Measurement System of the Yangtze River Economic Belt, Based on Deep Learning and Neural Network Algorithm
Bibliographic Data
| ID | 15501016 |
|---|---|
| Authors | Huafang Huang (0009-0001-5945-3069, Hefei Normal University), Xiaomao Wu (University of Sheffield), Cheng Xian-fu (Anhui Normal University, corresponding author), Xianfu Cheng (Anhui Normal University) |
| Year | 2020 |
| Volume | 17 |
| Issue | 12 |
| Pages | 4194-4194 |
| Publication date | 2020-06-12 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph17124194 |
| PMID | 32545504 |
| OpenAlex | W3035577579 |
| Language | EN |
| Citations received | 3 |
| References cited | 42 |
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
China · Civil engineering · Context (archaeology · Economic growth · Economics · Geography · Sustainable development · Urban planning · Urban sprawl · Urbanization · Yangtze river · Engineering · Land Use and Ecosystem Services · Regional Economic and Spatial Analysis · Regional Socio-Economic Development Trends · Ecology
A Narrative Literature Review
Constructing urban sprawl measurement system of the Yangtze River economic belt zone for healthier lives and social changes in sustainable cities
Intensification of land surface temperature and its seasonal variation in rapidly growing municipalities of Kolkata metropolitan area
| Unique citing works | 3 |
|---|---|
| Citations per year | 0,6 |
| Citation span | 2021 - 2025 (5) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 3 |