The Analysis of the Urban Sprawl Measurement System of the Yangtze River Economic Belt, Based on Deep Learning and Neural Network Algorithm

Abstract

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 km2. 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.

The Analysis of Differential High-Quality Development of Economy by Deep Neural Network and Internet of Things

“…They conducted an empirical analysis based on actual urban development data in the Yangtze River Economic Belt. The results show that the proposed BPNN model of intelligent growth evaluation based on deep supervised learning has good evaluation accuracy, and the error is within the preset range [12].…”

Smart City Economic Management Prediction Model Based on Information Analysis System

“…In the construction process of the platform, almost all rely on the power of this government department. During the period, only the ird Party C New Industry Investment Consulting Co., Ltd., was entrusted to carry out relevant work in the process of system construction [20]. is fully reflects that the construction of smart economic service platform in city C does not give full play to the role of the government and the market, especially the decisive role of the market in allocating resources, and does not fully mobilize the initiative, enthusiasm, and creativity of enterprises, the public, and other subjects to participate in the construction of smart city.…”

A deep learning neural network approach for predicting the factors influencing heavy-metal adsorption by clay minerals

“…Furthermore, the artificial neural networks simulate the learning procedure of the human brain through analysis (e.g., Matt et al, 2015;Luke et al, 2017;Hu et al, 2017;Chaudhary et al, 2018;Castro et al, 2020;Liu et al, 2020;Gyanendra et al, 2020). A neural network system based on deep learning can be used to accurately predict the adsorption rate, which has a wide range of input parameters, through simulation fitting training of the effective number of iterations (Huang et al, 2020). In addition, Pearson sensitivity analysis can be used to determine the contribution of individual input parameters to the adsorption rates predicted by the neural network (Hasan et al, 2017).…”

“…Castro et al , 2015; Chaudhary et al , 2017; Hu et al , 2017; Liu et al , 2020). A neural network system based on deep learning can be used to accurately predict adsorption rates, which have a wide range of input parameters, through simulation fitting training with large amounts of useful data (Huang et al , 2020). In addition, Pearson sensitivity analysis can be used to determine the contribution of individual input parameters to the adsorption rates predicted by the neural network.…”