scholarly journals Analysis and Prediction of Expansion of Central Cities Based on Nighttime Light Data in Hunan Province, China

2021 ◽  
Vol 13 (21) ◽  
pp. 11982
Author(s):  
Yuxin Liu ◽  
Tian He ◽  
Yi Wang ◽  
Changhui Peng ◽  
Hui Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Urban Population ◽  
Urban Expansion ◽  
Hunan Province ◽  
Sustainable Urban Development ◽  
Central Cities ◽  
Urban Road ◽  
Industry Output ◽  
Expansion Speed

Quantifying the characteristics of urban expansion as well as influencing factors is essential for the simulation and prediction of urban expansion. In this study, we extracted the built-up regions of 14 central cities in the Hunan province using the DMSP-OLS night light remote sensing datasets from 1992 to 2018, and evaluated the spatial and temporal characteristics of the built-up regions in terms of the area, expansion speed, and main expansion direction. The backpropagation (BP) neural network and autoregressive integrated moving average (ARIMA) model were used to predict the area of the built-up regions from 2019 to 2026. The model predictions were based on the GDP, ratio of the secondary industry output to the GDP, ratio of the tertiary industry output to the GDP, year-end urban population, and urban road area. The results demonstrated that the built-up area and expansion speed of the central cities in the eastern part of the Hunan province were significantly higher than those in the western part. The main expansion directions of the 14 central cities were east and south. The urban road area, year-end urban population, and GDP were the main driving factors of the expansion. The urban expansion model based on the BP neural network provided a high prediction accuracy (R = 0.966). It was estimated that the total area of urban built-up regions in the Hunan province will reach 2463.80 km2 by 2026. These findings provide a new perspective for predicting urban areas rapidly and simply, and it also provides a useful reference for studying the spatial expansion characteristics of central cities and formulating a sustainable urban development strategy during the 14th Five-Year Plan of China.

scholarly journals Travel time prediction of urban road based on BP neural network

2020 ◽  
Vol 1651 ◽  
pp. 012190
Author(s):  
Fangyi Deng ◽  
Pei Su ◽  
Bingxue Luo ◽  
Peng Wu ◽  
Yan Guo
Keyword(s):  
Neural Network ◽  
Travel Time ◽  
Bp Neural Network ◽  
Travel Time Prediction ◽  
Urban Road ◽  
Time Prediction

scholarly journals Simulation of Dynamic Urban Expansion under Ecological Constraints Using a Long Short Term Memory Network Model and Cellular Automata

2021 ◽  
Vol 13 (8) ◽  
pp. 1499
Author(s):  
Jiamin Liu ◽  
Bin Xiao ◽  
Yueshi Li ◽  
Xiaoyun Wang ◽  
Qiang Bie ◽  
...  
Keyword(s):  
Neural Network ◽  
Cellular Automata ◽  
Short Term Memory ◽  
Urban Expansion ◽  
Sustainable Urban Development ◽  
Effective Control ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Rapid urban expansion has seriously threatened ecological security and the natural environment on a global scale, thus, the simulation of dynamic urban expansion is a hot topic in current research. Existing urban expansion simulation models focus on the mining of spatial neighborhood features among driving factors, however, they ignore the over-fitting, gradient explosion, and vanishing problems caused by the long-term dependence of time series data, which results in limited model accuracy. In this study, we proposed a new dynamic urban expansion simulation model. Considering the long-time dependence issue, long short term memory (LSTM) was employed to automatically extract the transformation rules through memory units and provide the optimal attribute features for cellular automata (CA). This study selected Lanzhou, which is a semi-arid region in Northwest China, as an example to confirm the validity of the model performance using data from 2000 to 2020. The results revealed that the overall accuracy of the model was 91.01%, which was higher than that of the traditional artificial neural network (ANN)-CA and recurrent neural network (RNN)-CA models. The LSTM-CA framework resolved existing problems with the traditional algorithm, while it significantly reduced complexity and improved simulation accuracy. In addition, we predicted urban expansion to 2030 based on natural expansion (NE) and ecological constraint (EC) scenarios, and found that EC was an effective control strategy. This study provides a certain theoretical basis and reference value toward the realization of new urbanization and ecologically sound civil construction, in the context of territorial spatial planning and healthy/sustainable urban development.

scholarly journals Efficiency Evaluation of Urban Road Transport and Land Use in Hunan Province of China Based on Hybrid Data Envelopment Analysis (DEA) Models

2019 ◽  
Vol 11 (14) ◽  
pp. 3826 ◽  
Author(s):  
Yang ◽  
Guan ◽  
Qian ◽  
Xing ◽  
Wu
Keyword(s):  
Land Use ◽  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
National Development ◽  
Road Transport ◽  
Central China ◽  
Hunan Province ◽  
Sustainable Urban Development ◽  
Data Envelopment ◽  
Urban Road

Urban road transport and land use (RTLU) jointly promote economic development by concentrating labor, material, and capital. This paper presents an integrated RTLU efficiency analysis that explores the degree of coordination between these two systems to provide guidance for future adaptations necessary for sustainable urban development. Both a super efficiency Data Envelopment Analysis model and window analysis were used to spatiotemporally evaluate RTLU efficiency from 2012 to 2016 in 14 cities of Hunan province, central China. The Malmquist index was decomposed into technical efficiency and technology change to reveal reasons for changes in RTLU efficiency. These evaluation results show regional disparities in efficiency across Hunan province, with western cities being the least efficient. Eight cities showed an increasing trend in RTLU efficiency while Yueyang exhibited a decreasing trend. In 13 of 14 regions, productivity improved every year. At the same time, five regions had a decline in technical efficiency even though technical progress increased in all regions. Our analysis shows that greater investment in road transport and urban construction are not enough to ensure sustainable urban growth. Policy must instead promote the full use of current resources according to local conditions to meet local, regional, and national development goals.

A Prediction of Vehicle Possession in Hunan Province Based on Principal Component and BP Neural Network

2011 ◽  
Vol 403-408 ◽  
pp. 1337-1341 ◽  
Author(s):  
Yin Li ◽  
Xin Shao Zhou ◽  
Chao Kui ◽  
Ya Ping Tian
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Fractal Theory ◽  
Urban Transportation ◽  
Principal Component ◽  
Direct Reference ◽  
Urban Traffic ◽  
Hunan Province ◽  
Car Ownership ◽  
The Impact

Prediction of car ownership has direct reference significance for the development of urban transportation and construction of urban roads. By analyzing the impact factors of urban auto possession, this paper first analyzes 8 indicators such as urban population, GDP, road passenger traffic and so on determined by some references, then establish BP neural network model to predict the vehicles possession in Hunan Province from 2006 to 2008. The figures of prediction is 989,300, 1,221,800 and 1,370,300 respectively in 2006, 2007 and 2008, which is very close to the real ownership of 946,400,1,217,200 and 1,426,700 respectively. It shows the prediction is very accurate. This suggests that the BP neural network has very strong learning and generalization ability and can be employed in prediction of vehicle possession effectively. The prediction of car ownership, as a foundational work for transportation planning,has direct reference significance on the development of urban traffic,its control and management and construction of urban road, etc.Early in 1940s this research has been started in foreign countries[1]. Many different models of prediction of car ownership have been developed.Many of them are developed mainly based on the factors such as urban economy, population network capacity, the land utilization and parking facilities.In China there are also some researches on this issue. They predicate the car ownership mainly by time series prediction, regression analysis and fractal theory and entropy method [2~6].However, these methods do not comprehensively describe the complex relationship between car ownership and other factors. The author of this paper chooses some car ownership-related factors and employ principal component method to analyze to obtain the main factors, then tries to find the relationship between BP neural networks and car ownership according to these factors so as to predict the car ownership in Hunan Province form 2006 to 2008, which will be greatly significant to the development of urban transportation, management and construction.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

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