Simulation of Early Warning Indicators of Urban Expansion Derived from Machine Learning
Publication: Journal of Urban Planning and Development
Volume 149, Issue 1
Abstract
Rapid urbanization has brought along with it many environmental and social problems such as ecosystem damage and traffic congestion. Therefore, forecasting the trend of urban expansion and providing a reasonable urban planning basis for government departments have become the focus of researchers. An artificial neural network (ANN) can be used to consider spatial nonstationarity when obtaining the changing characteristics of urban land types. Therefore, in this study, we use cellular automata (CA) based on ANN (ANN-CA) to simulate and forecast urban expansion and discuss the parameter sensitivity of the model in detail. In addition, we propose a new Urban Expansion Early Warning Indicator system to warn about the deterioration of future land distribution patterns. Chengdu is selected as the study area, and the study period is from 2000 to 2020. The results showed the following: (1) The best accuracy was achieved when the neighborhood size is 7 × 7 and the number of model iterations is 250, and overall accuracy (OA), Kappa coefficient, and figure of merit (FOM) are 91.47%, 0.855, and 0.354, respectively; (2) ANN-CA is more suitable for predicting the urban expansion of Chengdu than CA based on logistic regression (LR-CA) and CA based on decision tree (DT-CA). Compared with the worst performance model, the score of OA increased by 6.23%, that of kappa increased by 0.062, and that of FOM increased by 0.056. (3) According to the current development trend, artificial built-up areas will increase substantially. The comprehensive evaluation results of the morphology effect, ecological effect, and intensity effect of urban expansion predict severe early warning for Jinniu District, Qingyang District, and Wuhou District by 2030.
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Acknowledgments
The authors like to express their appreciation to the staff in our lab for their valuable comments and other forms of assistance. We sincerely thank Dr. Brittany Turner for assisting with manuscript editing and all anonymous reviewers for their constructive comments and suggestions. This research was funded by the “Research on Landscape Resource Conservation Planning in National Park Based on Ecological Sensitivity: A Case Study of the Giant Panda National Park” supported by National Park Research Center, Key Research Base of Philosophy and Social Sciences in Sichuan Province (No. GJGY2022-ZD003) and the “Application of modern technology in national park management” supported by the National Park Research Center, Key Research Base of Social Sciences in Sichuan Province (No. GJGY2019-YB002).
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Journal of Urban Planning and Development
Volume 149 • Issue 1 • March 2023
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© 2022 American Society of Civil Engineers.
History
Received: Mar 23, 2022
Accepted: Oct 20, 2022
Published online: Dec 13, 2022
Published in print: Mar 1, 2023
Discussion open until: May 13, 2023
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Automation and robotics
- Computer programming
- Computing in civil engineering
- Ecosystems
- Engineering fundamentals
- Environmental engineering
- Forecasting
- Infrastructure
- Mathematics
- Model accuracy
- Models (by type)
- Neural networks
- Statistics
- Systems engineering
- Traffic congestion
- Traffic engineering
- Traffic management
- Traffic models
- Transportation engineering
- Urban and regional development
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