Journal of Geographical Sciences ›› 2020, Vol. 30 ›› Issue (2): 333-352.doi: 10.1007/s11442-020-1731-x
• Review Article • Previous Articles
CUI Xuegang1,2, FANG Chuanglin1,2,*(), LIU Haimeng1,2, LIU Xiaofei1,2, LI Yonghong3
Received:
2019-08-29
Accepted:
2019-10-18
Online:
2020-02-25
Published:
2020-04-21
Contact:
FANG Chuanglin
E-mail:fangcl@igsnrr.ac.cn
About author:
Cui Xuegang (1990-), PhD, specialized in urban geography and regional planning. E-mail: cuixg.16b@igsnrr.ac.cn
Supported by:
CUI Xuegang, FANG Chuanglin, LIU Haimeng, LIU Xiaofei, LI Yonghong. Dynamic simulation of urbanization and eco-environment coupling: Current knowledge and future prospects[J].Journal of Geographical Sciences, 2020, 30(2): 333-352.
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Table 1
Comparison of techniques for dynamic simulation of urbanization and eco-environment coupling."
Name | Discipline | Advantages | Disadvantages | Application |
---|---|---|---|---|
System dynamics | Systems science and computer simulation | Modeling process is simple and can be combined with an index system to identify system boundary and related variables | Difficult to reflect the characteristics of adaptive and spatial change in the system, and the feedbacks are in part regression relationships | Urban system change, urban sustainable development and urbanization and eco-environment element coupling |
Artificial neural network | Artificial intelligence | A typical human brain model with three advantages: self-learning, associative storage and high-speed optimization | Defective in learning, causal explanation and other aspects, especially in dealing with system uncertainty | Urban land expansion, environmental change, and resources demand |
Bayesian networks | Artificial intelligence, probability theory, statistics and graph theory | Good at causal and diagnostic reasoning, as empirical data can be incomplete | Difficult to deal with the large number of nodes and the learning ability is less than for ANN | Identification of urban ecological vulnerability and demand for resources |
CLUE-s | LUCC, systems science and computer simulation | Good at dealing with different spatial scales based on empirical data | Focus on local equilibrium analysis | Land use allocation on multiple spatial scales |
Cellular automata | LUCC, systems science and computer simulation | Simplifies complex problems by bottom-up modeling and can simulate complex discrete systems | Difficult to solve the problem of spatial heterogeneity and lacks explanation of the mechanism | Urban sprawl and land use change |
Multi-agent system | Artificial intelligence and complexity science | Compensates for the neglect of policy factors and explains land use change processes | Research space is abstracted as homogeneous and model validation is difficult | Policy-driven urban sprawl and land use change |
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