Journal of Geographical Sciences, Volume. 30, Issue 2, 333(2020)
Dynamic simulation of urbanization and eco-environment coupling: Current knowledge and future prospects
Figures & Tables(2)
Fig. 1. Interactions between urbanization, resources and environment in URE
Table 1.
Comparison of techniques for dynamic simulation of urbanization and eco-environment coupling.
View tableView in ArticleTable 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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Xuegang CUI, Chuanglin FANG, Haimeng LIU, Xiaofei LIU, Yonghong LI. Dynamic simulation of urbanization and eco-environment coupling: Current knowledge and future prospects[J]. Journal of Geographical Sciences, 2020, 30(2): 333
Paper Information
Received: Aug. 29, 2019
Accepted: Oct. 18, 2019
Published Online: Sep. 29, 2020
The Author Email: FANG Chuanglin (fangcl@igsnrr.ac.cn)
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