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Simulation of Early Warning Indicators of Urban Expansion Derived from Machine Learning
| 题名： | Simulation of Early Warning Indicators of Urban Expansion Derived from Machine Learning |
| 正文语种： | eng |
| 作者： | Rui Liu;Yuan Xu;Changbing Xue;Zuhua Xia;Gulin Li;Xiaojuan Gou;Shubin Luo |
| 作者单位： | Chengdu Univ. of Technology;Chengdu Univ. of Technology;Chengdu Park Urban Construction Administration;Chengdu Park Urban Construction Administration;Chengdu Univ. of Technology;Chengdu Univ. of Technology;Chengdu Univ. of Technology |
| 关键词： | Urban expansion;Artificial neural network;Cellular automata;Urban expansion warning |
| 摘要： | 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. |
| 出版年： | 2023 |
| 期刊名称： | Journal of Urban Planning and Development |
| 卷： | 149 |
| 期： | 1 |
| 页码： | 04022058.1-04022058.15 |
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