| Résumé : |
(auteur) As a powerful predictive technique based on machine learning, the maximum entropy (MaxEnt) model has been widely used in geographic modeling. However, its performance in calibrating cellular automata (CA) for urban growth simulation has not been investigated. This study compares the MaxEnt model with logistic regression (LR), artificial neural network (ANN), and support vector machine (SVM) models to explore its advantages in simulating urban growth and interpreting driving mechanisms. With the land use data of 2000 and 2020 from GlobeLand30, the constructed LR-CA, ANN-CA, SVM-CA, and MaxEnt-CA models are applied to simulate the urban growth of Beijing, Tianjin, and Wuhan, respectively. Their performance has been evaluated from multiple aspects such as the accuracy of training, testing, and projecting, computational efficiency, simulation accuracy, and simulated urban landscape. The results indicate that the MaxEnt model is superior to the other models except for the computational efficiency, but the time required for the MaxEnt training and projecting is acceptable and far less than that of the SVM. Taking the LR-CA as the benchmark, the kappa coefficients (Kappa) of the MaxEnt-CA have been increased by 4.20%, 3.38%, and 5.87% in Beijing, Tianjin, and Wuhan, respectively; the increments of corresponding figure of merits (FoM) are 6.26%, 4.58%, and 8.49%. The driving mechanisms of urban growth such as the interactions, response curves, and importance of spatial variables, have also been revealed by the MaxEnt modeling. The driving mechanisms of urban growth in Tianjin are more complex than that in Beijing and Wuhan, because there are more variable interactions; the relationships between spatial factors and urban growth in the three study areas are all nonlinear; the topographic factors and city center of Beijing, the traffic factors and water bodies of Tianjin, and the traffic factors, city center and water bodies of Wuhan are significant factors affecting their urban growth. |
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