A hybrid modeling approach considering spatial heterogeneity and nonlinearity to discover the transition rules of urban cellular automata models
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DOI: 10.1177/23998083221149018
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- Markus Reichstein & Gustau Camps-Valls & Bjorn Stevens & Martin Jung & Joachim Denzler & Nuno Carvalhais & Prabhat, 2019. "Deep learning and process understanding for data-driven Earth system science," Nature, Nature, vol. 566(7743), pages 195-204, February.
- Shijie Li & Chunshan Zhou & Shaojian Wang & Shuang Gao & Zhitao Liu, 2019. "Spatial Heterogeneity in the Determinants of Urban Form: An Analysis of Chinese Cities with a GWR Approach," Sustainability, MDPI, vol. 11(2), pages 1-16, January.
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- Zeng, Haoran & Wang, Haijun & Zhang, Bin & Song, Youcheng & Cao, Xiaoxu & Li, Qiyuan, 2024. "A new cellular automata framework of urban growth modeling by incorporating land use policies and economic development zone planning," Ecological Modelling, Elsevier, vol. 498(C).
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Keywords
cellular automata; geographically weighted regression; artificial neural network; transition rule; urban simulation;All these keywords.
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