A novel spatio-temporal cellular automata model coupling partitioning with CNN-LSTM to urban land change simulation

Ye Zhou, Chen Huang, Tao Wu and Mingyue Zhang

Ecological Modelling, 2023, vol. 482, issue C

Abstract: Land use change (LUC) has gained attention as a core topic of global ecological environment change research. The cellular automata (CA) model affects the global layout through local changes, and is widely used in LUC. However, most previous studies are based on the assumption of the Markov model which ignores the temporal dependency of LUC. In addition, most researchers have used the identical transition rules when simulating LUC variation across a region, ignoring the spatial heterogeneity in LUC studies.

Keywords: Cellular automata; Land use change; Spatio-temporal dependency; Deep learning; Urbanisation; Spatial partitioning (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:482:y:2023:i:c:s0304380023001254

DOI: 10.1016/j.ecolmodel.2023.110394

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