Balancing simulation performance and computational intensity of CA models for large-scale land-use change simulations

Abstract

Large-scale land-use change simulations are crucial for understanding land dynamics, investigating climate change, and shaping policy regulations. However, conducting fine-resolution land-use change simulations on a large scale is challenging due to high computational demands. Conversely, land-use change simulations with coarse-resolution data distort spatial details, thereby reducing simulation performance. Parallel computing can reduce computational demands but requires significant computational resources. Mixed-cell CA models offer a solution to balance simulation performance and computational intensity. The comparison experiments using various resolution land use datasets demonstrate that mixed-cell CA models, even those with coarse-resolution data, achieve results comparable to those of pure-cell CA models using fine-resolution data, but with significantly reduced simulation time. This highlights the efficiency of mixed-cell CA models in achieving comparable performance with lower computational intensity. Additionally, this study provides a measurement method for the uncertainty of mixed-cell CA models. In summary, this study reveals the unique advantages of mixed-cell CA models in efficient large-scale land use simulations, thereby providing valuable insights and guidance for future land use management and policy decisions.