Land-use change simulation for large-scale regions (i.e. provincial regions or countries) is very useful for many global studies. Such simulation, however, is affected by computational capability of general computers. This paper proposes a method to implement cellular automata (CA) for land use change simulation based on graphics processing units (GPUs). This method can be applied to large-scale land-use change simulations by combining the latest GPU high-performance computing technology and CA. We carried out the experiments by simulating land-use change processes at a provincial scale. This involves a lot of sophisticated techniques, such as model mapping, and computational procedure of GPU-CA model. This proposed model has been validated by land-use change simulation in Guangdong Province, China. The comparison indicates that the GPU-CA model is faster than traditional CA by 30 times. Such improvement is crucial for land-use change simulations in provincial regions and countries. The outputs of the simulation can be further used to provide information to other global change models. graphics processing unit (GPU), cellular automata (CA), land-use change simulation, large-scale, global change
There are many different methods to calibrate cellular automata (CA) models for better simulation results of urban land-use changes. However, few studies have been reported on combination of parameter update and error control using local data in CA calibration procedures. This paper presents a self-modifying CA model (SM-CA) that uses the dual ensemble Kalman filter (dual EnKF), which enables the CA model to simultaneously update model parameters and simulation results by merging observation data (local data). We applied the proposed model to simulate urban land-use changes in a 13-year period (1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005) in Dongguan City, a rapidly urbanizing region in south China. Simulation results indicate that this model yields better simulation results than the conventional logistic-regression CA and decision-tree CA models. For example, the validation is carried out using cross-tabulation matrix. The simulation results of SM-CA have allocation disagreement of 10. 18%, 19.64%, and 30.03% in 1997, 2001, and 2005, respectively, which are 2.12%, 2.47%, and 6% lower than conventional logistic-regression CA models.
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