Sustainable urban planning and management require reliable land change models,
which can be used to improve decision making. The objective of this study was to test a
random forest-cellular automata (RF-CA) model, which combines random forest (RF) and
cellular automata (CA) models. The Kappa simulation (KSimulation), figure of merit, and
components of agreement and disagreement statistics were used to validate the RF-CA
model. Furthermore, the RF-CA model was compared with support vector machine cellular
automata (SVM-CA) and logistic regression cellular automata (LR-CA) models. Results
show that the RF-CA model outperformed the SVM-CA and LR-CA models. The RF-CA
model had a Kappa simulation (KSimulation) accuracy of 0.51 (with a figure of merit
statistic of 47%), while SVM-CA and LR-CA models had a KSimulation accuracy of 0.39
and -0.22 (with figure of merit statistics of 39% and 6%), respectively. Generally, the
RF-CA model was relatively accurate at allocating “non-built-up to built-up” changes as
reflected by the correct “non-built-up to built-up” components of agreement of 15%. The
performance of the RF-CA model was attributed to the relatively accurate RF transition
potential maps. Therefore, this study highlights the potential of the RF-CA model for
simulating urban growth.