Integrating artificial neural network-cellular automata and bayesian weight of evidence for spatiotemporal analysis of urban change in Brisbane City

Abstract Urbanisation is a global phenomenon, with major cities worldwide undergoing rapid transformation driven by economic and population growth. However, the urbanisation process in Brisbane City remains comparatively underexplored relative to other Australian metropolitan areas. This study presents a novel integrated framework that combines Google Earth Engine (GEE), Bayesian weight of evidence (WofE), and Artificial Neural Network–Cellular Automata (ANN–CA) to analyse and forecast spatiotemporal patterns of urban change. The research addresses three objectives: (i) monitoring urban settlements in Brisbane City from 1990 to 2021, (ii) identifying the drivers of urban expansion between 1990 and 2021, (iii) projecting future urban growth to 2030, and (iv) critically assessing the capability, strengths, and weaknesses of the ANN–CA model. Random forest classification using GEE achieved overall, producer, and user accuracies ranging from 0.97 to 1.00. The results revealed a period of stagnation and slight decline during 1990–2000, followed by accelerated expansion from 2000–2021, during which the urban area nearly doubled from 142.29 km 2 to 307.41 km 2 . WofE analysis highlighted key determinants of urban growth, including distance to the central business district, proximity to waterways, roads, and points of interest, as well as topographic variables. ANN–CA simulations underscored the model’s sensitivity to underfitting, overfitting, and imbalanced change rates. Adaptive-period recalibration improved performance, with validation on 2021 dataset yielding 93.69% overall accuracy and Kappa coefficient of 0.67. Based on this corrective method, the model projects Brisbane’s urban extent to reach 380.45 km 2 by 2030. This integrated approach offers methodological advances and new insights into the mechanisms and trajectories of urban expansion.