  Deep learning (DL) and machine learning (ML) methods have recently
contributed to the advancement of models in the various aspects of prediction,
planning, and uncertainty analysis of smart cities and urban development. This
paper presents the state of the art of DL and ML methods used in this realm.
Through a novel taxonomy, the advances in model development and new application
domains in urban sustainability and smart cities are presented. Findings reveal
that five DL and ML methods have been most applied to address the different
aspects of smart cities. These are artificial neural networks; support vector
machines; decision trees; ensembles, Bayesians, hybrids, and neuro-fuzzy; and
deep learning. It is also disclosed that energy, health, and urban transport
are the main domains of smart cities that DL and ML methods contributed in to
address their problems.
