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.
Nosratabadi, SaeedMosavi, AmirKeivani, RaminArdabili, SinaAram, Farshid
Year of publication: 2020Date of RADAR deposit: 2020-01-22
© Springer Nature Switzerland AG 2020. Archived content may not be published verbatim in whole or in part, whether or not this is done for Commercial Purposes, either in print or online. This restriction does not apply to reproducing normal quotations with an appropriate citation.