Preprint Review Version 1 This version is not peer-reviewed

State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

Version 1 : Received: 11 August 2019 / Approved: 13 August 2019 / Online: 13 August 2019 (10:00:34 CEST)

How to cite: Nosratabadi, S.; Mosavi, A.; Keivani, R.; Faizollahzadeh ardabili, S.; Aram, F. State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability. Preprints 2019, 2019080154 (doi: 10.20944/preprints201908.0154.v1). Nosratabadi, S.; Mosavi, A.; Keivani, R.; Faizollahzadeh ardabili, S.; Aram, F. State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability. Preprints 2019, 2019080154 (doi: 10.20944/preprints201908.0154.v1).

Abstract

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.

Subject Areas

deep learning; machine learning; smart cities; urban sustainability; cities of future; internet of things (IoT); data science; big data

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