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. Preprints2019, 2019080154. https://doi.org/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. https://doi.org/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. Preprints2019, 2019080154. https://doi.org/10.20944/preprints201908.0154.v1
APA Style
Nosratabadi, S., Mosavi, A., Keivani, R., Faizollahzadeh ardabili, S., & Aram, F. (2019). State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability. Preprints. https://doi.org/10.20944/preprints201908.0154.v1
Chicago/Turabian Style
Nosratabadi, S., Sina Faizollahzadeh ardabili and Farshid Aram. 2019 "State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability" Preprints. https://doi.org/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.
Keywords
deep learning; machine learning; smart cities; urban sustainability; cities of future; internet of things (IoT); data science; big data
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
