Conference Paper


State of the art survey of deep learning and machine learning models for smart cities and urban sustainability

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.

Attached files

Authors

Nosratabadi, Saeed
Mosavi, Amir
Keivani, Ramin
Ardabili, Sina
Aram, Farshid

Dates

Year of publication: 2020
Date 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 Archived content may not be published verbatim in whole or in part, whether or not this is done for Commercial Purposes, either in print or online.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.


Related resources

This RADAR resource is the Accepted Manuscript of State of the art survey of deep learning and machine learning models for smart cities and urban sustainability

Details

  • Owner: Joseph Ripp
  • Collection: Outputs
  • Version: 1 (show all)
  • Status: Live
  • Views (since Sept 2022): 351