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

Nosratabadi, Saeed and Mosavi, Amir and Keivani, Ramin and Ardabili, Sina and Aram, Farshid (2020). State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability. In: "International Conference on Global Research and Education : INTER-ACADEMIA 2019", 4-7 September 2019, Balatonfüred, Hungary. pp. 228-238. https://doi.org/10.1007/978-3-030-36841-8_22.

Description

Title: State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability
Author/s:
  • Nosratabadi, Saeed
  • Mosavi, Amir
  • Keivani, Ramin
  • Ardabili, Sina
  • Aram, Farshid
Item Type: Presentation at Congress or Conference (Article)
Event Title: International Conference on Global Research and Education : INTER-ACADEMIA 2019
Event Dates: 4-7 September 2019
Event Location: Balatonfüred, Hungary
Title of Book: Engineering for Sustainable Future
Date: January 2020
ISSN: 2367-3370
Volume: 101
Subjects:
Faculty: E.T.S. Arquitectura (UPM)
Department: Urbanística y Ordenación del Territorio
Creative Commons Licenses: None

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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.

More information

Item ID: 62467
DC Identifier: http://oa.upm.es/62467/
OAI Identifier: oai:oa.upm.es:62467
DOI: 10.1007/978-3-030-36841-8_22
Official URL: https://doi.org/10.1007/978-3-030-36841-8_22
Deposited by: Farshid Aram
Deposited on: 15 Apr 2020 08:12
Last Modified: 15 Apr 2020 08:12
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