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

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

doi:10.1007/978-3-030-36841-8_22

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.

Highlights

IntroductionMajor population of the world will be moving to the cities [2]
machine learning (ML) methods have been contributing to various application domains with promising results in, e.g., mobility management and monitoring, city planning, resource allocation, energy demand and consumption prediction, food supply and production prediction, air pollution monitoring and prediction, etc. [16–21]
To identify the most relevant literature in the realm of using ML and deep learning (DL) methods for smart cities and sustainable urban development we explored the web of science (WoS) and Scopus with the following search keywords: “smart cities” or “sustainable urban development” and all the existing ML and DL methods [12]

Summary

Introduction

Major population of the world will be moving to the cities [2]. This trend will be extremely challenging for the land use management, sustainable urban development, food supply, safety, security, and human well-being in general [3, 4]. Considering the smart cities research, there have been various surveys on the applications of artificial intelligence, ML and DL methods, an insight into the popular methods, classification of the methods, and future trend in the advancement of novel methods are not given yet [47–54]. This paper further contributes to identifying future trends in the advancement of learning algorithms for smart cities. E.g., atmospheric sciences and hydrology were hybrids, and ensemble ML models have increased in popularity, in the smart city domain DL applications are dominant

ML and DL Models for Smart Cities

Artificial Neural Networks in Smart Cities

Support Vector Machines

Tree-Based Models (Decision Trees)

Ensembles, Bayesian, Hybrids, and Neuro-Fuzzy

Discussion and Conclusions