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.

References in corpus (7)

• Flood Prediction Using Machine Learning Models: Literature Review
• Sustainable Business Models: A Review
• Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model
• Modeling Daily Pan Evaporation in Humid Climates Using Gaussian Process Regression
• Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO${}_2$
• Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content
• Multi-label Classification for Fault Diagnosis of Rotating Electrical Machines