When Smart Cities Get Smarter via Machine Learning: An In-Depth Literature Review

Abstract

The manuscript represents a comeprehensive and systematic literature review on the machine learning methods in the emerging applications of smart city. Application domains include the essential aspect of the smart cities including the energy, healthcare, transportation, security, and pollution. The methodology presents the state-of-the-art, taxonomy, evaluation and model performance. The study concludes that the hybrid models and ensembles are the best performers since they exhibit both high accuracy and not-costly complexity. On the other hand, the deep learning (DL) techniques had higher accuracy than the hybrid models and ensembles, but they demanded relatively higher computation power. Moreover, all these advanced ML methods had a slower processing speed than the single methods. Likewise, the support vector machine (SVM) and decision tree (DT) generally outperformed the artificial neural network (ANN) for accuracy and other metrics. However, since the difference is negligible, it can be concluded that using either of them is appropriate. The study’s findings identify the pros and cons of the methods in each application for future researchers, practitioners, and policy-makers for the right problem within the context of smart cities.