Artificial Intelligence for Smart Decision-Making in the Cities of the Future

Abstract

Global urbanization is growing at a rapid pace and in the near future most of the world’s population will move to cities. This trend will be extremely challenging for: land use management, sustainable urban development, food supply, security and general human well-being. Thus, for several years, emerging technologies and new concepts of smart cities have been proposed to ensure optimal management of the cities of the future, namely artificial intelligence applications such as: The Internet of Things (IoT), the Machine Learning (ML) and the Deep Learning (DL). In this context, we propose in this paper a methodology that is based on the use of the Formal Concept Analyzes (FCA) method on some qualitative data points obtained from the City Carbon Disclosure Project (CDP) database to generate some Key Performance Indicators (KPIs) that can help and guide decision makers to reduce CO$_{2}$ emissions and build as well efficient sustainable development strategies, especially for the cities of the future. We have focused our experimentation on 9 American cities. Our experimental results show that New York is the city that emits the most CO$_{2}$ and that the emission sources are: transportation, urban land Use and Energy Demand in Buildings.