Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era

Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era

Ana De Las Heras, Amalia Luque-Sendra, Francisco Zamora-Polo

The unprecedented urban growth of recent years requires improved urban planning and management to make urban spaces more inclusive, safe, resilient and sustainable. Additionally, humanity faces the COVID pandemic, which especially complicates the management of Smart Cities. A possible solution to address these two problems (environmental and health) in Smart Cities may be the use of Machine Learning techniques. One of the objectives of our work is to thoroughly analyze the link between the concepts of Smart Cities, Machine Learning techniques and their applicability. In this work, an exhaustive study of the relationship between Smart Cities and the applicability of Machine Learning (ML) techniques is carried out with the aim of optimizing sustainability. For this, the ML models, analyzed from the point of view of the models, techniques and applications, are studied. The areas and dimensions of sustainability addressed are analyzed, and the Sustainable Development Goals (SDGs) are discussed. The main objective is to propose a model (EARLY) that allows us to tackle these problems in the future. An inclusive perspective on applicability, sustainability scopes and dimensions, SDGs, tools, data types and Machine Learning techniques is provided. Finally, a case study applied to an Andalusian city is presented.

2020

machine learning, sustainability, smart cities, sgds

10.3390/su12229320

Biotemas

Universidade Federal de Santa Catarina

Environmental effects of industries and plants, Renewable energy sources, Environmental sciences