# An ML-Based Solution in the Transformation towards a Sustainable Smart City
## 2 AI in SSC
### General Picture of AI in SSCs
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Area** & **Subarea** & **Tasks** \\ \hline Urban planning and development & Simulation and modelling & AI helps simulate urban development scenarios, enabling planners to assess the sustainability of different projects and optimise the use of space, resources and energy. \\ \cline{2-3}  & Building smart infrastructure & AI helps in the design and management of smart buildings that optimise energy consumption, increase user comfort and reduce environmental impact. \\ \hline Energy management and efficiency & Smart grids & AI is used to manage smart grids that optimise energy distribution and consumption in real time. This helps to reduce energy waste and integrate renewable energy sources more effectively. \\ \cline{2-3}  & Predictive maintenance & AI algorithms predict when infrastructure, such as power lines or renewable energy equipment, will require maintenance, thus preventing downtime and reducing operational costs. \\ \hline Transport and mobility & Traffic management & Traffic management systems based on artificial intelligence optimise traffic flow, reducing congestion and emissions. These systems can adjust traffic signals in real time based on traffic patterns. \\ \cline{2-3}  & Deployment of autonomous vehicles & Self-driving cars and public transport systems based on artificial intelligence are being tested and deployed to reduce congestion and carbon emissions. \\ \hline Water management & Intelligent water systems & AI monitors water quality and consumption, ensuring efficient distribution and reducing water waste. It also helps detect leaks and manage water resources during droughts. \\ \cline{2-3}  & Flood prediction & AI models are used to predict flooding, enabling cities to take proactive measures to minimise damage and protect citizens. \\ \hline Air quality monitoring & Real-time air quality monitoring & AI systems continuously monitor air quality, providing real-time data to help identify sources of pollution and take immediate action to mitigate them. \\ \cline{2-3}  & Pollution prediction & AI can predict pollution levels based on weather patterns and human activity, enabling cities to implement preventative measures. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Developing status of AI in SSC. Basic requirements for the current SC include
* Careful planning, taking into account the objectives of the SC and its inhabitants;
* Differentiation according to geographical, demographic, economic, social, etc., factors;
* Collection and analysis of data to extract valuable knowledge associated with location and navigation strategies;
* Optimal response of the SC according to social rules (cyberdemocracy);
* Sustainable management of resources;
* Strong and comprehensive ICT platform;
* Cybersecurity;
* Dynamic modernisation at different levels and time horizons of operational improvements;
* The search for new models, solutions and technologies (self-developing SC) [10; 11; 12]. Redundancy (and, indirectly, heterogeneity) of systems is already stardom. SC in its daily work should not depend on the correct functioning of a single platform, but rather on the management systems of a group of such platforms. Therefore, it must be possible to combine smart city platforms with other types of platforms and management systems for such scalable integrated group platforms [6]. Such platforms must be efficient, scalable, flexible and integrable. The challenge is to create smart systems to simultaneously manage smart territories, i.e., urban and rural areas for services targeting users with different needs. Residents do not need to be knowledgeable (or even aware) of the smart systems they use but must be able to rely on the reliability of the technological infrastructure [7; 13].