## 3 Results
### Hydrogeological Risk
Furthermore, although there is no direct relationship between light pollution and the concentration of carbon dioxide, this type of pollution is associated with increased energy consumption, mainly due to the higher energy use caused by unshielded external lighting, which contributes to CO\({}_{2}\) emissions. In parallel, flood risk mitigation requires an integrated approach that involves the use of green infrastructure. The adoption of green roofs, permeable pavements, and the redevelopment of river areas can improve water absorption and reduce surface runoff. The use of early alert systems and predictive models can also support risk management, allowing rapid and effective responses to extreme events. Integrating these aspects into urban planning promotes cities that are more resilient, sustainable, and safe for people. From a performance perspective, the system has shown promising results. CPU and memory usage have remained below optimal thresholds, ensuring efficient resource utilization. These results highlight the computational efficiency and effectiveness of the system, allowing for smooth management of large-scale urban data while maintaining high performance. This system architecture is highly scalable and provides the necessary infrastructure for future enhancements, making it well suited for urban planning, risk management, and environmental monitoring applications. Compared to Barcellona's traffic digital twin [27], Singapore's smart cities initiatives [52], and Snap4 City, our approach focuses on two specific phenomena, aiming to study and analyze their characteristics and evolution over time: light pollution and hydrogeological risk. The key difference in our approach is that users will not need to download any program or install anything on their PC and have access to the software as a service in cloud. With the data available, they will be able to work in cloud on solutions that go beyond viewing the uploaded data, enabling the creation of complex pipelines in which the results of one step's processing become the inputs for subsequent steps. In detail, regarding \begin{table}
\begin{tabular}{c c} \hline
**Land Use Class** & **Risk Area Percentage** \\ \hline
11100 Continuous Urban Fabric & 3.85\% \\
11210 Discontinuous Dense Urban Fabric & 4.78\% \\
11220 Discontinuous Medium Density Urban Fabric & 3.61\% \\
11230 Discontinuous Low Density Urban Fabric & 0.72\% \\
11300 Isolated Structures & 0.21\% \\
12100 Industrial, Commercial, Public, Military, and Private Units & 3.49\% \\
12210 Fast Transit Roads and Associated Land & 0.40\% \\
12220 Other Roads and Associated Land & 9.12\% \\
12230 Railways and Associated Land & 0.20\% \\
12300 Port Areas & 3.42\% \\
13100 Mineral Extraction and Dump Sites & 1.87\% \\
13400 Low without Current Use & 0.59\% \\
14100 Green Urban Areas & 0.68\% \\
14200 Sports and Leisure Facilities & 1.34\% \\
22000 Permanent Crops & 7.94\% \\
23000 Pastures & 39.58\% \\
31000 Forests & 0.50\% \\
32000 Herbaceous Vegetation Associations & 15.76\% \\
33000 Open Spaces with Little or No Vegetation & 1.94\% \\ \hline \end{tabular}
\end{table} 
Table 3: Risk area percentage in land use class. Barcellona's digital twin, this one is based on traffic simulation, aided by SUMO software (version 1.22) [51], and developed on a large scale; meanwhile, our digital twin is based on the Cesium library, and there also no software exception to local server, web browser, and database software. Our approach represents a less-computational resources solution also based on open-source software and libraries; the Singapore digital twin is mainly based on IoT and city sustainability while our project is mainly focused on hydrogeological risk and light pollution. The aim of this work was also to connect the incoming data so that inputs can modify the proposed urban digital twin through relationships designed and studied specifically for this purpose.