# Leveraging Urban Water Distribution Systems with Smart Sensors for Sustainable Cities
## 5 Experimental Results
### Summary of Results  
The results obtained in this study were analyzed for water distribution networks in Lugo, Moratalla, and Balerma using EPANET simulations and linear regression techniques. The results show a substantial difference in the MAE between Lugo (46.10) and Balerma (0.05), which can be attributed to Lugo's larger and more complex network. Additionally, Balerma's high number of optimized nodes (430 out of 445) reflects its network's ability to achieve significant resource efficiency despite its smaller population. These findings suggest that optimization efforts should be concentrated in areas with high node density for maximum efficiency gains. We highlight the following points:  
1. Demand Prediction Accuracy: The study highlighted the variability in demand prediction accuracy across different scenarios. Balerma achieved the highest accuracy with an MAE of 0.049, next was Moratalla with an MAE of 7.08, and finally, Lugo had the lowest accuracy (MAE of 46.10). 2. Resource Optimization: Resource optimization varied significantly, with Balerma optimizing 430 out of 445 nodes, Moratalla 19 out of 27 nodes, and Lugo 10 out of 28 nodes. The highest resource savings per node were observed in Lugo and Balerma. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Lugo** & **Moratalla** & **Balerma** \\ \hline Regression & & & \\ coefficient & 0.1847 & \(-0.0200\) & 0.0007 \\ \hline Intersection & 119.06 & 18.60 & 5.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of Algorithm 2. Relationship Between Base Demand and Pipe Diameter: The linear regression analyses showed distinct trends. Lugo displayed a positive correlation between base demand and diameter, while Moratalla showed a negative correlation. Balerma's base demand stabilized across different diameters, indicating a minimal diameter impact on base demand. While the simulation results have highlighted the significant potential for optimization in urban water networks, the next logical step is to translate these findings into a practical solution. To ensure real-world applicability, we propose a scalable, technology-driven architecture that builds on these insights, enabling optimized water distribution systems deployment across diverse urban environments. This architecture leverages sensors, wireless communication, and cloud-based data processing to provide real-time monitoring, anomaly detection, and predictive modeling, ensuring effective and sustainable water management. It features sensors in nodes, LoRaWAN, and satellites for global wireless communication and cloud-based data processing for real-time monitoring, anomaly detection, and predictive modeling. While the proposed architecture offers a promising blueprint for optimizing water distribution systems, practical challenges must be addressed to ensure real-world applicability. These include the integration costs of deploying IoT sensors across vast urban networks, ensuring the accuracy and reliability of real-time sensor data, and managing large-scale data processing requirements. Additionally, securing reliable wireless communication (e.g., LoRaWAN) in urban areas with potential signal interference or remote areas with less coverage could pose challenges. Overcoming these obstacles will require collaborations with local water authorities, pilot projects in urban centers, and further refinement of the system architecture based on real-world feedback, as seen in Figure 14.  
The sensor block, comprising water level and water flow sensors, will be strategically installed at reservoir nodes to obtain real-time measurements. Water flow sensors will detect anomalies like leaks, triggering alarms to alert users. Utilizing LoRaWAN wireless technology, sensor readings will be transmitted to a satellite or data-processing center. A LoRaWAN gateway will facilitate communication between the sensors and a constellation of LEO satellites, enhancing the global availability of water level data for expedited decision-making in resource optimization.