# Leveraging Urban Water Distribution Systems with Smart Sensors for Sustainable Cities
## 5 Experimental Results
### Balerna Scenario
This could be attributed to the city's larger and more complex water network, which introduces greater variability in local demand. Despite the lower prediction accuracy, Lugo has the highest resource optimization potential, optimizing over 76 L per second. Moratalla, with a moderate MAE (7.08), falls between the other two cities regarding prediction accuracy and resource optimization. The relatively lower total optimized resources (7.27 L/s) reflect its smaller water distribution network and population size. Overall, the table underscores the importance of tuning the optimization algorithm to the specific characteristics of each city's water network for maximum effectiveness. In Table 4, a comparative analysis of resource savings demonstrates the varying levels of efficiency achieved across the different cities regarding water resource management. This table highlights how the optimization strategies applied to each city resulted in differing amounts of water savings. Figure 13: Resource savings per node optimized in Balerma. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Lugo** & **Moratalla** & **Balerma** \\ \hline MAE & 46.10 & 7.08 & 0.05 \\ \hline Optimized & & & \\ nodes & 10/28 & 19/27 & 430/445 \\ \hline Total optimized & & & \\ resources & 76.45 & 7.27 & 2.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of Algorithm 1. This table presents the results of the linear regression model used to predict base demand based on pipe diameter in the three cities. The regression coefficients and intercepts for Lugo, Moratalla, and Balerma reveal distinct trends in how base demand scales with pipe diameter in each city's water network. The positive regression coefficient in Lugo (0.1847) suggests that as pipe diameters increase, so does base demand. This is logical for a large urban center like Lugo, where population density and infrastructure requirements necessitate larger pipes to meet higher demand. In contrast, Moratalla's negative coefficient (\(-0.0200\)) indicates that larger pipes are less frequently used or underutilized in some areas, likely due to lower population density or water distribution across a more spread-out network. This negative relationship may also point to inefficiencies in pipe allocation in Moratalla, where pipe size does not match actual demand patterns, signaling a need for infrastructure adjustments to optimize water flow better. This indicates a more dispersed network with varying demand patterns, where larger pipes might be underutilized in certain areas. Balerma's near-zero regression coefficient (0.0007) reflects the stability of base demand across different pipe diameters, characteristic of its smaller, more homogenous network. The relatively small variations in demand make it easier to predict resource needs accurately. The intercept values further highlight the differences between the cities, with Lugo showing the highest intercept (119.06 L/s), representing the base demand in areas with high water usage. In comparison, Balerma's low intercept (5.36 L/s) reflects its smaller scale. This analysis demonstrates that network topology, population distribution, and infrastructure complexity significantly influence the relationship between pipe diameter and base demand, offering insights for more tailored optimization strategies. The data reveal that cities with more optimized nodes, such as Balerma, achieved more significant savings due to their smaller population. In contrast, cities like Lugo and Moratalla experienced a more moderate improvement. This comparative approach allows for a better understanding of how localized factors--such as network size, population density, and infrastructure complexity--affect the effectiveness of resource-saving algorithms. Additionally, it emphasizes the importance of tailoring optimization strategies to the specific characteristics of each water distribution system to maximize efficiency and sustainability.